Vector

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.

Version: 3.1.0

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-utilities>=2.0.0 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-launch 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.

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MCP

This server utilizes 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)

MCP Tool	Toggle Env Var	Description
vector_collection_management	COLLECTION_MANAGEMENTTOOL	Manage collection management operations.
vector_search	SEARCHTOOL	Manage search operations.

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 you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You 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 your transport connection URL:
  • ?tools=tool1,tool2
  • ?tags=tag1

When query strings or parameters are supplied, 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.

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MCP Configuration Examples

Install the connector-focused [mcp] extra. Examples use vector-mcp[mcp] to add FastMCP / FastAPI through agent-utilities[mcp]; the required Agent Utilities core still carries epistemic-graph[full]. The [agent-runtime] extra additionally enables model orchestration.

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",
        "LLM_SSL_VERIFY": "False",
        "SEARCHTOOL": "True",
        "VECTOR_DB_TYPE": "epistemic_graph"
      }
    }
  }
}

Runtime references require an alias-aware launcher such as GraphOS. 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",
        "LLM_SSL_VERIFY": "False",
        "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 LLM_SSL_VERIFY=False \
  -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.

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Environment Variables

Package environment variables

Variable	Example	Description
HOST	127.0.0.1
PORT	8000
TRANSPORT	stdio	options: stdio, streamable-http, sse
ENABLE_OTEL	—
EMBEDDING_TLS_PROFILE_REF	secret://runtime/embedding-tls-profile	Configure AgentConfig EMBEDDING_MODELS and its referenced runtime credentials.
LLM_BASE_URL	http://localhost:8000/v1	embedding/LLM API base url
LLM_TOKEN	secret-injected	bearer token for the embedding/LLM endpoint
LLM_API_KEY	secret-injected	alias accepted if LLM_TOKEN is unset
LLM_SSL_VERIFY	False	verify TLS for the embedding/LLM endpoint
DOCUMENT_DIRECTORY	—	Required only for filesystem ingestion. Supply the operator-owned root at runtime.
DATABASE_TYPE	epistemic_graph	Backend used when db_type is unspecified. Default is the native epistemic-graph engine (local, zero-infra, durable). Options: epistemic_graph, postgres, mongodb, qdrant. DATABASE_TYPE is the canonical variable; VECTOR_DB_TYPE is accepted as an alias for backward compatibility.
VECTOR_DB_TYPE	epistemic_graph
DB_HOST	—	postgres/qdrant host
DBNAME	—	postgres/mongodb database name
DB_PORT	5432
DB_USERNAME_REF	secret://runtime/db-username
DB_PASSWORD_REF	secret://runtime/db-password
MONGODB_URI_REF	secret://runtime/mongodb-uri
QDRANT_API_KEY_REF	secret://runtime/qdrant-api-key
QDRANT_HTTP_ALLOWED_PRIVATE_HOSTS	—	comma-separated SSRF allowlist for a private Qdrant host
COLLECTION_MANAGEMENTTOOL	True
SEARCHTOOL	True
TEST_POSTGRES_CONNECTION_STRING	postgresql://postgres:password@localhost:5432/vectordb
TEST_MONGODB_HOST	localhost
TEST_MONGODB_PORT	27017
TEST_MONGODB_DB	vectordb
TEST_QDRANT_LOCATION	http://localhost:6333
TEST_COUCHBASE_CONNECTION	couchbase://localhost
TEST_COUCHBASE_USER	Administrator
TEST_COUCHBASE_PASSWORD	secret-injected
TEST_COUCHBASE_DB	vector_db

Inherited agent-utilities variables (apply to every connector)

Variable	Example	Description
MCP_TOOL_MODE	intent	Tool surface: intent | condensed | verbose | both
MCP_ENABLED_TOOLS	—	Comma-separated tool allow-list
MCP_DISABLED_TOOLS	—	Comma-separated tool deny-list
MCP_ENABLED_TAGS	—	Comma-separated tag allow-list
MCP_DISABLED_TAGS	—	Comma-separated tag deny-list
EUNOMIA_TYPE	none	Authorization mode: none | embedded | remote
EUNOMIA_POLICY_FILE	mcp_policies.json	Embedded Eunomia policy file
EUNOMIA_REMOTE_URL	—	Remote Eunomia authorization server URL
OTEL_EXPORTER_OTLP_ENDPOINT	—	OTLP collector endpoint
MCP_CLIENT_AUTH	—	Outbound MCP child auth: oidc-client-credentials | basic | none
OIDC_CLIENT_ID	—	OIDC client id (service-account auth)
OIDC_CLIENT_SECRET_REF	secret://identity/oidc-client-secret	Runtime secret reference for the OIDC service account
MCP_BASIC_AUTH_USERNAME	—	HTTP Basic username (MCP_CLIENT_AUTH=basic)
MCP_BASIC_AUTH_PASSWORD_REF	secret://identity/mcp-basic-password	Runtime secret reference for HTTP Basic auth (MCP_CLIENT_AUTH=basic)
DEBUG	False	Verbose logging
PYTHONUNBUFFERED	1	Unbuffered stdout (recommended in containers)
MCP_URL	http://localhost:8000/mcp	URL of the MCP server the agent connects to
PROVIDER	openai	LLM provider for the agent
MODEL_ID	gpt-4o	Model id for the agent
ENABLE_WEB_UI	True	Serve the AG-UI web interface

31 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

Variable	Description	Default
TRANSPORT	stdio, streamable-http, or sse	stdio
HOST	Bind host (HTTP transports)	0.0.0.0
PORT	Bind port (HTTP transports)	8000
MCP_TOOL_MODE	Tool surface: condensed, verbose, or both	condensed
MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS	Comma-separated tool allow/deny list	—
MCP_ENABLED_TAGS / MCP_DISABLED_TAGS	Comma-separated tag allow/deny list	—
PYTHONUNBUFFERED	Unbuffered stdout (recommended in containers)	1

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.

