Merge branch 'main' into feature/runtime-tools

Author: NikBellini

.pre-commit-config.yaml

@@ -7,6 +7,20 @@ repos:
       - id: prettier
         types_or: [yaml, json5]
 
+  - repo: https://github.com/igorshubovych/markdownlint-cli
+    rev: v0.45.0
+    hooks:
+      - id: markdownlint
+        args:
+          [
+            "--fix",
+            "--config",
+            "pyproject.toml",
+            "--configPointer",
+            "/tool/markdown/lint",
+          ]
+        types: [markdown]
+
   - repo: local
     hooks:
       - id: ruff-format

CLAUDE.md

@@ -26,15 +26,19 @@ This document contains critical information about working with this codebase. Fo
    - Bug fixes require regression tests
 
 - For commits fixing bugs or adding features based on user reports add:
+
   ```bash
   git commit --trailer "Reported-by:<name>"
   ```
+
   Where `<name>` is the name of the user.
 
 - For commits related to a Github issue, add
+
   ```bash
   git commit --trailer "Github-Issue:#<number>"
   ```
+
 - NEVER ever mention a `co-authored-by` or similar aspects. In particular, never
   mention the tool used to create the commit message or PR.
 

CODE_OF_CONDUCT.md

@@ -60,7 +60,7 @@ representative at an online or offline event.
 
 Instances of abusive, harassing, or otherwise unacceptable behavior may be
 reported to the community leaders responsible for enforcement at
-mcp-coc@anthropic.com.
+<mcp-coc@anthropic.com>.
 All complaints will be reviewed and investigated promptly and fairly.
 
 All community leaders are obligated to respect the privacy and security of the
@@ -116,13 +116,13 @@ the community.
 
 This Code of Conduct is adapted from the [Contributor Covenant][homepage],
 version 2.0, available at
-https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+<https://www.contributor-covenant.org/version/2/0/code_of_conduct.html>.
 
 Community Impact Guidelines were inspired by [Mozilla's code of conduct
 enforcement ladder](https://github.com/mozilla/diversity).
 
 [homepage]: https://www.contributor-covenant.org
 
 For answers to common questions about this code of conduct, see the FAQ at
-https://www.contributor-covenant.org/faq. Translations are available at
-https://www.contributor-covenant.org/translations.
+<https://www.contributor-covenant.org/faq>. Translations are available at
+<https://www.contributor-covenant.org/translations>.

CONTRIBUTING.md

@@ -9,6 +9,7 @@ Thank you for your interest in contributing to the MCP Python SDK! This document
 3. Fork the repository
 4. Clone your fork: `git clone https://github.com/YOUR-USERNAME/python-sdk.git`
 5. Install dependencies:
+
 ```bash
 uv sync --frozen --all-extras --dev
 ```
@@ -25,22 +26,26 @@ uv sync --frozen --all-extras --dev
 3. Make your changes
 
 4. Ensure tests pass:
-```bash 
+
+```bash
 uv run pytest
 ```
 
 5. Run type checking:
+
 ```bash
 uv run pyright
 ```
 
 6. Run linting:
+
 ```bash
 uv run ruff check .
 uv run ruff format .
 ```
 
 7. Update README snippets if you modified example code:
+
 ```bash
 uv run scripts/update_readme_snippets.py
 ```

README.md

@@ -96,6 +96,7 @@ If you haven't created a uv-managed project yet, create one:
    ```
 
 Alternatively, for projects using pip for dependencies:
+
 ```bash
 pip install "mcp[cli]"
 ```
@@ -135,11 +136,13 @@ def get_greeting(name: str) -> str:
 ```
 
 You can install this server in [Claude Desktop](https://claude.ai/download) and interact with it right away by running:
+
 ```bash
 uv run mcp install server.py
 ```
 
 Alternatively, you can test it with the MCP Inspector:
+
 ```bash
 uv run mcp dev server.py
 ```
@@ -202,7 +205,7 @@ mcp = FastMCP("My App", lifespan=app_lifespan)
 def query_db() -> str:
     """Tool that uses initialized resources"""
     ctx = mcp.get_context()
-    db = ctx.request_context.lifespan_context["db"]
+    db = ctx.request_context.lifespan_context.db
     return db.query()
 ```
 
@@ -233,6 +236,7 @@ def get_settings() -> str:
   "debug": false
 }"""
 ```
+
 _Full example: [examples/snippets/servers/basic_resource.py](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/basic_resource.py)_
 <!-- /snippet-source -->
 
