deer-flow/backend/docs/MCP_SERVER.md

MCP (Model Context Protocol) Configuration

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.

Setup

1. Copy extensions_config.example.json to extensions_config.json in the project root directory.

   # Copy example configuration
   cp extensions_config.example.json extensions_config.json
   

2. Enable the desired MCP servers or skills by setting "enabled": true.

3. Configure each server’s command, arguments, and environment variables as needed.

4. Restart the application to load and register MCP tools.

OpenViking MCP Tools

OpenViking's official server exposes a Streamable HTTP MCP endpoint at /mcp. DeerFlow connects to it through the same generic MCP client used for other HTTP servers:

{
  "mcpServers": {
    "openviking": {
      "enabled": true,
      "type": "http",
      "url": "http://127.0.0.1:1933/mcp",
      "headers": {
        "X-API-Key": "$OPENVIKING_API_KEY"
      }
    }
  }
}

Set OPENVIKING_API_KEY to a normal owner-bound OpenViking USER API key. The key determines the OpenViking account and user. Do not use a root/admin key, trusted mode, or add X-OpenViking-Account, X-OpenViking-User, or X-OpenViking-Actor-Peer headers for this personal single-owner setup. X-API-Key is used here because DeerFlow expands a whole-string $ENV_VAR value without storing a credential in the checked-in configuration. If OPENVIKING_API_KEY is missing or empty during initialization, OpenViking authentication fails and DeerFlow skips that MCP server, so no OpenViking tools appear. Changing only the environment variable does not invalidate DeerFlow's already-populated, file-signature-based MCP tool cache; after setting or fixing the key, restart DeerFlow, modify and re-save the extensions config, or call the MCP cache-reset endpoint at POST /api/mcp/cache/reset.

OpenViking owns the tool schemas and behavior. DeerFlow performs the standard MCP initialization and discovery flow, prefixes the discovered names with openviking_ by default, and routes calls back through the generic MCP client. For capability parity with other official OpenViking harnesses, DeerFlow exposes the native forget tool with the other discovered tools. forget permanently deletes a viking:// URI and should be called only after explicit user confirmation; DeerFlow does not enforce that confirmation.

Operators who do not want agents to call forget can block its default visible name with DeerFlow's existing guardrail configuration:

guardrails:
  enabled: true
  provider:
    use: deerflow.guardrails.builtin:AllowlistProvider
    config:
      denied_tools: ["openviking_forget"]

If tool_name_prefix is disabled for the OpenViking server, block forget instead.

This explicit tool path is separate from the automatic OpenViking memory backend configured under config.yaml -> memory. Both may be enabled at the same time: the memory backend handles automatic turn capture and recall, while MCP tools are model-selected operations.

For Docker, point url at the OpenViking address reachable from the Gateway container, such as http://openviking:1933/mcp for a shared Compose network or http://host.docker.internal:1933/mcp for a host-installed server.

Routing Hints

Use routing when an MCP server should be preferred for specific requests, such as internal database questions that should use a PostgreSQL MCP tool before web search. Routing hints are soft model guidance: they add a <mcp_routing_hints> prompt section, but they do not forbid other tools. Use agent-level allow/deny policy for hard restrictions. If tool_search.enabled defers MCP tool schemas, matching routing metadata can also auto-promote the deferred schema before the model call. Auto-promotion is controlled by the top-level config.yaml -> tool_search.auto_promote_top_k setting.

{
   "mcpServers": {
      "postgres": {
         "enabled": true,
         "type": "stdio",
         "command": "npx",
         "args": ["-y", "@modelcontextprotocol/server-postgres", "postgresql://localhost/mydb"],
         "routing": {
            "mode": "prefer",
            "priority": 50,
            "keywords": ["orders", "users", "SQL", "database", "table"]
         },
         "tools": {
            "query": {
               "routing": {
                  "mode": "prefer",
                  "priority": 100,
                  "keywords": ["query database", "orders table", "metrics"]
               }
            }
         }
      }
   }
}

• routing.mode: off disables hints; prefer emits hints.
• routing.priority: 0 to 100; higher-priority hints are rendered first. When tool_search.enabled=true, priority also orders auto-promote matches.
• routing.keywords: operator-authored terms that describe when to prefer the MCP tool. Empty keywords are allowed but do not emit a hint line and do not trigger auto-promotion. Auto-promote matching is a case-insensitive substring test against the latest user message (not token/word-boundary matching), so prefer distinctive keywords — a short term like api also matches rapid. Over-matching only exposes an extra tool schema (soft/additive), never disables other tools.
• tools.<original_tool_name>.routing: overrides only the fields explicitly set for that tool. The key is the MCP server's original tool name, before the <server>_ prefix added for model binding. If the server-level routing.mode is off, a tool override must set mode: "prefer"; setting only priority or keywords still inherits off and emits no hint.
• tool_search.auto_promote_top_k: global limit for auto-promoted deferred MCP schemas per model call. Default 3; valid range 1..5.

