A tiny local-first web research engine for MCP agents.

TinySearch searches the web, reranks results, crawls the best pages, extracts the most relevant chunks, and returns a source-grounded prompt your LLM can answer from.

No hosted dashboard. No account system. No analytics. No scraped-data cache.

Just search -> crawl -> rerank -> grounded prompt.

Why TinySearch?

• Give local agents web research without wiring together a whole search stack.
• Keep source URLs attached to the evidence your model sees.
• Avoid dumping full webpages into context.
• Use local ONNX embeddings or an OpenAI-compatible embedding API.
• Run over MCP or a simple FastAPI endpoint.

TinySearch is built for local agents, prototypes, personal workflows, and small systems where source-grounded web research matters more than running a full search backend.

How it works

flowchart TB
    subgraph Row1["Search and choose pages"]
        direction LR
        A[User query] --> B[DuckDuckGo HTML search]
        B --> C[Filter HTTP results<br/>build title URL domain snippet docs]
        C --> D[Rank search docs<br/>dense + BM25 weighted RRF]
    end

    subgraph Row2["Crawl and build prompt"]
        direction LR
        E[Crawl kept URLs in parallel<br/>crawl4ai markdown] --> F[Truncate and chunk markdown]
        F --> G[Rank combined chunk pool<br/>dense + BM25 weighted RRF]
        G --> H[Dedupe chunks<br/>apply source quotas and fill]
        H --> I[Build source-grounded prompt]
    end

    Row1 --> Row2

TinySearch does not directly answer the question. It returns a structured prompt in the MCP tool's answer field, and your client model uses that prompt to produce the final cited response.

QUESTION
What happened in the latest NFL playoffs?

TODAY
2026-05-15

RESULTS
1. Title
   URL
   Relevant extracted text...

2. Title
   URL
   Relevant extracted text...

INSTRUCTIONS
Answer only from the results. Cite source URLs.

Quick start

git clone https://github.com/MarcellM01/TinySearch
cd TinySearch

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Run the MCP server:

python servers/mcp_server.py

Or run the HTTP API:

uvicorn servers.fastapi_server:app --reload

MCP setup

Add TinySearch to your MCP client config. Use absolute paths.

macOS / Linux:

{
  "mcpServers": {
    "tinysearch": {
      "command": "/absolute/path/to/TinySearch/.venv/bin/python",
      "args": [
        "/absolute/path/to/TinySearch/servers/mcp_server.py"
      ]
    }
  }
}

Windows:

{
  "mcpServers": {
    "tinysearch": {
      "command": "C:/absolute/path/to/TinySearch/.venv/Scripts/python.exe",
      "args": [
        "C:/absolute/path/to/TinySearch/servers/mcp_server.py"
      ]
    }
  }
}

The MCP server exposes one tool:

research(query)

Pass the user's question as-is. TinySearch searches, crawls, reranks, and returns the grounded prompt in answer.

Template config files live in mcp_templates/.

The repo also includes agentic_coding_templates/global-rules-recommended.md, a global-rules template for agentic coding tools such as Cline and Roo Code. These rules help coding agents call TinySearch only when web research is actually needed.

The server uses stdio by default, which is what Cursor and similar clients expect when they spawn python .../mcp_server.py. To run with sse or streamable-http, set MCP_TRANSPORT when starting the process. Do not put transport in configs/research_config.json.

Docker

Pull the default image:

docker pull marcellm01/tinysearch:latest

MCP over HTTP

Most MCP clients that connect to a running server use this mode.

Start TinySearch on port 8000:

docker run --rm \
  -p 8000:8000 \
  -e MCP_TRANSPORT=streamable-http \
  -e MCP_HOST=0.0.0.0 \
  marcellm01/tinysearch:latest

Then point any MCP client that supports streamable HTTP at:

http://localhost:8000/mcp

Example MCP client config:

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

With MCP_TRANSPORT=streamable-http, the image serves Streamable HTTP on /mcp and SSE on /mcp/sse. GET requests to /mcp without an mcp-session-id are treated as the legacy SSE stream. If a client still cannot connect, try MCP_TRANSPORT=sse alone or the stdio Docker setup below.