@@ -259,15 +263,17 @@ def get_weather(city: str, unit: str = "celsius") -> str:
     # This would normally call a weather API
     return f"Weather in {city}: 22degrees{unit[0].upper()}"
 ```
+
 _Full example: [examples/snippets/servers/basic_tool.py](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/basic_tool.py)_
 <!-- /snippet-source -->
 
 #### Structured Output
 
 Tools will return structured results by default, if their return type
-annotation is compatible. Otherwise, they will return unstructured results. 
+annotation is compatible. Otherwise, they will return unstructured results.
 
 Structured output supports these return types:
+
 - Pydantic models (BaseModel subclasses)
 - TypedDicts
 - Dataclasses and other classes with type hints
@@ -279,17 +285,17 @@ Classes without type hints cannot be serialized for structured output. Only
 classes with properly annotated attributes will be converted to Pydantic models
 for schema generation and validation.
 
-Structured results are automatically validated against the output schema 
-generated from the annotation. This ensures the tool returns well-typed, 
+Structured results are automatically validated against the output schema
+generated from the annotation. This ensures the tool returns well-typed,
 validated data that clients can easily process.
 
 **Note:** For backward compatibility, unstructured results are also
-returned. Unstructured results are provided for backward compatibility 
+returned. Unstructured results are provided for backward compatibility
 with previous versions of the MCP specification, and are quirks-compatible
 with previous versions of FastMCP in the current version of the SDK.
 
-**Note:** In cases where a tool function's return type annotation 
-causes the tool to be classified as structured _and this is undesirable_, 
+**Note:** In cases where a tool function's return type annotation
+causes the tool to be classified as structured _and this is undesirable_,
 the  classification can be suppressed by passing `structured_output=False`
 to the `@tool` decorator.
 
@@ -446,6 +452,7 @@ def debug_error(error: str) -> list[base.Message]:
         base.AssistantMessage("I'll help debug that. What have you tried so far?"),
     ]
 ```
+
 _Full example: [examples/snippets/servers/basic_prompt.py](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/basic_prompt.py)_
 <!-- /snippet-source -->
 
@@ -495,6 +502,7 @@ async def long_running_task(task_name: str, ctx: Context, steps: int = 5) -> str
 
     return f"Task '{task_name}' completed"
 ```
+
 _Full example: [examples/snippets/servers/tool_progress.py](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/tool_progress.py)_
 <!-- /snippet-source -->
 
@@ -503,6 +511,7 @@ _Full example: [examples/snippets/servers/tool_progress.py](https://github.com/m
 MCP supports providing completion suggestions for prompt arguments and resource template parameters. With the context parameter, servers can provide completions based on previously resolved values:
 
 Client usage:
+
 ```python
 from mcp.client.session import ClientSession
 from mcp.types import ResourceTemplateReference
@@ -581,6 +590,7 @@ async def handle_completion(
 
     return None
 ```
+
 _Full example: [examples/snippets/servers/completion.py](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/completion.py)_
 <!-- /snippet-source -->
 ### Elicitation
@@ -631,10 +641,12 @@ async def book_table(
     # Date available
     return f"[SUCCESS] Booked for {date} at {time}"
 ```
+
 _Full example: [examples/snippets/servers/elicitation.py](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/elicitation.py)_
 <!-- /snippet-source -->
 
 The `elicit()` method returns an `ElicitationResult` with:
+
 - `action`: "accept", "decline", or "cancel"
 - `data`: The validated response (only when accepted)
 - `validation_error`: Any validation error message
@@ -670,6 +682,7 @@ async def generate_poem(topic: str, ctx: Context) -> str:
         return result.content.text
     return str(result.content)
 ```
+
 _Full example: [examples/snippets/servers/sampling.py](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/sampling.py)_
 <!-- /snippet-source -->
 
@@ -698,6 +711,7 @@ async def process_data(data: str, ctx: Context) -> str:
 
     return f"Processed: {data}"
 ```
+
 _Full example: [examples/snippets/servers/notifications.py](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/notifications.py)_
 <!-- /snippet-source -->
 
@@ -736,6 +750,7 @@ mcp = FastMCP(
 For a complete example with separate Authorization Server and Resource Server implementations, see [`examples/servers/simple-auth/`](examples/servers/simple-auth/).
 
