We recently added outputSchema support for our MCP tools (not yet for
worker routes yet). Today, we always return structuredContent. On tool
execution errors we return structuredContent: {"error": "..."} with
isError: True, even when that shape does not match the tool’s declared
outputSchema. Since the MCP spec says clients SHOULD validate
structuredContent against outputSchema, some clients reject these
responses.
Since structuredContent is optional, we’re going to omit it when
isError: true.
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> **Medium Risk**
> Changes the shape of tool error responses across the MCP server, which
may break clients or tools that previously relied on
`structuredContent["error"]` for failures. Behavior is more
spec-compliant but touches core request/response paths and test
expectations.
>
> **Overview**
> Prevents MCP tool error responses from violating a tool’s declared
`outputSchema` by **always setting `structuredContent=None` when
`isError=True`** (server execution errors, unknown tools, middleware
exceptions, and `Context.tools.call_raw` JSON-RPC errors).
>
> Updates requirement-failure error formatting to put the human-friendly
message in `content[0]` and (when present) serialize extra
machine-readable fields (e.g. `authorization_url`, `llm_instructions`)
into an additional `content` item. Examples and integration/unit tests
are updated to read errors from `content[0].text`, and
`arcade-mcp-server` is bumped to `1.19.2`.
>
> <sup>Reviewed by [Cursor Bugbot](https://cursor.com/bugbot) for commit
4213bdd4aa44362de85c30f5f31c576243c132d5. Bugbot is set up for automated
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<!-- /CURSOR_SUMMARY -->
When a tool’s output TypedDict uses total=False, MCP clients reject the
response with:
```
MCP error -32602: Structured content does not match the tool's output schema
```
Note that the bug also exists for the Engine transport
(/worker/tools/execute), but since the engine doesn't validate the
output schema, the bug never surfaced. This PR addresses the problem
holistically (MCP and Engine) in preparation for a future where the
Engine transport validates output schemas.
Two bugs combined to cause this:
1. Schema: The outputSchema had no required array and declared all
fields as strict types (e.g. "type": "string"), making every field look
mandatory and non-null.
2. Serialization: model_dump() on TypedDict-derived Pydantic models
emitted None for absent optional fields. A tool returning {"name":
"hello"} produced {"name": "hello", "optional_field": null} which is a
value the schema forbids.
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> **Medium Risk**
> Adjusts core schema generation and MCP JSON Schema conversion for
TypedDicts, affecting how tool input/output contracts are emitted and
validated across clients; mistakes could break compatibility or
validation behavior.
>
> **Overview**
> Fixes MCP/engine validation failures for `TypedDict(total=False)`
outputs by ensuring absent optional keys are **omitted from serialized
output** and that emitted schemas correctly describe **required vs
optional** keys.
>
> `arcade-core` now tracks `required_keys`/`inner_required_keys` and
per-field `nullable` in `ValueSchema`, derives required sets from
TypedDict `__required_keys__`, and unwraps `Optional[T]` to support
optional nested TypedDicts; TypedDict-derived Pydantic models now
`model_dump(exclude_unset=True)` to avoid leaking missing fields as
`null`.
>
> `arcade-mcp-server` JSON Schema conversion now emits `required` arrays
(including for arrays of objects), supports `nullable` by generating
`type: [<type>, "null"]` (and `enum` including `None`), and treats
nullable top-level objects as valid unwrapped output schemas. Adds
focused unit/end-to-end tests plus an expanded example server
demonstrating total-false, mixed required/optional, nullable, and
optional-nested TypedDict outputs, and bumps package
versions/dependencies accordingly.
>
> <sup>Reviewed by [Cursor Bugbot](https://cursor.com/bugbot) for commit
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<!-- /CURSOR_SUMMARY -->
Resolves
https://linear.app/arcadedev/issue/TOO-590/add-resources-support-to-server-framework
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> **Medium Risk**
> Adds new resource registration/reading semantics (including URI
templates and duplicate/multiple-match policies) and changes JSON Schema
generation for tool I/O, which may affect MCP client compatibility and
runtime behavior across servers.
>
> **Overview**
> **Adds first-class MCP Resources support across `arcade-mcp-server`.**
`MCPApp` can now register resources at build time via
`add_resource`/`@resource` plus convenience `add_text_resource` and
`add_file_resource`, and passes these through to `MCPServer` for startup
loading (including `ResourceTemplate` URIs with `{param}` and `{param*}`
matching).
>
> **Extends `ResourceManager` behavior.** Resource reads now coerce
handler return types (including raw `bytes` to base64
`BlobResourceContents`), support template matching with
overlap/multiple-match detection, and introduce configurable duplicate
handling policies.
