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.

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.

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.

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.

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

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.

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.

Repository Owners

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.

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
.security		.security
.specify/specs		.specify/specs
docker		docker
docs		docs
scripts		scripts
tests		tests
vector_mcp		vector_mcp
.bumpversion.cfg		.bumpversion.cfg
.codespellignore		.codespellignore
.dockerignore		.dockerignore
.env.example		.env.example
.gitattributes		.gitattributes
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
connector_manifest.yml		connector_manifest.yml
mcp_config.json		mcp_config.json
mkdocs.yml		mkdocs.yml
opencode.json		opencode.json
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
requirements.txt		requirements.txt
uv.lock		uv.lock

Navigation Menu

Uh oh!

Repository files navigation

vector-mcp

Governed capability

Install

MCP configuration

MCP

Available MCP Tools

Condensed action-routed tools (`MCP_TOOL_MODE=condensed`)

Dynamic Tool Selection & Visibility

MCP Configuration Examples

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

Streamable-HTTP Transport (networked / production)

Additional Deployment Options

Environment Variables

Package environment variables

Inherited agent-utilities variables (apply to every connector)

MCP server / transport

Tool toggles

Telemetry & governance

Agent CLI (full `[agent]` runtime only)

Provider and ontology integration

Development checks

Documentation

License

Deploy with `agent-utilities-deployment`

Installation

Container images (`:mcp` vs `:agent`)

Knowledge-graph database (`epistemic-graph`)

Repository Owners

Contribute

Deploy with `agent-os-genesis`

About

Releases

Packages

Contributors

Languages

Uh oh!

Latest commit

History

Folders and files

Repository files navigation

vector-mcp

Governed capability

Install

MCP configuration

MCP

Available MCP Tools

Condensed action-routed tools (MCP_TOOL_MODE=condensed)

Dynamic Tool Selection & Visibility

MCP Configuration Examples

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

Streamable-HTTP Transport (networked / production)

Additional Deployment Options

Environment Variables

Package environment variables

Inherited agent-utilities variables (apply to every connector)

MCP server / transport

Tool toggles

Telemetry & governance

Agent CLI (full [agent] runtime only)

Provider and ontology integration

Development checks

Documentation

License

Deploy with agent-utilities-deployment

Installation

Container images (:mcp vs :agent)

Knowledge-graph database (epistemic-graph)

Repository Owners

Contribute

Deploy with agent-os-genesis

About

Topics

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

Condensed action-routed tools (`MCP_TOOL_MODE=condensed`)

Agent CLI (full `[agent]` runtime only)

Deploy with `agent-utilities-deployment`

Container images (`:mcp` vs `:agent`)

Knowledge-graph database (`epistemic-graph`)

Deploy with `agent-os-genesis`