TinyContext MCP Server

TinyContext

Context that fits your local LLMs.

TinyContext is a token-light local memory layer for AI agents. It stores concise memories and their embeddings in SQLite, ranks them with hybrid BM25 and dense retrieval, and returns only the context that fits the requested token budget.

No hosted account. No giant context dumps. No required vector database.

Choose a tier

The Python library contains the memory engine. MCP, FastAPI, and Docker are adapters around the same save_memories and recall_memories operations.

One-command MCP

Add TinyContext to any stdio MCP client:

{
  "mcpServers": {
    "tinycontext": {
      "command": "uvx",
      "args": [
        "--python",
        "3.12",
        "--from",
        "tinysuite-context[server]",
        "tinycontext"
      ]
    }
  }
}

The no-argument tinycontext command runs stdio MCP. On its first launch, TinyContext downloads the selected ONNX embedding bundle into its per-user data directory. The database is created lazily on the first save or recall. Later launches reuse both local assets.

Check the resolved configuration and storage readiness with:

uvx --python 3.12 --from "tinysuite-context[server]" tinycontext doctor

TinyContext exposes two tools:

save_memories(memories)
recall_memories(query)

• Use save_memories for durable facts, preferences, decisions, and research notes.
• Use recall_memories before answering when previous context may help.

Python library

Install only the transport-independent core:

pip install tinysuite-context

from pathlib import Path

from tinycontext import (
    MemoryInput,
    TinyContextConfig,
    recall_memories,
    save_memories,
)

config = TinyContextConfig(
    memory_db_path=str(Path("agent-memory.db").resolve()),
    recall_max_tokens=800,
)

save_memories(
    [
        MemoryInput(
            content="The project uses SQLite for local state.",
            tags=["architecture"],
            metadata={"source": "design-session"},
        )
    ],
    session_id="project-a",
    config=config,
)

result = recall_memories(
    "How does the project store state?",
    session_id="project-a",
    config=config,
)

for memory in result["memories"]:
    print(memory["content"])

Programmatic configuration does not read environment variables or depend on the checkout. Passing no config uses the per-user data directory returned by platformdirs.

Docker

Run the published image as an MCP server over Streamable HTTP:

docker compose -f "https://github.com/TinySuiteHQ/TinyContext.git#main:compose.quickstart.yaml" up -d

Connect an MCP client to:

{
  "mcpServers": {
    "tinycontext": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

The data volume persists /data/memories.db and /data/models.

Stop the service with:

docker compose -f "https://github.com/TinySuiteHQ/TinyContext.git#main:compose.quickstart.yaml" down

For a local image build:

docker compose up -d --build

The optional FastAPI profile uses the same image:

docker compose --profile fastapi up -d --build

• MCP Streamable HTTP: http://localhost:8000/mcp
• FastAPI: http://localhost:8001

How recall works

flowchart LR
    A[Agent] --> B[save_memories]
    A --> C[recall_memories]
    B --> D[(SQLite)]
    C --> D
    C --> E[BM25 rank]
    C --> G[sqlite-vec cosine rank]
    E --> H[Weighted RRF]
    G --> H
    H --> F[Token budget trim]
    F --> A

1. Generate embeddings locally with the selected ONNX model.
2. Save text, metadata, and float32 embedding BLOBs in the same SQLite row.
3. Filter by session_id, rank lexical matches with BM25, and calculate cosine similarity in SQLite through sqlite-vec.
4. Fuse both rankings with weighted reciprocal rank fusion (RRF).
5. Return the highest-ranked memories within the token budget.

Existing TinyContext databases are upgraded in place with nullable embedding columns. The first recall backfills embeddings for legacy rows; no database migration command or separate vector service is required.

FastAPI

The optional HTTP API mirrors the two MCP tools.

Install and run it directly:

pip install "tinysuite-context[server]"
uvicorn tinycontext.servers.fastapi_server:app --host 0.0.0.0 --port 8000

Save request

{
  "session_id": "optional-session",
  "memories": [
    {
      "content": "User prefers concise answers",
      "tags": ["preference"],
      "metadata": {"source": "chat"}
    }
  ]
}

Recall request

{
  "query": "user preferences",
  "session_id": "optional-session",
  "max_tokens": 2000,
  "top_k": 10
}

Error codes

Configuration

The core defaults are:

Server processes look for context_config.json in the per-user TinyContext configuration directory. A relative memory_db_path inside a JSON config is resolved relative to that file.

Environment overrides:

An existing checkout-local database remains usable:

TINYCONTEXT_MEMORY_DB_PATH=/absolute/path/to/TinyContext/data/memories.db tinycontext

Development

git clone https://github.com/TinySuiteHQ/TinyContext
cd TinyContext
python -m venv .venv
source .venv/bin/activate
pip install -e ".[server]"
python -m unittest discover tests
python scripts/smoke_mcp_stdio.py

TinyContext supports Python 3.12 and newer. CI tests Python 3.12, 3.13, and 3.14 across Linux, macOS, and Windows.

Source-checkout compatibility shims remain available:

python servers/mcp_server.py
uvicorn servers.fastapi_server:app --host 0.0.0.0 --port 8000

Entrypoints

• tinycontext.save_memories and tinycontext.recall_memories: Python API
• tinycontext / tinycontext mcp: stdio MCP
• tinycontext serve: Streamable HTTP MCP
• tinycontext doctor: configuration and storage readiness
• tinycontext.servers.fastapi_server:app: optional FastAPI application

Security

Release images are scanned with Trivy, run as a non-root user, and signed with Cosign. See SECURITY.md for details and how to report a vulnerability.

License

MIT. See LICENSE and NOTICE.