Vector MCP README

vector-mcp

Action-routed MCP and agent interfaces for governed vector collection management and retrieval. The native default is epistemic-graph. Secure opt-in providers cover PostgreSQL/pgvector, Qdrant, and MongoDB Atlas.

Governed capability

• MCP tools: vector_collection_management and vector_search
• Skill provider: the consolidated vector-mcp-operations workflow
• Ontology provider: the packaged vector retrieval ontology
• Source connector provider: a read-only vector collection inventory preset
• Runtime configuration: AgentConfig, environment variables, and secret references
• Privacy posture: no checked-in endpoints, credentials, personal identity, or host paths

Install

Use the smallest extra set required by the deployment:

uvx --from 'vector-mcp[mcp]' vector-mcp

The runtime requires agent-connector-sdk>=0.2.0 and its self-contained full epistemic-graph engine contract. A bare numeric-only or partial engine profile is not a supported deployment.

For a selected storage provider:

uv add 'vector-mcp[postgres]'
uv add 'vector-mcp[qdrant]'
uv add 'vector-mcp[mongodb]'

The all extra enables every supported optional provider plus the agent, Langfuse, and Logfire runtimes. Production images should install only the providers they operate.

MCP configuration

The package includes a neutral agent-start configuration containing only the command, condensed tool mode, and tool toggles. Runtime values are inherited from AgentConfig or injected by the operator. Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in docs/index.md.

MCP

This server use dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Available MCP Tools

Auto-generated from the live MCP server — do not edit by hand.

Condensed action-routed tools (MCP_TOOL_MODE=condensed)

2 action-routed tool(s) · 0 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (intent default — the six verb-tools, granular set loaded on demand · condensed action-routed · verbose 1:1 · both). Auto-generated — do not edit.

Detailed tool schemas, parameter shapes, and validation constraints are preserved in the usage guide.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows the operator to restrict the set of exposed tools to prevent blowing up the LLM's context window.

The operator can configure tool filtering via multiple input channels:

• CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
• Environment Variables: Define standard environment variables:
  • MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
  • MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
• HTTP SSE Request Headers: Pass custom headers during transport initialization:
  • x-mcp-enabled-tools / x-mcp-disabled-tools
  • x-mcp-enabled-tags / x-mcp-disabled-tags
• HTTP SSE Request Query Parameters: Append query parameters directly to the operator's transport connection URL:
  • ?tools=tool1,tool2
  • ?tags=tag1

When query strings or parameters are provided, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

MCP Configuration Examples

Install the connector-focused [mcp] extra. Examples use vector-mcp[mcp] to add FastMCP / FastAPI through agent-connector-sdk[mcp].

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

{
  "mcpServers": {
    "vector-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "vector-mcp[mcp]",
        "vector-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "intent",
        "COLLECTION_MANAGEMENTTOOL": "True",
        "DATABASE_TYPE": "epistemic_graph",
        "DB_PASSWORD_REF": "secret://runtime/db-password",
        "DB_PORT": "5432",
        "DB_USERNAME_REF": "secret://runtime/db-username",
        "MONGODB_MAX_POOL_SIZE": "20",
        "MONGODB_REQUEST_TIMEOUT_MS": "30000",
        "MONGODB_URI_REF": "secret://runtime/mongodb-uri",
        "POSTGRES_MAX_POOL_SIZE": "20",
        "POSTGRES_REQUEST_TIMEOUT": "30",
        "QDRANT_API_KEY_REF": "secret://runtime/qdrant-api-key",
        "QDRANT_REQUEST_TIMEOUT": "30",
        "SEARCHTOOL": "True",
        "VECTOR_DB_TYPE": "epistemic_graph"
      }
    }
  }
}

Runtime references require an alias-aware launcher such as Arbora. Other launchers must omit those entries and inject the resolved values through their own runtime secret boundary.

Streamable-HTTP Transport (networked / production)

{
  "mcpServers": {
    "vector-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "vector-mcp[mcp]",
        "vector-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "127.0.0.1",
        "PORT": "8000",
        "MCP_TOOL_MODE": "intent",
        "COLLECTION_MANAGEMENTTOOL": "True",
        "DATABASE_TYPE": "epistemic_graph",
        "DB_PASSWORD_REF": "secret://runtime/db-password",
        "DB_PORT": "5432",
        "DB_USERNAME_REF": "secret://runtime/db-username",
        "MONGODB_MAX_POOL_SIZE": "20",
        "MONGODB_REQUEST_TIMEOUT_MS": "30000",
        "MONGODB_URI_REF": "secret://runtime/mongodb-uri",
        "POSTGRES_MAX_POOL_SIZE": "20",
        "POSTGRES_REQUEST_TIMEOUT": "30",
        "QDRANT_API_KEY_REF": "secret://runtime/qdrant-api-key",
        "QDRANT_REQUEST_TIMEOUT": "30",
        "SEARCHTOOL": "True",
        "VECTOR_DB_TYPE": "epistemic_graph"
      }
    }
  }
}

Alternatively, connect to a pre-deployed Streamable-HTTP instance by url:

{
  "mcpServers": {
    "vector-mcp": {
      "url": "http://localhost:8000/vector-mcp/mcp"
    }
  }
}