>
> **Improves tool schema + MCP Apps linking.** Tool input/output JSON
Schema generation is refactored to recursively expand nested `json`
schemas and ensure `outputSchema` is always an object (wrapping
non-object returns in a `result` property); `MCPApp` also supports
attaching arbitrary tool `_meta` extensions (e.g., `ui.resourceUri`)
applied at server start.
>
> Adds two new example servers (`resources`, `tools_with_output_schema`)
and broad test coverage for resource templates, static/file resources,
meta extensions, and schema wrapping/recursion.
>
> <sup>Written by [Cursor
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<!-- /CURSOR_SUMMARY -->
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
**Implements**: [SEP-2448: server execution telemetry]
(https://github.com/modelcontextprotocol/modelcontextprotocol/pull/2448)
**Description:**
**The Observability Gap (The Problem)**
MCP clients propagate trace context to servers, but server-side
execution remains a black box. The client sees a single tools/call or
resources/read span; everything the server does (auth checks, policy
evaluation, API calls, sub-tool invocations) is invisible. In
cross-organization deployments, clients and servers use separate
observability backends with no shared collector access, making
traditional span export useless.
<img width="1015" height="450" alt="Screenshot 2026-03-23 at 3 43 21 PM"
src="https://github.com/user-attachments/assets/58c817b5-fee6-46a3-9877-d523a25368ad"
/>
**Server Execution Telemetry (The Solution)**
Servers advertise serverExecutionTelemetry and return a curated slice of
their execution spans directly in _meta.otel of the response. Clients
ingest these verbatim OTLP spans into their own collector, stitching
server-side execution into their distributed trace; no shared
infrastructure required. The black box becomes transparent.
<img width="945" height="574" alt="Screenshot 2026-03-23 at 3 43 44 PM"
src="https://github.com/user-attachments/assets/38d97c94-aa73-4e62-9b4e-3264600e5ed0"
/>
.
**Summary:**
Implement MCP serverExecutionTelemetry capability that enables
cross-organization distributed tracing by returning server-side
OpenTelemetry spans to clients inline via _meta.otel.traces.
Server-side (middleware):
- TelemetryPassbackMiddleware intercepts tools/call and resources/read
- ContextVarSpanCollector isolates span collection per-request via
ContextVar
- Propagates traceparent from client request for distributed trace
stitching
- Serializes collected spans to verbatim OTLP JSON (resourceSpans
format), directly POSTable to /v1/traces
- Top-level span filtering by default; full span tree via detailed
opt-in
- Middleware advertises capabilities via get_capabilities() on the
Middleware base class
- Provisional API: FutureWarning emitted until SEP-2448 is ratified
Client-side (reference agent):
- LangChain ReAct agent connects to MCP server via
streamable_http_client with OAuth 2.1
- Detects serverExecutionTelemetry capability at initialization
- Dynamically wraps discovered MCP tools with traceparent propagation
and _meta.otel span request
- Ingests returned server spans into Jaeger (OTLP JSON) and Galileo
(OTLP protobuf)
- Two-act demo: --no-passback (black box) vs default (full server-side
visibility)
Dependencies:
- opentelemetry-api and opentelemetry-sdk added to arcade-mcp-server
Bump arcade-mcp-server version to 1.18.0.
Resolves TOO-201
Documentation PR for this is here:
https://github.com/ArcadeAI/docs/pull/626
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> **Medium Risk**
> Changes how environment variables/secrets are discovered and loaded,
which can subtly alter runtime behavior depending on directory structure
and existing env vars; bounded traversal and added tests reduce but
don’t eliminate this risk.
>
> **Overview**
> **Improves `.env` discovery across the MCP server and CLI.** Adds
`find_env_file()` (bounded by the nearest `pyproject.toml` by default)
and switches settings loading, `arcade deploy`, `arcade configure` stdio
env injection, and provider API-key resolution to use it.
>
> Updates dev reload to also watch the discovered `.env` even when it
lives outside the current working directory, adjusts `deploy --secrets
all` to only run when a `.env` was found, and moves the minimal
scaffold’s `.env.example` to the project root with updated
tests/integration checks. Version bumps align examples and top-level
deps with `arcade-mcp-server` `1.17.4` and `arcade-mcp` `1.11.2`.
>
> <sup>Written by [Cursor
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<!-- /CURSOR_SUMMARY -->
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
@EricGustin you can use this cli command:
```
uv run arcade evals mcp_building_evals_results/eval_toolkit_iteration_dict.py \
-p openai:gpt-4o,gpt-4o-mini \
-p anthropic:claude-sonnet-4-20250514 \
-k openai:$OPENAI_API_KEY \
-k anthropic:$ANTHROPIC_API_KEY \
-d \
--num-runs 3 \
--seed random \
--multi-run-pass-rule majority \
--max-concurrent 6 \
-o mcp_building_evals_results/results
```
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> **Medium Risk**
> Touches core eval execution and all result formatters while adding new
CLI inputs and output schema (`run_stats`/`critic_stats` and capture
`runs`), so regressions could affect evaluation results and report
compatibility despite being additive and validated.
