新增用户意图确认功能
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# MCP (Model Context Protocol) Configuration
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DeerFlow supports configurable MCP servers and skills to extend its capabilities, which are loaded from a dedicated `extensions_config.json` file in the project root directory.
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## Setup
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1. Copy `extensions_config.example.json` to `extensions_config.json` in the project root directory.
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```bash
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# Copy example configuration
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cp extensions_config.example.json extensions_config.json
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```
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2. Enable the desired MCP servers or skills by setting `"enabled": true`.
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3. Configure each server’s command, arguments, and environment variables as needed.
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4. Restart the application to load and register MCP tools.
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## OAuth Support (HTTP/SSE MCP Servers)
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For `http` and `sse` MCP servers, DeerFlow supports OAuth token acquisition and automatic token refresh.
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- Supported grants: `client_credentials`, `refresh_token`
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- Configure per-server `oauth` block in `extensions_config.json`
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- Secrets should be provided via environment variables (for example: `$MCP_OAUTH_CLIENT_SECRET`)
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Example:
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```json
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{
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"mcpServers": {
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"secure-http-server": {
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"enabled": true,
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"type": "http",
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"url": "https://api.example.com/mcp",
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"oauth": {
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"enabled": true,
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"token_url": "https://auth.example.com/oauth/token",
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"grant_type": "client_credentials",
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"client_id": "$MCP_OAUTH_CLIENT_ID",
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"client_secret": "$MCP_OAUTH_CLIENT_SECRET",
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"scope": "mcp.read",
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"refresh_skew_seconds": 60
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}
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}
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}
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}
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```
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## Custom Tool Interceptors
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You can register custom interceptors that run before every MCP tool call. This is useful for injecting per-request headers (e.g., user auth tokens from the LangGraph execution context), logging, or metrics.
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Declare interceptors in `extensions_config.json` using the `mcpInterceptors` field:
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```json
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{
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"mcpInterceptors": [
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"my_package.mcp.auth:build_auth_interceptor"
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],
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"mcpServers": { ... }
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}
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```
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Each entry is a Python import path in `module:variable` format (resolved via `resolve_variable`). The variable must be a **no-arg builder function** that returns an async interceptor compatible with `MultiServerMCPClient`’s `tool_interceptors` interface, or `None` to skip.
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Example interceptor that injects auth headers from LangGraph metadata:
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```python
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def build_auth_interceptor():
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async def interceptor(request, handler):
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from langgraph.config import get_config
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metadata = get_config().get("metadata", {})
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headers = dict(request.headers or {})
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if token := metadata.get("auth_token"):
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headers["X-Auth-Token"] = token
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return await handler(request.override(headers=headers))
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return interceptor
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```
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- A single string value is accepted and normalized to a one-element list.
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- Invalid paths or builder failures are logged as warnings without blocking other interceptors.
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- The builder return value must be `callable`; non-callable values are skipped with a warning.
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## How It Works
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MCP servers expose tools that are automatically discovered and integrated into DeerFlow’s agent system at runtime. Once enabled, these tools become available to agents without additional code changes.
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## Example Capabilities
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MCP servers can provide access to:
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- **File systems**
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- **Databases** (e.g., PostgreSQL)
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- **External APIs** (e.g., GitHub, Brave Search)
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- **Browser automation** (e.g., Puppeteer)
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- **Custom MCP server implementations**
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## Learn More
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For detailed documentation about the Model Context Protocol, visit:
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# MCP (Model Context Protocol) Configuration
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DeerFlow supports configurable MCP servers and skills to extend its capabilities, which are loaded from a dedicated `extensions_config.json` file in the project root directory.
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## Setup
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1. Copy `extensions_config.example.json` to `extensions_config.json` in the project root directory.
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```bash
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# Copy example configuration
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cp extensions_config.example.json extensions_config.json
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```
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2. Enable the desired MCP servers or skills by setting `"enabled": true`.
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3. Configure each server’s command, arguments, and environment variables as needed.
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4. Restart the application to load and register MCP tools.
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## OAuth Support (HTTP/SSE MCP Servers)
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For `http` and `sse` MCP servers, DeerFlow supports OAuth token acquisition and automatic token refresh.
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- Supported grants: `client_credentials`, `refresh_token`
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- Configure per-server `oauth` block in `extensions_config.json`
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- Secrets should be provided via environment variables (for example: `$MCP_OAUTH_CLIENT_SECRET`)
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Example:
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```json
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{
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"mcpServers": {
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"secure-http-server": {
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"enabled": true,
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"type": "http",
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"url": "https://api.example.com/mcp",
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"oauth": {
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"enabled": true,
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"token_url": "https://auth.example.com/oauth/token",
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"grant_type": "client_credentials",
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"client_id": "$MCP_OAUTH_CLIENT_ID",
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"client_secret": "$MCP_OAUTH_CLIENT_SECRET",
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"scope": "mcp.read",
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"refresh_skew_seconds": 60
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}
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}
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}
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}
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```
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## Custom Tool Interceptors
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You can register custom interceptors that run before every MCP tool call. This is useful for injecting per-request headers (e.g., user auth tokens from the LangGraph execution context), logging, or metrics.
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Declare interceptors in `extensions_config.json` using the `mcpInterceptors` field:
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```json
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{
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"mcpInterceptors": [
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"my_package.mcp.auth:build_auth_interceptor"
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],
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"mcpServers": { ... }
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}
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```
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Each entry is a Python import path in `module:variable` format (resolved via `resolve_variable`). The variable must be a **no-arg builder function** that returns an async interceptor compatible with `MultiServerMCPClient`’s `tool_interceptors` interface, or `None` to skip.
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Example interceptor that injects auth headers from LangGraph metadata:
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```python
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def build_auth_interceptor():
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async def interceptor(request, handler):
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from langgraph.config import get_config
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metadata = get_config().get("metadata", {})
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headers = dict(request.headers or {})
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if token := metadata.get("auth_token"):
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headers["X-Auth-Token"] = token
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return await handler(request.override(headers=headers))
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return interceptor
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```
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- A single string value is accepted and normalized to a one-element list.
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- Invalid paths or builder failures are logged as warnings without blocking other interceptors.
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- The builder return value must be `callable`; non-callable values are skipped with a warning.
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## How It Works
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MCP servers expose tools that are automatically discovered and integrated into DeerFlow’s agent system at runtime. Once enabled, these tools become available to agents without additional code changes.
|
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## Example Capabilities
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MCP servers can provide access to:
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- **File systems**
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- **Databases** (e.g., PostgreSQL)
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- **External APIs** (e.g., GitHub, Brave Search)
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- **Browser automation** (e.g., Puppeteer)
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- **Custom MCP server implementations**
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## Learn More
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For detailed documentation about the Model Context Protocol, visit:
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https://modelcontextprotocol.io
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