 **Architecture:**
+
 - **Authorization Server (AS)**: Handles OAuth flows, user authentication, and token issuance
 - **Resource Server (RS)**: Your MCP server that validates tokens and serves protected resources
 - **Client**: Discovers AS through RFC 9728, obtains tokens, and uses them with the MCP server
@@ -787,6 +802,7 @@ if __name__ == "__main__":
 ```
 
 Run it with:
+
 ```bash
 python server.py
 # or
@@ -866,10 +882,12 @@ app.mount("/math", math.mcp.streamable_http_app())
 ```
 
 For low level server with Streamable HTTP implementations, see:
+
 - Stateful server: [`examples/servers/simple-streamablehttp/`](examples/servers/simple-streamablehttp/)
 - Stateless server: [`examples/servers/simple-streamablehttp-stateless/`](examples/servers/simple-streamablehttp-stateless/)
 
 The streamable HTTP transport supports:
+
 - Stateful and stateless operation modes
 - Resumability with event stores
 - JSON or SSE response formats
@@ -1042,6 +1060,7 @@ async def query_db(name: str, arguments: dict) -> list:
 ```
 
 The lifespan API provides:
+
 - A way to initialize resources when the server starts and clean them up when it stops
 - Access to initialized resources through the request context in handlers
 - Type-safe context passing between lifespan and request handlers
@@ -1168,6 +1187,7 @@ async def call_tool(name: str, arguments: dict[str, Any]) -> dict[str, Any]:
 ```
 
 Tools can return data in three ways:
+
 1. **Content only**: Return a list of content blocks (default behavior before spec revision 2025-06-18)
 2. **Structured data only**: Return a dictionary that will be serialized to JSON (Introduced in spec revision 2025-06-18)
 3. **Both**: Return a tuple of (content, structured_data) preferred option to use for backwards compatibility
@@ -1293,6 +1313,7 @@ async def display_resources(session: ClientSession):
 ```
 
 The `get_display_name()` function implements the proper precedence rules for displaying names:
+
 - For tools: `title` > `annotations.title` > `name`
 - For other objects: `title` > `name`
 
@@ -1351,7 +1372,6 @@ async def main():
 
 For a complete working example, see [`examples/clients/simple-auth-client/`](examples/clients/simple-auth-client/).
 
-
 ### MCP Primitives
 
 The MCP protocol defines three core primitives that servers can implement:

SECURITY.md

@@ -1,4 +1,5 @@
 # Security Policy
+
 Thank you for helping us keep the SDKs and systems they interact with secure.
 
 ## Reporting Security Issues

examples/clients/simple-auth-client/README.md

@@ -47,7 +47,7 @@ The client will open your browser for authentication. After completing OAuth, yo
 
 ## Example
 
-```
+```markdown
 🔐 Simple MCP Auth Client
 Connecting to: http://localhost:3001
 

examples/clients/simple-chatbot/README.MD

@@ -25,11 +25,12 @@ This example demonstrates how to integrate the Model Context Protocol (MCP) into
    ```plaintext
    LLM_API_KEY=your_api_key_here
    ```
+
    **Note:** The current implementation is configured to use the Groq API endpoint (`https://api.groq.com/openai/v1/chat/completions`) with the `llama-3.2-90b-vision-preview` model. If you plan to use a different LLM provider, you'll need to modify the `LLMClient` class in `main.py` to use the appropriate endpoint URL and model parameters.
 
 3. **Configure servers:**
 
-   The `servers_config.json` follows the same structure as Claude Desktop, allowing for easy integration of multiple servers. 
+   The `servers_config.json` follows the same structure as Claude Desktop, allowing for easy integration of multiple servers.
    Here's an example:
 
    ```json
@@ -46,9 +47,11 @@ This example demonstrates how to integrate the Model Context Protocol (MCP) into
      }
    }
    ```
+
    Environment variables are supported as well. Pass them as you would with the Claude Desktop App.
 
    Example:
+
    ```json
    {
      "mcpServers": {
@@ -72,7 +75,7 @@ This example demonstrates how to integrate the Model Context Protocol (MCP) into
    ```
 
 2. **Interact with the assistant:**
-   
+
    The assistant will automatically detect available tools and can respond to queries based on the tools provided by the configured servers.
 
 3. **Exit the session:**
@@ -86,6 +89,7 @@ This example demonstrates how to integrate the Model Context Protocol (MCP) into
 - **Server Integration**: Supports any MCP-compatible server, tested with various server implementations including Uvicorn and Node.js.
 
 ### Class Structure
+
 - **Configuration**: Manages environment variables and server configurations
 - **Server**: Handles MCP server initialization, tool discovery, and execution
 - **Tool**: Represents individual tools with their properties and formatting
@@ -107,5 +111,3 @@ This example demonstrates how to integrate the Model Context Protocol (MCP) into
      - If it's a direct response → return to user
    - Tool results are sent back to LLM for interpretation
    - Final response is presented to user
-
-