>
> **Overview**
> Adds **multi-run evaluation support** to `arcade evals` via new flags
`--num-runs`, `--seed`, and `--multi-run-pass-rule`, with upfront
validation and plumbing through the CLI runner into eval/capture suite
execution.
>
> Fixes provider selection UX/bug by making `--use-provider/-p`
**repeatable** (instead of a space-delimited string), updates
docs/examples accordingly, and extends capture mode to optionally record
**per-run tool calls** (`CapturedRun`) when `num_runs > 1`.
>
> Enhances all output formatters (HTML/Markdown/Text/JSON) to
**propagate and display** per-case `run_stats` and `critic_stats`,
including new HTML UI for run tabs/cards and comparative tables showing
mean ± stddev when multi-run data is present.
>
> <sup>Written by [Cursor
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<!-- /CURSOR_SUMMARY -->
`arcade-mcp-server` version was not bumped in
https://github.com/ArcadeAI/arcade-mcp/pull/717, so this PR bumps
`arcade-mcp-server`, and then update's `arcade-mcp`'s dependency on
`arcade-mcp-server` by increasing the minimum version
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> Bumps arcade-mcp-server to 1.13.0, updates arcade-mcp to 1.6.2, and
raises related dependency minimums (including example auth server).
>
> - **Versions**:
> - Bump `libs/arcade-mcp-server` project version from `1.12.0` to
`1.13.0`.
> - Bump `arcade-mcp` package version from `1.6.1` to `1.6.2`.
> - **Dependencies**:
> - Raise `arcade-mcp` dependency on `arcade-mcp-server` to `>=1.13.0`
in `pyproject.toml` (including `all` extra).
> - Increase example server
`examples/mcp_servers/authorization/pyproject.toml` minimum
`arcade-mcp-server` to `>=1.12.0`.
>
> <sup>Written by [Cursor
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<!-- /CURSOR_SUMMARY -->
# Valuable references for the reviewer:
- Docs PR: https://github.com/ArcadeAI/docs/pull/583
- Implements Phase 1 of the following planning doc:
https://linear.app/arcadedev/project/arcade-mcp-supports-mcp-auth-front-door-auth-7cbaa20cb054/overviewhttps://github.com/user-attachments/assets/79ad43fd-f5e8-4793-a1dd-18b35acefdc3
# PR Description
Adds OAuth 2.1 Resource Server authentication to arcade-mcp-server,
enabling HTTP MCP servers to validate Bearer tokens on every request.
This unlocks tool-level authorization and secrets support for HTTP
servers.
- Multiple authorization server support
- Granular token validation options (verify_exp, verify_iat, verify_iss)
- Environment variable configuration
- OAuth discovery metadata endpoint
(/.well-known/oauth-protected-resource)
- Extracts sub claim from token as context.user_id
- Lifts transport restrictions for tools requiring auth/secrets on HTTP
when protected
```python
from arcade_mcp_server import MCPApp
from arcade_mcp_server.resource_server import ResourceServerAuth, AuthorizationServerEntry
resource_server_auth = ResourceServerAuth(
canonical_url="http://127.0.0.1:8000/mcp",
authorization_servers=[
AuthorizationServerEntry(
authorization_server_url="https://auth.example.com",
issuer="https://auth.example.com",
jwks_uri="https://auth.example.com/jwks",
)
],
)
app = MCPApp(name="my_server", version="1.0.0", auth=resource_server_auth)
```
# Testing
Beyond the comprehensive unit tests, I also manually tested end-to-end
with WorkOS Authkit (DCR) and KeyCloak (non-DCR).
# Future Work
- CIMD support
- An `ArcadeResourceServer` to make adding front-door auth super easy
when using Arcade's Auth Server
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> Adds OAuth 2.1 front-door auth (JWKS validation + OAuth discovery) and
propagates user identity to tools, enabling auth/secret-requiring tools
over HTTP.
>
> - **Authentication (Front-Door OAuth 2.1)**
> - New `resource_server` module with `ResourceServerAuth`
(multi-authorization-server, metadata) and `JWKSTokenValidator`
(JWKS-based JWT validation) plus granular validation options.
> - ASGI `ResourceServerMiddleware` validates Bearer tokens on every
HTTP request and injects `resource_owner`.
> - OAuth discovery endpoint via FastAPI router at
`/.well-known/oauth-protected-resource[/<path>]`.
> - **Integration**
> - `MCPApp`/`worker` accept `auth`/`resource_server_validator`, mount
middleware, expose discovery; logs accepted auth servers.
> - HTTP transport (`http_streamable`) carries `SessionMessage` with
`resource_owner` from request → session.