Tool Name Prefixes

DeerFlow prefixes discovered MCP tool names with <server_name>_ by default. This avoids collisions when two enabled servers expose tools with the same name. A server that already namespaces its own tools can opt out:

{
  "mcpServers": {
    "semantic-scholar": {
      "type": "stdio",
      "command": "uvx",
      "args": ["s2-mcp-server"],
      "tool_name_prefix": false
    }
  }
}

With this setting, a server tool named semantic_scholar_search_papers keeps that name instead of becoming semantic-scholar_semantic_scholar_search_papers. The default is true for backward compatibility. Disable it only when every resulting tool name remains unique across the enabled servers. Stdio tools continue to use DeerFlow's persistent per-thread session pool regardless of this setting.

Server Timeouts

Two independent settings bound stdio MCP servers and durable HTTP/SSE task calls. session_init_timeout covers server bring-up — tool discovery (subprocess spawn + initialize + tools/list) and persistent-session initialization — plus ephemeral HTTP/SSE task-session initialization. It defaults to 60s so a hung server (e.g. npx blocked on a package download, or a server that never answers initialize) cannot block agent construction or the task poller indefinitely. Set it to null to disable:

{
   "mcpServers": {
      "github": {
         "enabled": true,
         "type": "stdio",
         "command": "npx",
         "args": ["-y", "@modelcontextprotocol/server-github"],
         "env": {
            "GITHUB_TOKEN": "$GITHUB_TOKEN"
         },
         "session_init_timeout": 60,
         "tool_call_timeout": 60
      }
   }
}

tool_call_timeout limits each individual stdio tool call in seconds. Ordinary durable-task submit/status/cancel calls also honor it for http and sse servers, independently of transport idle timeouts, so a live connection that never returns the matching MCP response cannot stall the task poller. Other http and sse tools continue to use transport-level timeouts.

Filesystem MCP Servers

DeerFlow already provides built-in file tools for thread-scoped workspace access. Do not add an MCP filesystem server for the same DeerFlow workspace. The overlapping file tools use different path semantics, which can make LLM tool selection and file access behavior unstable.

DeerFlow does not currently adapt the MCP Roots mode for filesystem servers. In particular, it does not publish per-thread MCP roots or map DeerFlow sandbox paths such as /mnt/user-data/... to paths accepted by @modelcontextprotocol/server-filesystem. Use DeerFlow's built-in file tools for DeerFlow workspace files.

Durable Background Tasks with Ordinary MCP Tools

An MCP server can expose a fast submit tool plus status and cancel tools for long-running work. DeerFlow keeps the remote task ID in SQL and polls it outside the Agent run, so the model does not have to remember or repeatedly send that ID.

Enable the restart-required runtime in config.yaml:

mcp_tasks:
  enabled: true
  poll_interval_seconds: 5
  lease_seconds: 120
  max_concurrent_polls: 8

Then bind exact remote tool names in extensions_config.json. These names are the server's raw names, before DeerFlow adds any <server_name>_ prefix:

{
  "mcpServers": {
    "report-service": {
      "enabled": true,
      "type": "http",
      "url": "https://reports.example.com/mcp",
      "session_init_timeout": 60,
      "tool_call_timeout": 60,
      "task_toolsets": [
        {
          "name": "report-generation",
          "submit_tool": "submit_report",
          "status_tool": "get_report_status",
          "cancel_tool": "cancel_report"
        }
      ]
    }
  }
}

The three remote tools must use MCP structuredContent; ordinary text blocks are never parsed as a task protocol:

• submit_report(<business arguments>) returns {"task_id":"remote-123","status":"running"} quickly.
• get_report_status({"task_id":"remote-123"}) returns a status from running, input_required, completed, failed, or cancelled. It may also return result, result_artifact (uri plus mime_type), error, error_code, input_required, and a finite positive poll_after_seconds. DeerFlow caps that remote scheduling hint at 24 hours.
• cancel_report({"task_id":"remote-123"}) is idempotent and returns the actual terminal status: cancelled, completed, or failed.

For the status tool, isError: true means that the status call itself failed; DeerFlow records a bounded snippet of its first text content block and retries with capped exponential backoff. It does not infer that the remote task failed, because MCP tool errors do not distinguish transient from permanent conditions. A server must report a permanent remote-task failure through a normal tool result (isError: false or omitted) whose structuredContent contains status: "failed" and an optional error. This distinction lets a temporary server or network outage recover without terminalizing work that may still be running remotely.

