Metadata-Version: 2.5
Name: biomedmcp
Version: 0.2.4
Summary: biomedmcp biomedical research MCP and CLI
Project-URL: Repository, https://github.com/unibio-intelligence/biomedmcp
Author: UniBio Intelligence
License-Expression: Apache-2.0
License-File: LICENSE
License-File: NOTICE.md
License-File: servers/python/antibody-registry/LICENSE
License-File: servers/python/bio-ontology-mcp/LICENSE
License-File: servers/python/chembl-mcp/LICENSE
License-File: servers/python/clinical-trials-mcp/LICENSE
License-File: servers/python/dailymed-mcp/LICENSE
License-File: servers/python/depmap-mcp/LICENSE
License-File: servers/python/ensembl-mcp/LICENSE
License-File: servers/python/gtex/LICENSE
License-File: servers/python/mychem/LICENSE
License-File: servers/python/mydisease/LICENSE
License-File: servers/python/mygene/LICENSE
License-File: servers/python/ncbi-mcp/LICENSE
License-File: servers/python/omnipath-mcp/LICENSE
License-File: servers/python/openfda-mcp/LICENSE
License-File: servers/python/opentargets/LICENSE
License-File: servers/python/orange-book-mcp/LICENSE
License-File: servers/python/pdb-mcp/LICENSE
License-File: servers/python/protein-atlas-mcp/LICENSE
License-File: servers/python/reactome-mcp/LICENSE
License-File: servers/python/surechembl-mcp/LICENSE
License-File: servers/python/uniprot-mcp/LICENSE
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Requires-Dist: httpcore==1.0.9
Requires-Dist: httpx==0.28.1
Requires-Dist: jsonschema>=4.23
Requires-Dist: mcp<3.0.0,>=2.1.1
Requires-Dist: mmcif-pdbx==2.1.0
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Requires-Dist: pandas>=2.0.0
Requires-Dist: pydantic<3,>=2.12
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Requires-Dist: uvicorn>=0.35
Description-Content-Type: text/markdown

# biomedmcp

BioMedMCP gives your AI agent cited access to biomedical sources, including
PubMed, UniProt, ClinicalTrials.gov, ChEMBL, Open Targets and GTEx, so its
answers point to records you can check.

<!-- mcp-name: io.github.unibio-intelligence/biomedmcp -->

## Start with a research question

Try this with your MCP client:

> Build an evidence notebook on LDL-receptor biology and cholesterol lowering.
> Start with a human LDLR target dossier, inspect tissue expression, then find
> PCSK9 genetic studies. Separate observations, intervention results and open
> questions. Cite the records you read and identify any unavailable sources.

Connect to `https://biomedmcp.unibiointelligence.com/mcp` using Streamable HTTP
and sign in when prompted. For a local server, install the
Python distribution or build from this checkout; see [connection options](https://github.com/unibio-intelligence/biomedmcp/blob/main/docs/connect.md).

## Run the same investigation locally

Use Python 3.13+ in an activated virtual environment:

```sh
python -m pip install biomedmcp==0.2.4
UBI_PROFILE=local biomedmcp build_dossier '{"workflow":"target_dossier","focus":"LDLR","species":9606}'
```

The single Python distribution includes all source adapters. From a source
checkout, `uv sync --frozen` installs the locked environment; prefix commands
with `uv run --frozen` when using it.

These focused calls can fill gaps in the notebook:

<!-- public-call-examples:start -->
```sh
UBI_PROFILE=local biomedmcp get_record '{"entity":"gene","id":"LDLR","species":9606,"sections":["expression"]}'
UBI_PROFILE=local biomedmcp search_literature '{"query":"PCSK9","filters":{"source":"pubmed","date_from":"2024-01-01"}}'
UBI_PROFILE=local biomedmcp get_variant_evidence '{"id":"PCSK9 R46L"}'
```
<!-- public-call-examples:end -->

Check each response's source, species and identifiers before combining records;
a partial or unavailable section is a gap, not a negative result. Follow its
continuation and recovery links to see more than the preview.

## Choose the next question

| Research task | Where to start |
| --- | --- |
| Separate target observations from causal claims | [Evidence investigations](https://github.com/unibio-intelligence/biomedmcp/blob/main/docs/investigations.md) |
| Inspect an allele without conflating transcript or genome build | [Typed call examples](https://github.com/unibio-intelligence/biomedmcp/blob/main/docs/examples.md) |
| Read a paper's accessible text and tables | `get_record` with `entity: "article"` and `sections: ["fulltext"]` |
| Examine registered studies | `trial_search`; inspect each study's submitted record |
| Reproduce a local analysis | [Synthetic study walkthrough](https://github.com/unibio-intelligence/biomedmcp/blob/main/docs/studies.md) |

## Connect a local agent

```sh
UBI_PROFILE=local biomedmcp-server --transport http --host 127.0.0.1 --port 8000
```

Use `http://127.0.0.1:8000/mcp`, or configure a stdio client using the
[connection guide](https://github.com/unibio-intelligence/biomedmcp/blob/main/docs/connect.md). The [call reference](https://github.com/unibio-intelligence/biomedmcp/blob/main/docs/examples.md) covers
exact JSON arguments; [result handling](https://github.com/unibio-intelligence/biomedmcp/blob/main/docs/output-contracts.md) explains partial
results and evidence boundaries.

Some sources need a key or installed dataset. Run `biomedmcp diagnose` and consult
[source access](https://github.com/unibio-intelligence/biomedmcp/blob/main/docs/source-access.md). Keep credentials in environment variables
or client secret storage. A failed source doesn't mean there is no evidence.

The integration code uses [Apache-2.0](https://github.com/unibio-intelligence/biomedmcp/blob/main/LICENSE). Retrieved data retains its
[source terms](https://github.com/unibio-intelligence/biomedmcp/blob/main/docs/source-licenses.md). See [notices](https://github.com/unibio-intelligence/biomedmcp/blob/main/NOTICE.md),
[release notes](https://github.com/unibio-intelligence/biomedmcp/blob/main/CHANGELOG.md) and [contributing](https://github.com/unibio-intelligence/biomedmcp/blob/main/CONTRIBUTING.md).

## Acknowledgements

We gratefully acknowledge support from Anthropic's AI for Science rare disease
research grants.