> - `Context`/`Session`/`Server` plumb `resource_owner`; `Server`
selects `user_id` preferring token `sub`.
> - **Behavior Changes**
> - HTTP transport restriction lifted for tools requiring
`authorization`/`secrets` when request is authenticated; otherwise
blocked with actionable error.
> - **Configuration**
> - Env-var based auth config via `MCP_RESOURCE_SERVER_*` in
`MCPSettings.ResourceServerSettings`; `.env` auto-load.
> - **Telemetry**
> - Usage tracking records `resource_server_type` on server start.
> - **Examples**
> - New `examples/mcp_servers/authorization` sample server (HTTP auth,
secrets, Reddit tool) with Docker setup.
> - **Tests**
> - Extensive unit tests for validators, middleware, env config,
multi-AS, transport rules, and app integration.
> - **Version**
> - Bump `arcade-mcp-server` to `1.12.0`; minor docstring tweak in
`__init__.py`.
>
> <sup>Written by [Cursor
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<!-- /CURSOR_SUMMARY -->
Resolves TOO-152
# PR Description
Consider this PR the result of a full pass through of this repository.
## Add helper for adding tools to an `MCPApp`
You can now add all of the tools in a module to an `MCPApp` via
`app.add_tools_from_module(...)`
## Edit what `arcade new` generates
First, I updated the backend to use hatchling.
Second, the structure generated before this PR was simple, but did not
create a proper Python module.
This hindered developers in the following ways:
1. Difficult to add the tools in your server to an evaluation suite
2. Difficult to add more than one tool to an MCPApp at a time
3. All other niceties that come with being able to import modules
```
# Before
server/
├── .env.example
├── server.py
└── pyproject.toml
```
This PR updates the structure generated such that a valid Python module
is generated:
```
# After
server/
├── pyproject.toml
└── src/
└── server/
├── __init__.py
├── .env.example
└── server.py
```
## Fix Tool Chaining
`self._ctx.server.executor.run(...)` was being called, but `MCPServer`
does not have an instance of `ToolExecutor` (and it's not intended to be
an instance anyways). I updated `Tool.call_raw` to pass the programmatic
tool call through the `MCPServer._handle_call_tool`. This means that the
programmatic tool calls now go through the same steps that a typical
tool call (initiated by the MCP client) would.
This means that **toolA**, which specifies **requirementsA**, is
permitted to call **toolB**, which specifies **requirementsB**, without
needing to explicitly declare or satisfy **requirementsB**. I believe
this is acceptable because the secrets and/or auth token associated with
**toolB's** `Context` are not exposed to **toolA**, and the secrets
and/or auth token associated with **toolA's** `Context` are not exposed
to **toolB**.
## Fix User Elicitation
1. The read & write streams were created with a maximum queue size of 0.
I increased this to 100.
2. I updated `ServerSession`'s run loop to both read messages from the
stream & process them concurrently. This enables server initiated
requests (like user elicitation and progress reporting) to be handled
while tools are being executed. Otherwise, the server initiated requests
would wait for the tool to finish executing and the tool execution would
wait for the server initiated request to finish.
3.
## Fix Progress Reporting
Progress tokens sent by the client were not being stored. Therefore
there was no way to notify a client with progress updates. I am now
storing the `progressToken`, along with other `_meta` sent from the
client, in the `ServerSession`'s `_request_meta`. I am setting
`_request_meta` whenever the `MCPServer` is handling an incoming message
from a client.
## Fix handling of server names with spaces
Before:
Server name: "The simple server name"
Tool name: whisper_secret
Name seen by client: "The_simple_server_name_WhisperSecret"
After
Server name: "The simple server name"
Tool name: whisper_secret
Name seen by client: "TheSimpleServerName_WhisperSecret"
## Add Integration Tests
The stdio integration test is much more comprehensive than the http
integration test. These tests will let me sleep a bit more at night
## Add Example MCP Servers
Example servers for sampling, user-elicitation, progress reporting,
logging, tool chaining, combining prebuilt tools with custom tools, tool
secrets, tool auth, evaluations, and more!
## Add Docker template
Added a Docker template for running an MCP server in Docker (and removed
the old docker stuff)
Versions:
* arcade-mcp\==1.0.0rc1
* arcade-mcp-server\==1.0.0rc1
* arcade-core\==2.5.0rc1
* arcade-tdk\==2.6.0rc1
* arcade-serve\==2.2.0rc1
### Summary
Adds first-class MCP support across Arcade, introduces a new MCP server
and CLI, unifies the project under the arcade-mcp name, overhauls
templates/scaffolding, and improves developer tooling, secrets
management, and examples.
### Highlights
- **MCP Server & Core**
- New MCP server with stdio and HTTP/SSE transports, session management,
resumability, and lifecycle handling.