Run a reviewed container image as a least-privilege stdio child (no listener or published port):

docker run -i --rm \
  --read-only \
  --cap-drop=ALL \
  --security-opt=no-new-privileges \
  --pids-limit=256 \
  --tmpfs /tmp:rw,noexec,nosuid,nodev,size=64m \
  -e TRANSPORT=stdio \
  -e MCP_TOOL_MODE=intent \
  -e COLLECTION_MANAGEMENTTOOL=True \
  -e DATABASE_TYPE=epistemic_graph \
  -e DB_PASSWORD_REF \
  -e DB_PORT=5432 \
  -e DB_USERNAME_REF \
  -e MONGODB_MAX_POOL_SIZE=20 \
  -e MONGODB_REQUEST_TIMEOUT_MS=30000 \
  -e MONGODB_URI_REF \
  -e POSTGRES_MAX_POOL_SIZE=20 \
  -e POSTGRES_REQUEST_TIMEOUT=30 \
  -e QDRANT_API_KEY_REF \
  -e QDRANT_REQUEST_TIMEOUT=30 \
  -e SEARCHTOOL=True \
  -e VECTOR_DB_TYPE=epistemic_graph \
  registry.example.invalid/vector-mcp@sha256:<digest> vector-mcp

For containerized network HTTP, supply an authenticated TLS ingress (or direct server TLS), exact MCP_ALLOWED_HOSTS, and an exact trusted-proxy CIDR policy through the operator-owned deployment profile. The generator does not emit an unauthenticated non-loopback listener.

Auto-generated from the code-read env surface (MCP_TOOL_MODE + package vars) — do not edit.

Additional Deployment Options

vector-mcp can also run as a local container (Docker / Podman / uv) or be consumed from a remote deployment. The Deployment guide has full, copy-paste mcp_config.json for all four transports — stdio, streamable-http, local container / uv, and remote URL:

• Local container / uv — launch the server from mcp_config.json via uvx, docker run, or podman run, or point at a local streamable-http container by url.
• Remote URL — connect to a server deployed behind Caddy at https://vector-mcp.example.invalid/mcp using the "url" key.

Environment Variables

Package environment variables

Inherited agent-utilities variables (apply to every connector)

25 package + 20 inherited variable(s). Auto-generated from .env.example + the shared agent-utilities set — do not edit.

Every variable the server reads, grouped by purpose. See .env.example for the canonical, copy-paste list — including the DATABASE_TYPE / GRAPH_SERVICE_SOCKET / GRAPH_SERVICE_AUTH_SECRET connection settings for the native epistemic-graph backend. Backend endpoints, database locations, and credentials for opt-in providers (Postgres/Qdrant/Mongo/ Chroma/Couchbase) are never README-documented literal values or MCP tool arguments — they resolve through AgentConfig and secret:///env:///vault:// references at runtime.

MCP server / transport

Tool toggles

Each action-routed tool can be disabled individually via its toggle env var (set to false). The full list is in the Available MCP Tools table above.

Telemetry & governance

See .env.example for a copy-paste starting point.

Provider and ontology integration

The package contributes its skills, prompts, ontology, and source connector through Python entry points. The collection-inventory connector is intentionally read-only and registers collection metadata, not document or embedding payloads.

Generated connector signatures must be recreated only after the installed MCP schema is observed and a release signing key is provided at runtime. A signature from an older tool schema or ontology must never be copied forward.

Development checks

Low-cost checks that do not start providers:

python -m compileall -q vector_mcp

Provider tests use mocked SDK boundaries and make no network calls. Live qualification is a separate deployment gate and must use operator-provided AgentConfig and secrets.

Documentation

• Installation
• Configuration and privacy
• Deployment
• Usage
• Architecture overview

The slim :mcp streamable-http container (docker/mcp.compose.yml) publishes 0.0.0.0:8000 with a /health check; see Deployment for the full compose service definition.

License

See LICENSE.

Deploy with agent-utilities-deployment

Provision this package with the consolidated agent-utilities-deployment workflow. It selects an installed-package, editable-source, or immutable-container path; records only runtime secret and TLS-profile references in AgentConfig; and runs doctor, registration, policy, observability, and rollback gates. Ask your agent to "deploy vector-mcp with agent-utilities-deployment".

The repository embeds no deployment profile, credential value, certificate path, or environment-specific endpoint. Supply those at runtime through AgentConfig and the configured secret provider.

Installation

Pick the extra that matches what the operator want to run:

# MCP server only (recommended for tool hosting — slim deps)
uv pip install "vector-mcp[mcp]"

# Full agent runtime (Pydantic AI + epistemic-graph engine)
uv pip install "vector-mcp[mcp]"

# Everything (development)
uv pip install "vector-mcp[all]"      # or: python -m pip install "vector-mcp[all]"

Container image

The shared connector pipeline renders a slim :mcp image (vector-mcp[mcp], entrypoint vector-mcp) from pyproject.toml at release time:

pipelines-connector render --root .
docker build -t knucklessg1/vector-mcp:mcp docker/

The render command creates the release-owned docker/mcp.compose.yml; it runs the image with the configured host publication.

Knowledge-graph database (epistemic-graph)

Graph storage, vector search and queries are served by epistemic-graph, run as its own database container. Deployment recipes (single-node + Raft HA), connection config, and the database architecture are in the epistemic-graph deployment guide.

Repository Owners

Contribute

Contributions are welcome! Please ensure code quality by running local checks before submitting pull requests:

• Format code using ruff format .
• Lint code using ruff check .
• Validate type-safety with mypy .
• Run test suites using pytest