Variable	Description	Default
COLLECTION_MANAGEMENTTOOL	Enable the collection-management tool	True
SEARCHTOOL	Enable the search tool	True

Telemetry & governance

Variable	Description	Default
ENABLE_OTEL	Enable OpenTelemetry export	True
OTEL_EXPORTER_OTLP_ENDPOINT	OTLP collector endpoint	—
OTEL_EXPORTER_OTLP_PUBLIC_KEY / OTEL_EXPORTER_OTLP_SECRET_KEY	OTLP auth keys	—
OTEL_EXPORTER_OTLP_PROTOCOL	OTLP protocol (e.g. http/protobuf)	—
EUNOMIA_TYPE	Authorization mode: none, embedded, remote	none
EUNOMIA_POLICY_FILE	Embedded policy file	mcp_policies.json
EUNOMIA_REMOTE_URL	Remote Eunomia server URL	—

Agent CLI (full [agent] runtime only)

Variable	Description	Default
MCP_URL	URL of the MCP server the agent connects to	http://localhost:8000/mcp
PROVIDER	LLM provider (e.g. openai)	openai
MODEL_ID	Model id (e.g. gpt-4o)	gpt-4o
ENABLE_WEB_UI	Serve the AG-UI web interface	True

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 launch providers:

python scripts/security_sanitizer.py
python scripts/security_contract.py --contract .security/security-contract.json validate
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-supplied AgentConfig and secrets.

Documentation

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

The slim :mcp streamable-http container (docker/mcp.compose.yml) publishes :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".

Install mode	Command
Installed package	uv tool install "vector-mcp[mcp]", then run vector-mcp
Editable source	uv pip install -e ".[agent]", then run vector-mcp
Immutable container	deploy registry.example.invalid/vector-mcp@sha256:<digest> through the operator-selected orchestrator

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.

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Installation

Pick the extra that matches what you want to run:

Extra	Installs	Use when
vector-mcp[mcp]	Slim MCP server only (agent-utilities[mcp] — FastMCP/FastAPI)	You only run the MCP server (smallest install / image)
vector-mcp[agent]	Full agent runtime (agent-utilities[agent,logfire] — Pydantic AI + the epistemic-graph engine)	You run the integrated agent
vector-mcp[all]	Everything (mcp + all vector backends + agent)	Development / both surfaces

# 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[agent]"

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

Container images (:mcp vs :agent)

One multi-stage docker/Dockerfile builds two right-sized images, selected by --target:

Image tag	Build target	Contents	Entrypoint
knucklessg1/vector-mcp:mcp	--target mcp	vector-mcp[mcp] — slim, no engine/pydantic-ai/dspy/llama-index/tree-sitter	vector-mcp
knucklessg1/vector-mcp:latest	--target agent (default)	vector-mcp[agent] — full agent runtime + epistemic-graph engine	vector-agent

docker build --target mcp   -t knucklessg1/vector-mcp:mcp    docker/   # slim MCP server
docker build --target agent -t knucklessg1/vector-mcp:latest docker/   # full agent

docker/mcp.compose.yml runs the slim :mcp server; docker/agent.compose.yml runs the agent (:latest) with a co-located :mcp sidecar.

Knowledge-graph database (epistemic-graph)

The full agent ([agent] / :latest) embeds the epistemic-graph engine (pulled in transitively via agent-utilities[agent]). For production — or to share one knowledge graph across multiple agents — run epistemic-graph as its own database container and point the agent at it instead of embedding it. Deployment recipes (single-node + Raft HA), connection config, and the full database architecture (with diagrams) are documented in the epistemic-graph deployment guide. The slim [mcp] server does not require the database.

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Repository Owners

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Contribute

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

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

Deploy with agent-os-genesis

This package can be provisioned for you — skill-guided — by the agent-os-genesis universal skill (its single-package deploy mode): it picks your install method, seeds secrets to OpenBao/Vault (or .env), trusts your enterprise CA, registers the MCP server, and verifies it — the same machinery that stands up the whole Agent OS, narrowed to just this package. Ask your agent to "deploy vector-mcp with agent-os-genesis".

Install mode	Command
Bare-metal, prod (PyPI)	uvx vector-mcp · or uv tool install vector-mcp
Bare-metal, dev (editable)	uv pip install -e ".[all]" · or pip install -e ".[all]"
Container, prod	deploy knucklessg1/vector-mcp:latest via docker-compose / swarm / podman / podman-compose / kubernetes
Container, dev (editable)	deploy docker/compose.dev.yml (source-mounted at /src; edits live on restart)

Secrets are read-existing + seeded via vault_sync — you are only prompted for what's missing.