- FastAPI-like `MCPApp` for building servers with lazy init; integrated
worker+MCP HTTP app option.
- Middleware system (logging and error handling), robust exception
hierarchy, and Pydantic-based settings.
- Async-safe managers for tools, resources, and prompts backed by
registries and locks.
- Developer-facing, transport-agnostic runtime context interfaces (logs,
tools, prompts, resources, sampling, UI, notifications).
- Conversion from Arcade ToolDefinition to MCP tool schema; OpenAI JSON
tool schema converter.
- Parser supports `@app.tool`/`@app.tool(...)` decorators.
- **CLI**
- New `mcp` command to run MCP servers with stdio or HTTP/SSE.
- New `secret` command to set/list/unset tool secrets (supports .env
input, preserves original casing for lookups).
- `new` command refactored; option to create a full toolkit package with
scaffolding.
- `chat` command removed.
- `serve.py` imports updated to `arcade_serve.fastapi.telemetry`;
version retrieval now uses `arcade-mcp`.
- `show.py` refactor to use new local catalog utilities.
- `display_tool_details` improved: adds “Default” column and handles
nested properties.
- **Configuration & Discovery**
- New `configure.py` to set up Claude Desktop, Cursor, and VS Code to
connect to local or Arcade Cloud MCP servers.
- Discovery utilities to find/install toolkits, build `ToolCatalog`s,
analyze files for tools, load kits from directories (pyproject parsing),
and build minimal toolkits.
- Better handling of provider API key resolution and evaluation suite
loading.
- **Templates & Scaffolding**
- Reorganized template structure (minimal vs full); moved
`.pre-commit-config.yaml`, `.ruff.toml`, license, Makefile, README,
tests, and tools layout to correct paths.
- Minimal template adds `.env.example` for runtime secret injection.
- Template pyproject updated for MCP servers; includes sample server
with greeting and secret-reveal tools.
- Authorization flow in templates simplified.
- **Repo-wide Renaming & Examples**
- Migrates references from `arcade-ai` to `arcade-mcp` across READMEs,
scripts, and package metadata.
- Examples updated (LangChain/LangGraph/AI SDK/TypeScript) and package
name changed to `arcade-mcp-sdk`.
- **Evals & Core Utilities**
- Evals now use OpenAI tooling format (`OpenAIToolList`, `to_openai`);
`tool_eval` takes `provider_api_key`.
- Core utilities: fixed `does_function_return_value` by dedenting before
parse; version bump to `2.5.0rc1` and dependency cleanup.
- **Tooling & CI**
- `setup-uv-env` action splits toolkit vs contrib dependency
installation.
- Pre-commit: excludes `libs/arcade-mcp-server/mkdocs.yml` and
`libs/tests/` from YAML and Ruff hooks; Ruff per-file ignores (e.g.,
C901 in `libs/**/*.py`, TRY400 in server docs paths).
- Makefile updates for uv env setup, quality checks, tests, builds, and
new `shell` target.
- Added Makefile to MCP server library to streamline dev workflow.
- **Cleanup**
- Removed `claude.json` config.
- Simplified stdio entrypoint; removed unused imports (`arcade_gmail`,
`arcade_search`).
### Breaking Changes
- **CLI**: `chat` command removed; use `mcp`, `secret`, and updated
`new`.
- **Naming**: All users should update references from `arcade-ai` to
`arcade-mcp`.
- **Templates**: File paths moved; downstream scripts referencing old
template locations may need updates.
### Getting Started
- Run an MCP server:
- `arcade mcp --stdio --toolkits your_toolkit`
- `arcade mcp --http --toolkits your_toolkit`
- Manage secrets:
- `arcade secret set your_toolkit KEY=value`
- `arcade secret list your_toolkit`
- `arcade secret unset your_toolkit KEY`
- Configure clients:
- `arcade configure` to set up Claude Desktop, Cursor, and VS Code for
local/Arcade Cloud MCP.
---------
Co-authored-by: Sam Partee <sam@arcade-ai.com>
Co-authored-by: Shub <125150494+shubcodes@users.noreply.github.com>
## Summary
Updated the Mastra example project to include leveraging multiple
toolkits into one Mastra agent. Additionally, the `package.json`
versions were pinned to the latest versions to avoid potential issues
with breaking changes as Mastra progresses through v0.x.x packages.
Also, by pinning the version, it demonstrates the latest confirmed
arcade-js compatibility version.
These changes were initiated when trying to debug an
[issue](https://github.com/ArcadeAI/arcade-ai/issues/560) using Arcade
toolkits with Mastra agents. Notably, I confirmed that there is no flaw
in arcade-js, which was suspected in the issue, by successfully using
the toolkits with the latest Mastra packages.
## Changes
* inboxTravelAgent created showcasing `GoogleFlights`, `GoogleHotels`,
and `Gmail` Arcade toolkits together
* package.json versions pinned to the latest working versions.