Persisted task errors are capped at 4,000 characters. An input_required payload must be valid JSON no larger than 64 KiB; an oversized or invalid payload is treated as a permanent protocol failure instead of being truncated into a different question. result_artifact must likewise serialize as JSON within 64 KiB; it is a small external reference, not a second result channel. Remote task IDs and task names are limited to 255 characters, and a task-enabled server name is limited to 128 characters, matching the durable SQL schema on both SQLite and PostgreSQL.

error_code: "task_not_found" is a permanent failure. Network and transport errors remain retryable with capped exponential backoff; the query API reports tracking_degraded after repeated failures. Oversized JSON results are not cut into invalid JSON: DeerFlow stores a text preview, marks result_truncated, and preserves any external result_artifact reference.

Only submit remains in the Agent's normal tool list. Status and cancel are runtime-internal. Query the current thread through:

• GET /api/threads/{thread_id}/mcp-tasks
• GET /api/threads/{thread_id}/mcp-tasks/{task_id}

Task toolsets require database.backend: sqlite or postgres; startup fails instead of falling back to a synchronous submit when persistence or the task runtime is disabled. Restart recovery also requires the remote service to keep the task alive and recognize its ID after DeerFlow reconnects. A stdio server must therefore persist its own tasks; multi-instance deployments should normally use an independently running HTTP/SSE service.

Server-level OAuth works during background polling and refreshes normally. Request-scoped secrets from a particular Agent run are not durable task credentials and are unavailable to later background polls; use server-level authentication for a task toolset. Restart DeerFlow after changing mcp_tasks, task_toolsets, mcpInterceptors, or any connection, authentication, transport, or timeout setting on a task-enabled server. DeerFlow rejects task-tool reloads that no longer match the Gateway's startup snapshot instead of discovering tools with new settings while the background poller still calls the old endpoint. Agent-facing description/routing changes and changes to servers without task toolsets remain hot-reloadable.

OAuth Support (HTTP/SSE MCP Servers)

For http and sse MCP servers, DeerFlow supports OAuth token acquisition and automatic token refresh.

• Supported grants: client_credentials, refresh_token
• Configure per-server oauth block in extensions_config.json
• Secrets should be provided via environment variables (for example: $MCP_OAUTH_CLIENT_SECRET)

Example:

{
   "mcpServers": {
      "secure-http-server": {
         "enabled": true,
         "type": "http",
         "url": "https://api.example.com/mcp",
         "oauth": {
            "enabled": true,
            "token_url": "https://auth.example.com/oauth/token",
            "grant_type": "client_credentials",
            "client_id": "$MCP_OAUTH_CLIENT_ID",
            "client_secret": "$MCP_OAUTH_CLIENT_SECRET",
            "scope": "mcp.read",
            "refresh_skew_seconds": 60
         }
      }
   }
}

Custom Tool Interceptors

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.

Declare interceptors in extensions_config.json using the mcpInterceptors field:

{
  "mcpInterceptors": [
    "my_package.mcp.auth:build_auth_interceptor"
  ],
  "mcpServers": { ... }
}

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.

Example interceptor that injects an authorization header from the request-scoped LangGraph secret context:

from langgraph.config import get_config


def build_auth_interceptor():
    async def interceptor(request, handler):
        config = get_config()
        secrets = (config.get("context") or {}).get("secrets") or {}
        token = secrets.get("MCP_AUTH_TOKEN")
        if token:
            request = request.override(
                headers={**(request.headers or {}), "Authorization": f"Bearer {token}"}
            )
        return await handler(request)

    return interceptor

Supply the credential on each run request through config.context.secrets:

{
  "metadata": {"source": "my-client"},
  "config": {
    "context": {
      "secrets": {"MCP_AUTH_TOKEN": "<request-scoped credential>"}
    }
  }
}

Both metadata.auth_token and config.metadata.auth_token are rejected with HTTP 422 at run admission and are never supported interceptor paths. Do not put credentials in either metadata surface; use config.context.secrets, whose values remain available to the live interceptor but are removed from persisted and API-visible run configuration copies.

• A single string value is accepted and normalized to a one-element list.
• Invalid paths or builder failures are logged as warnings without blocking other interceptors.
• The builder return value must be callable; non-callable values are skipped with a warning.

Migrating legacy MCP credentials

Deployments that previously sent metadata.auth_token or config.metadata.auth_token must:

1. Update the caller and interceptor to use config.context.secrets as shown above.
2. Rotate the exposed credential before resuming authenticated MCP traffic.
3. Locate and remove every retained legacy copy according to the deployment's retention policy, including database rows, run events, application or proxy logs, snapshots, exports, and backups.

Current history APIs hide legacy metadata.auth_token and config.metadata.auth_token values, but hiding a response does not erase material already retained by those systems. Restarting or upgrading DeerFlow does not rotate credentials or perform historical cleanup; operators must complete both actions explicitly.

How It Works

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.

Example Capabilities

MCP servers can provide access to:

• Databases (e.g., PostgreSQL)
• External APIs (e.g., GitHub, Brave Search)
• Browser automation (e.g., Puppeteer)
• Custom MCP server implementations

Learn More

For detailed documentation about the Model Context Protocol, visit:
https://modelcontextprotocol.io