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.
   ```bash
   # 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:

```json
{
  "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:

```yaml
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.

## Parallel Search (optional)

The `parallel-search` entry in `extensions_config.example.json` is disabled by
default. To opt in, copy that entry into `mcpServers` in your root
`extensions_config.json`, set `"enabled": true`, and restart DeerFlow. It connects
to `https://search.parallel.ai/mcp` over HTTP and adds Parallel's search and fetch
tools. With DeerFlow's default tool-name prefix, the agent sees
`parallel-search_web_search` and `parallel-search_web_fetch`. Existing search
providers and defaults stay unchanged.

This is a third-party service operated by Parallel.ai. Search calls send
objectives and queries to Parallel; fetch calls send requested page URLs and
any extraction objective. These inputs can contain information from your
conversation, so enable it only if you are comfortable sending that data to
Parallel.

Access is anonymous by default: no API key or authentication headers are needed.
For higher rate limits, optionally add this `headers` field to the
`parallel-search` entry in your local `extensions_config.json`:

```json
{
  "headers": {
    "Authorization": "$PARALLEL_AUTHORIZATION"
  }
}
```

Set `PARALLEL_AUTHORIZATION` in the DeerFlow backend's environment to the full
value `Bearer <your-parallel-api-key>`, then restart DeerFlow. Include `Bearer `
in the environment variable because DeerFlow expands only whole-string
`$ENV_VAR` references, not `Bearer $ENV_VAR`. Keep the actual key out of committed
files. Remove the `headers` field and restart DeerFlow to return to anonymous
access. See the
[Parallel Search MCP documentation](https://docs.parallel.ai/integrations/mcp/search-mcp)
for details.

## 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.

```json
{
   "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:

```json
{
  "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:

```json
{
   "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`:

```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:

```json
{
  "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. `headers_from_context` follows the same
rule: submit is awaited inside the Agent run and carries the mapped headers,
while status and cancel polls skip them and authenticate with the server's
static or OAuth credentials — so `on_missing: "deny"` guards the submit but not
those polls. Declaring both on one server logs a warning at startup. 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:

```json
{
   "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
         }
      }
   }
}
```

## Request-Scoped Headers (HTTP/SSE MCP Servers)

When the credential is chosen by the *caller* rather than by the operator —
multi-tenant gateways, per-run API keys, one shared MCP server fronting several
environments — declare a `headers_from_context` block instead of registering one
MCP server per credential.

Each entry maps an HTTP header name to a key of the run request's
`config.context.secrets` carrier:

```json
{
   "mcpServers": {
      "shared-api": {
         "enabled": true,
         "type": "http",
         "url": "https://mcp.example.com/mcp",
         "headers": { "Authorization": "Bearer $MCP_DISCOVERY_TOKEN" },
         "headers_from_context": {
            "enabled": true,
            "headers": {
               "X-Tenant-Id": "tenant_id",
               "Authorization": "tenant_token"
            },
            "on_missing": "deny"
         }
      }
   }
}
```

The caller supplies the values on each run request:

```json
{
  "config": {
    "context": {
      "secrets": {
        "tenant_id": "acme",
        "tenant_token": "Bearer <request-scoped credential>"
      }
    }
  }
}
```

- The config file stores **names only**, never a credential, so the block is
  returned unmasked by `GET /api/mcp/config`. The values travel out-of-band with
  each run and are stripped from persisted run configuration, API responses, and
  trace payloads.
- The server's static `headers` are used for startup tool discovery. A mapped
  header replaces the static one for that tool call, as shown above for
  `Authorization`. Header names are matched case-insensitively, so a mapped
  `Authorization` still replaces a static `authorization` instead of putting a
  second copy of the field on the wire. Mapping one header under two spellings
  is rejected at config load.
- `on_missing` defaults to `"deny"`: if the run carries no value for a mapped
  key, the tool call fails with an actionable error rather than falling back to
  the discovery credential — which in a multi-tenant deployment would send one
  tenant's request under another tenant's authority. Set `"passthrough"` to opt
  out and forward the static headers instead.
- A value that cannot be sent as an HTTP header — a stray newline picked up
  when reading a token from a file, leading/trailing whitespace, characters
  outside ASCII — is always denied, regardless of `on_missing`. The error
  names the offending key but never repeats the value; without this check a
  newline or stray whitespace would reach h11, whose rejection echoes the full
  credential into a model-visible tool error. The same check covers every other
  way a value reaches these headers: `user_auth`, the OAuth token returned by
  the token endpoint, and the static `headers` in the config file.
- Precedence for a server declaring several sources: static `headers` <
  `oauth` < `user_auth` < `headers_from_context`. The value chosen for this one
  request is the most specific, so it wins.
- `sse`/`http` only. A stdio server has no HTTP headers; declaring the block
  there logs a warning and is ignored.
- Durable background tasks are the one exception, and only half of one: a
  `task_toolsets` submit is awaited inside the Agent run and carries these
  headers, but the status and cancel polls run after that run ends, so they skip
  them and use the server's static/OAuth credentials. See *Durable Background
  Tasks* above.

Use `user_auth` instead when the credential belongs to a configured DeerFlow
user rather than to the individual request.

## 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:

```json
{
  "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. For a plain header mapping prefer the declarative
`headers_from_context` block above; write an interceptor when the header value
needs logic (signing, exchanging the secret for another token, routing on the
tool name):

```python
from deerflow.runtime.secret_context import extract_request_secrets


def build_auth_interceptor():
    async def interceptor(request, handler):
        runtime = getattr(request, "runtime", None)
        secrets = extract_request_secrets(getattr(runtime, "context", None))
        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
```

Read the run context from `request.runtime`, not from
`langgraph.config.get_config()`. The context is carried on the LangGraph
runtime, not on the `RunnableConfig` propagated to child runnables, so
`get_config().get("context")` is `None` inside a tool call. LangGraph's tool
node injects the runtime into any tool parameter named `runtime`, which is how
both the pooled stdio wrapper and `langchain-mcp-adapters`' HTTP/SSE tool
receive it. When the call originates outside a tool node, fall back to
`langgraph.runtime.get_runtime()` (see
`deerflow/mcp/context_headers.py::_current_runtime`).

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

```json
{
  "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