TinyFish Guided Research MCP

An MCP server that adds a simple research workflow on top of TinyFish Search and Fetch.

The client model does the reasoning. The server keeps track of the research run, handles the search and fetch flow, stores the state, checks evidence and citations, and tells the client what should happen next.

There is no LLM running inside the server.

How it works

A research run usually follows this flow:

text

Research request
      ↓
Plan
      ↓
Search
      ↓
Review sources
      ↓
Fetch useful content
      ↓
Track claims and evidence
      ↓
Check what is supported or still missing
      ↓
Research the gaps
      ↓
Verify citations
      ↓
Finalize

Search results are treated as candidates first, not as evidence by default.

The server also keeps duplicate sources from being counted more than once. This includes cases where the same paper or source appears through different URLs or mirrors.

Quotes are checked against the fetched source content, while semantic decisions such as whether a passage actually supports a claim are left to the client model.

Conflicting evidence is kept in the research state instead of being ignored, and citations are checked before the research is finalized.

What it handles

Research state across multiple steps
TinyFish Search and Fetch calls
Source screening and duplicate handling
Claim and evidence tracking
Quote checks against fetched content
Conflicting evidence
Research gaps and follow-up searches
Citation verification
Research budgets and stopping conditions
SQLite and PostgreSQL persistence

Quick start

Hosted

The hosted MCP endpoint is:

text

https://tinyfish-guided-research-mcp.fastmcp.app/mcp

Use it as a Streamable HTTP MCP server.

Local

Requires Python 3.11+ and a TinyFish API key.

bash

TINYFISH_API_KEY="your-api-key" uvx tinyfish-guided-research-mcp

Example MCP client config:

config.json

{
  "mcpServers": {
    "tinyfish-research": {
      "command": "uvx",
      "args": ["tinyfish-guided-research-mcp"],
      "env": {
        "TINYFISH_API_KEY": "your-api-key"
      }
    }
  }
}

The compatibility entrypoint is also available:

bash

uvx --from tinyfish-guided-research-mcp tinyfish-research-mcp

Storage

SQLite is fine for local or single-instance use:

server.ts

export RESEARCH_DB_PATH="research_state.db"

For hosted or multi-instance deployments, use PostgreSQL:

server.ts

export DATABASE_URL="postgresql://user:password@host:5432/database?sslmode=require"

PostgreSQL is the better option when more than one server instance can access the same research state.

Distribution

The server is available through PyPI, the official MCP Registry, and the hosted Horizon endpoint.

PyPI:

text

tinyfish-guided-research-mcp

MCP Registry:

text

io.github.MohdSaleh/tinyfish-guided-research

Hosted MCP:

text

https://tinyfish-guided-research-mcp.fastmcp.app/mcp

Development

Clone the repo and install the dependencies:

bash

git clone https://github.com/MohdSaleh/tinyfish-guided-research-mcp.git
cd tinyfish-guided-research-mcp
uv sync --all-extras

Run it locally:

bash

TINYFISH_API_KEY="your-api-key" uv run tinyfish-guided-research-mcp

Run the checks:

bash

uv run ruff check .
uv run ruff format --check .
uv run pyright
uv run pytest
uv run python evals/run_evals.py
uv run pip-audit
uv build

The regular tests cover the implementation and storage layer. The research evals cover cases such as duplicate sources, weak evidence, quote mismatches, superseded claims, and citation coverage.

You can also inspect the MCP tools with:

Terminal

npx @modelcontextprotocol/inspector \
  --cli uv run tinyfish-guided-research-mcp \
  --method tools/list

Deploying on Horizon

If you want to deploy your own instance with Prefect Horizon, use:

text

Entrypoint:
src/tinyfish_research_mcp/server.py:mcp

Dependencies:
pyproject.toml

Set:

text

TINYFISH_API_KEY
DATABASE_URL

Use PostgreSQL for hosted deployments instead of the local SQLite fallback.

License

MIT