## Testing
Ran the Mastra project locally and invoked the tools via the agent chat
interface.
### Overview
Major restructuring from monolithic `arcade-ai` package to modular
library architecture with standardized uv-based dependency management.
### New Package Structure
- **`arcade-tdk`** - Lightweight toolkit development kit (core
decorators, auth)
- **`arcade-core`** - Core execution engine and catalog functionality
- **`arcade-serve`** - FastAPI/MCP server components
- **`arcade-ai`** - Meta package that includes CLI functionality.
Optionally include evals via the `evals` extra. Optionally include all
packages via the `all` extra.
### Key Benefits
- **Lighter Dependencies**: Toolkits now depend only on `arcade-tdk` (~2
deps) vs full `arcade-ai` (~30+ deps)
- **Faster Builds**: uv provides 10-100x faster dependency resolution
and installation
- **Better Modularity**: Clear separation of concerns, consumers import
only what they need
- **Standard Tooling**: Eliminates custom poetry scripts, uses standard
Python packaging
### Migration Impact
- All 20 toolkits converted from poetry → uv with `arcade-tdk`
dependencies plus `arcade-ai[evals]` and `arcade-serve` dev
dependencies. When developing locally, devs should install toolkits via
`make install-local`.
- Modern Python 3.10+ type hints throughout
- Standardized build system with hatchling backend
- Enhanced Makefile with robust toolkit management commands
- Removed `arcade dev` CLI command
- Reduce the number of files created by `arcade new` and add an option
to not generate a tests and evals folder.
This foundation enables faster development cycles and cleaner dependency
chains for the growing toolkit ecosystem.
### Todo After this PR is merged
- [ ] Post-merge workflow(s) (release & publish containers, etc)
- [ ] Release order plan. @EricGustin suggests releasing in the
following order:
1. `arcade-core` version 0.1.0
2. `arcade-serve` version 0.1.0 and `arcade-tdk` version 0.1.0
3. `arcade-ai` version 2.0.0
4. Patch release for all toolkits (all changes in toolkits are internal
refactors)
- [ ] [Update docs](https://github.com/ArcadeAI/docs/pull/318)
---------
Co-authored-by: Eric Gustin <eric@arcade.dev>
Co-authored-by: Eric Gustin <34000337+EricGustin@users.noreply.github.com>
Update our example with the latest version of `arcade-js`. This means we
can delete all the utility functions we created here, since we now have
first-class `Zod` support and they are no longer needed.
> [!WARNING]
> Don't merge until this
[PR](https://github.com/ArcadeAI/arcade-js/pull/127) is merged.
MCP stdio Implementation:
The PR adds support for standard input/output (stdio) as a transport
mechanism for the Message Control Protocol. This is a replacement to the
SSE (Server-Sent Events) transport that was worked on in PR #359 but
will not be merged as it's not deprecated.
This will allow developers to use Arcade tools (written by the dev or
Arcade) in Claude, Cursor, windsurf, etc.
The engine Gateway already supports adding HTTPS streamable (replacement
for SSE) MCP servers as tool servers, and will soon support full gateway
capability in the client API as well.
To use any existing Toolkit just
## Examples
### Quickstart setup with existing toolkits
```bash
pip install arcade-ai
pip install <name of toolkit> # ex. arcade-google
arcade serve --mcp
```
### Run with Claude
Just add the following to the Claude config
```json
{
"mcpServers": {
"arcade": {
"command": "bash",
"args": ["-c", "export ARCADE_API_KEY=arc_xxxx && /path/to/python /path/to/arcade serve --mcp"]
}
}
}
```
### Customizing the Tool Server
Developers can customize their served tools and server furthermore by
importing the worker sdk
```python
import arcade_google # pip install arcade_google
import arcade_search # pip install arcade_search
from arcade.core.catalog import ToolCatalog
from arcade.worker.mcp.stdio import StdioServer
# 2. Create and populate the tool catalog
catalog = ToolCatalog()
catalog.add_module(arcade_google) # Registers all tools in the package
catalog.add_module(arcade_search)
# 3. Main entrypoint
async def main():
# Create the worker with the tool catalog
worker = StdioServer(catalog)
# Run the worker
await worker.run()
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
Then to run with claude, just run this python file instead of the prebuilt server used in ``arcade serve --mcp``
This PR adds a new example showcasing how to integrate Arcade tools with
LangGraph.js to create a ReAct agent. The example is based on the
[LangChain React Agent
JS](https://github.com/langchain-ai/react-agent-js/tree/main)
repository.
It is unnecessary to call `arcadeClient.tools.list` first and then
`arcadeClient.tools.formatted.get` for each tool. We can simply use the
`arcadeClient.tools.formatted.list` function.