For repeated use, keep downloaded models in a Docker volume and mount your local config:

docker run --rm \
  -p 8000:8000 \
  -v tinysearch-models:/data/models \
  -v "$PWD/configs/research_config.json:/config/research_config.json:ro" \
  -e TINYSEARCH_CONFIG_PATH=/config/research_config.json \
  -e MCP_TRANSPORT=streamable-http \
  -e MCP_HOST=0.0.0.0 \
  marcellm01/tinysearch:latest

MCP over stdio

Use this mode for MCP clients that launch tools as local commands instead of connecting to a URL. Replace /absolute/path/to/TinySearch with this repo's absolute path:

{
  "mcpServers": {
    "tinysearch": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-v",
        "tinysearch-models:/data/models",
        "-v",
        "/absolute/path/to/TinySearch/configs/research_config.json:/config/research_config.json:ro",
        "-e",
        "TINYSEARCH_CONFIG_PATH=/config/research_config.json",
        "-e",
        "TINYSEARCH_MODELS_DIR=/data/models",
        "marcellm01/tinysearch:latest"
      ]
    }
  }
}

Edit configs/research_config.json to choose embedding_model (fast, balanced, quality, or a custom Hugging Face ONNX repo id). The named Docker volume keeps downloaded model bundles between launches.

Optional HTTP server

Useful when you want HTTP instead of MCP:

uvicorn servers.fastapi_server:app --reload

Endpoints:

• GET /health
• GET /web_search?query=...
• POST /site_crawl
• POST /research

Configuration

Tune research defaults in configs/research_config.json. Set TINYSEARCH_CONFIG_PATH to load a different JSON config file, which is the recommended Docker override pattern.

The onnx embedding backend uses local ONNX bundles under models/. Starting the MCP server or FastAPI app downloads the configured embedding_model once from Hugging Face when embedding_backend is onnx.

Built-in local presets:

• fast: onnx-models/all-MiniLM-L6-v2-onnx
• balanced: BAAI/bge-small-en-v1.5
• quality: BAAI/bge-base-en-v1.5

You can also set embedding_model to a custom Hugging Face ONNX repo id. Set TINYSEARCH_MODELS_DIR to move the model cache, or use TINYSEARCH_ONNX_MODEL_DIR when you need to point at one exact bundle directory.

Key settings:

• Search: search_top_k, search_rrf_cutoff, search_dense_weight, search_max_results_to_keep
• Chunks: chunk_rrf_cutoff, chunk_dense_weight, chunk_max_results_to_keep
• Crawl: crawl_max_chunk_tokens, crawl_overlap_tokens, max_concurrent_crawls
• Embeddings: embedding_backend, embedding_model, embedding_openai_env_file, max_concurrent_embedding_calls
• Tokenizer: encoding_name
• Dense input prefixes: dense_query_prefix, dense_document_prefix
• Trace: trace_path

For embedding_backend openai_compatible, add a .env file at the project root, or set embedding_openai_env_file, with:

OPENAI_BASE_URL=
OPENAI_API_KEY=
OPENAI_EMBEDDING_MODEL=

OPENAI_BASE_URL is optional for api.openai.com. EMBEDDING_MODEL and MODEL_NAME are accepted as aliases for OPENAI_EMBEDDING_MODEL.

The research pipeline requires dense embeddings. It raises if search_dense_weight or chunk_dense_weight is set to 0.

When not to use TinySearch

TinySearch is not a replacement for a commercial search API or a persistent crawler. It is probably not the right tool if you need:

• guaranteed search coverage
• large-scale indexing
• long-term page caching
• enterprise observability
• production SLA-backed web search

TinySearch vs...

Entrypoints

• pipelines.agentic_research.agentic_run: single-turn search, crawl, ranking, and prompt assembly
• servers.mcp_server: MCP server for agent clients
• servers.fastapi_server: optional HTTP API

Tests

Run the unittest suite:

python -m unittest discover tests

Privacy notes

TinySearch reads the pages it crawls and returns ranked excerpts to the calling client. It does not include credentials in the repo, and .env / trace output should stay local. If you enable openai_compatible embeddings, your embedding provider receives the text snippets sent for vectorization.

License

Source code in this repository is under the MIT License.

When embedding_backend is onnx, TinySearch may download the selected local ONNX embedding bundle at runtime from Hugging Face. Those weights are separate distributions under their model-card licenses; keep license and attribution notices if you ship or redistribute those files. Optional manual export for fast uses sentence-transformers/all-MiniLM-L6-v2 (Apache-2.0).

See NOTICE for Docker and third-party distribution notes.