Small update to reflect some changes to the `langchain-arcade` package
in the last release.
---------
Co-authored-by: Eric Gustin <34000337+EricGustin@users.noreply.github.com>
- **New Class Structure**: Introduced `ToolManager` and
`AsyncToolManager` classes (`ArcadeToolManager` is deprecated)
- **Async Support**: Full async implementation for modern LangChain
applications
- **Better Tool Management**: New methods for adding individual tools
and toolkits
- **CI/CD**: for langchain_arcade
## Upgrade Changes
```python
# Old pattern
manager = ArcadeToolManager(api_key="...")
tools = manager.get_tools(toolkits=["Google"])
# New pattern
manager = ToolManager(api_key="...")
manager.init_tools(toolkits=["Google"])
tools = manager.to_langchain()
```
Now supports underscores vs dots in tool names for better model
compatibility.
# CrewAI Integration
crewai-arcade enables you to add Arcade tools and Arcade Auth into your
CrewAI applications. Just create an `ArcadeToolManager` and add your
tools to your CrewAI Agent/Tasks.
## Initializing the ArcadeToolManager
There are two main ways to initialize your `ArcadeToolManager`
1. Default handling of tool authorization and execution:
```py
"""
When you provide a user id to the ArcadeToolManger,
it will handle the tool authorization and tool execution for you
"""
manager = ArcadeToolManager(default_user_id="me@example.com,
api_key="...")
```
2. Custom handling of tool authorization and execution
```py
"""
Provide a callback function to the `ArcadeToolManager` that handles
tool authorization and tool execution. The callback function will be
called whenever your CrewAI
application wants to call a tool.
"""
def custom_tool_executor(
manager: ArcadeToolManager, tool_name: str, **tool_input: dict[str, Any]
) -> Any:
"""Custom tool executor for the ArcadeToolManager
ArcadeToolManager's default executor handles authorization and tool
execution.
This function overrides the default executor to handle authorization and
tool execution
in a custom way.
"""
# Your custom tool auth logic goes here
# Your custom tool execution logic goes here
...
manager = ArcadeToolManager(executor=custom_tool_executor,
api_key="...")
```
## Tool Registration
1. Initialize the tools in the manager
```py
"""
Clears any existing tools in the manager and replaces them with tools
and toolkits that are provided.
"""
manager.init_tools(tools=["Google.ListEmails"], toolkits=["Slack"])
```
2. Add tools to the manager
```py
"""
Adds tools and toolkits to the manager's internal tool list.
"""
manager.add_tools(tools=["Google.ListEmails"], toolkits=["Slack"])
```
3. Retrieve tools and toolkits from the manager
```py
"""
Retrieves the provided tools and toolkits as CrewAI StructuredTools.
"""
manager.get_tools(tools=["Google.ListEmails"], toolkits=["Slack"])
```
## Auth Helpers
The `ArcadeToolManager` provides multiple helper methods for when you
need to create
a custom auth flow.
1. `authorize_tool` handles the whole authorization flow for you. This
is used internally when a custom auth flow is not needed.
2. `requires_auth(tool_name)` checks if the provided tool has
authorization requirements.
3. `authorize(tool_name, user_id)` authorizes the use of the provided
tool for the provided user ID
4. `is_authorized(tool_name, user_id)` checks if a tool is authorized
for use by the provided user ID
5. `wait_for_auth(auth_response)` waits for an authorization process to
complete before returning
## Tool Execution Helpers
1. `execute_tool` handles the whole tool execution flow for you. This is
used internally when a custom tool execution flow is not needed.
---------
Co-authored-by: lgesuellip <lgesuellipinto@uade.edu.ar>
Co-authored-by: lpetralli <123559656+lpetralli@users.noreply.github.com>
Co-authored-by: lgesuellip <102637283+lgesuellip@users.noreply.github.com>
Co-authored-by: “lgesuellip” <“lgesuellipinto@uade.edu.ar”>
## PR Description
Changes `pip install 'arcade-ai[fastapi]'` to `pip install arcade-ai`.
In other words, FastAPI is now a required dependecy of arcade-ai.
Additionally, I snuck in some minor cleanup changes.
# PR Description
* This PR updates code in `examples/` to be compatible with version
1.0.0
* This PR removes the Spotify examples since the Arcade hosted worker
doesn't currently cataloge the Spotify toolkit. We can reintroduce these
examples when it does.
* This PR performs various renames across the codebase for
`arcade-ai.com` --> `arcade.dev` and `Arcade AI` --> `Arcade`
This PR updates the LangChain Arcade integration to v1.0.0, making the
following key changes:
• Bumped the package version in pyproject.toml from 0.2.0 to 1.0.0.
• Changed the default parameter in ArcadeToolManager from
langgraph=False to langgraph=True.
• Updated dependencies to require langgraph≥0.2.67,<0.3.0 and simplified
extras.
• Adjusted example scripts to remove explicit authorization_url
references in favor of a unified URL field.
• Updated docs and environment references to align with new usage
patterns and emphasize environment variables.
These changes unify and streamline the LangGraph-based tooling while
ensuring compatibility with the latest 1.0.0 release.
This PR enhances the Docker build and deployment process for the Arcade
Worker by:
- **Modularizing Docker Builds:**
- Introduces a new `INSTALL_TOOLKITS` build argument in the `Dockerfile`
to conditionally include toolkits. this enables the creation of a
`arcadeai/worker-base` which can be used to build custom containers in a
multi-stage build. an example of this is included in the example dir.
- Adds `docker-base` Makefile target to build a lightweight base image
without toolkits.
- **Publishing to GitHub Container Registry (GHCR):**
- Adds Makefile targets `publish-ghcr` and `gh-login` for pushing images
to GHCR.
- Supports publishing both base and full images with toolkits to GHCR.
- **Docker Compose:**
- Add Docker compose file and setup
- Renames the `actor` service to `worker`
- Adds an `nginx` service in `docker-compose.yml` to proxy requests to
the Arcade Engine.
- Introduces an `nginx.conf` file for the Nginx service.
- **Streamlining Toolkit Installation:**
- Moves toolkit installation from the `start.sh` script to the Docker
build process.
- Creates a `toolkits.txt` file to manage toolkit dependencies which can
be edited easily when we want to add a new toolkit. Developers can also
use this approach as shown in the example.
- **Improving Configuration Files:**
- Updates `docker.engine.yaml` and `env.example` to align with the new
setup.
TODO:
- CI/CD needs to be adjusted so that images are pushed to ghcr on
release.
- AWS resources need to be renamed actor -> worker @EricGustin
This can go in after the above two items are resolved.
---------
Co-authored-by: Wils Dawson <wils@arcade-ai.com>
Co-authored-by: Eric Gustin <34000337+EricGustin@users.noreply.github.com>
Co-authored-by: sdreyer <sterling@arcade-ai.com>
Co-authored-by: Sterling Dreyer <sdreyer21@gmail.com>
Co-authored-by: Nate Barbettini <nathanaelb@gmail.com>
**PR Description**
This update bumps the integration’s version to `0.2.0` and brings
several important changes to how `langchain-arcade` interfaces with
Arcade tools:
1. **Updated Tool Definition Imports**
• Replaces `arcadepy.types.shared.ToolDefinition` with
`arcadepy.types.ToolGetResponse as ToolDefinition`.
• The parameter extraction is now done via `tool_def.input.parameters`
instead of the previous `tool_def.inputs.parameters`.
2. **Authorization Flow Adjustments**
• Uses `auth_response.url` instead of `auth_response.authorization_url`.
• The `authorize` and `is_authorized` methods now rely on the Arcade
client’s updated arguments (`client.auth.status(id=authorization_id)`).
3. **Tool Execution Parameter Renaming**
• The `execute` method now expects `input=kwargs` instead of
`inputs=kwargs`, aligning with Arcade’s new API spec.
4. **Tool Retrieval Enhancements**
• `_retrieve_tool_definitions` is revised to better handle pagination
and tool listing (including when no tools/toolkits are explicitly
provided).
5. **Version & Dependency Updates**
• Increases `langchain-arcade` to `0.2.0`.
• Switches `arcadepy` dependency to `~1.0.0rc1`.
• Updates example requirements to consume
`langchain-arcade[langgraph]>=0.2.0`.
These changes may affect existing code that relies on older parameter
names (`inputs.parameters` → `input.parameters`) and the renamed execute
argument. Please ensure any integrations or custom usage of Arcade tools
is updated accordingly.
Refactor toolkit implementation examples following the removal of two
Spotify tools in
[PR#196](https://github.com/ArcadeAI/arcade-ai/pull/196), due to Spotify
API deprecation announcement.
## PR Description
This PR adds 7 examples.
* `call_a_tool_directly_with_auth.py` - Simple example that uses Arcade
client to execute a tool that lists Gmail emails
* `call_a_tool_directly.py` - Simple example that uses Arcade client to
execute a tool that adds two numbers together
* `call_a_tool_with_llm.py` - Simple example that uses the LLM api to
star the arcade-ai repository
* `get_auth_token.py` - Simple example that gets a Google auth token and
then calls the Google API
* `call_multiple_tools_directly_with_auth.py` - A more involved example
that directly calls multiple spotify tools sequentially
* `call_multiple_tools_with_llm.py` - A more involved example that uses
an llm to call multiple spotify tools sequentially
* `simple_chatbot.py` - Simple chatbot that uses arcade tools and has
history
---------
Co-authored-by: Nate Barbettini <nathanaelb@gmail.com>
