biomedmcp

io.github.unibio-intelligence由 GitHub 账号 unibio-intelligence 发布v0.2.5更新于 Nov 6, 2026

Integrated public biomedical research with cited, bounded evidence.

已验证Streamable HTTP可网页运行知识与记忆数据与分析

概览

AI 生成的概览

BioMedMCP 让 AI 助手以可核查引用的方式访问 PubMed、UniProt、ClinicalTrials.gov、ChEMBL、Open Targets、GTEx 等公开生物医学数据源。

可以做什么
  • 按文本检索基因、蛋白、药物、疾病、表型、通路、变异、研究、专利与作者,并按需获取单条记录的指定证据章节
  • 跨 PubMed、Europe PMC、PubTator 与 LitSense 检索论文,并按疾病、干预、分期与招募状态检索临床试验
  • 获取变异证据(ClinVar、gnomAD、CIViC 及可选预测源),并分析已安装的公开癌症基因组研究
  • 把名称解析为带物种的精确 ID,列出与某条记录关联的记录,并生成带引用的多章节档案
  • 执行特定数据源操作、并行批量执行最多十个读取调用,并查看可用数据源、章节、工作流与剩余配额
适用场景
适合需要以可核查、带引用的记录回答生物医学研究问题的场景,例如靶点档案、变异解读、文献综述或临床试验查询。
运行要求
可使用托管的 Streamable HTTP 端点(按提示登录),或本地安装:Python 3.13+ 与 biomedmcp 包(或容器镜像),运行时设置 UBI_PROFILE 环境变量。部分数据源需要密钥或已安装的数据集;凭据应放在环境变量或客户端密钥存储中。
安装前请注意
登录托管服务会把使用绑定到账户,并受配额限制。部分数据源需要 API 密钥或已安装数据集。检索到的数据保留其来源条款;数据源失败或章节缺失表示存在缺口,而非没有证据。私有数据湖、项目导入与建模不在本服务器范围内。

安装

在 SourceWeft 中

  1. 打开 控制台中的 biomedmcp,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.unibio-intelligence%2Fbiomedmcp/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "biomedmcp": {
      "type": "http",
      "url": "https://biomedmcp.unibiointelligence.com/mcp"
    }
  }
}

README

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.

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.

Run the same investigation locally

Use Python 3.13+ in an activated virtual environment:

sh
python -m pip install biomedmcp==0.2.5UBI_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:

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"}'

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 taskWhere to start
Separate target observations from causal claimsEvidence investigations
Inspect an allele without conflating transcript or genome buildTyped call examples
Read a paper's accessible text and tablesget_record with entity: "article" and sections: ["fulltext"]
Examine registered studiestrial_search; inspect each study's submitted record
Reproduce a local analysisSynthetic study walkthrough

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. The call reference covers exact JSON arguments; result handling explains partial results and evidence boundaries.

Some sources need a key or installed dataset. Run biomedmcp diagnose and consult source access. 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. Retrieved data retains its source terms. See notices, release notes and contributing.

Acknowledgements

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

来源:README.md,提交 70ca3db

工具

13

search_records

Search biomedical records

Search genes, proteins, drugs, diseases, phenotypes, pathways, variants, pharmacogenomics, GWAS, diagnostics, adverse events, studies, patents or authors by text; returns IDs for get_record (variants: get_variant_evidence). Papers: search_literature. Trials: search_trials. Patent search is chemistry-linked (SureChEMBL); assignee/inventor/CPC search is query_source(source=patents). e.g. {"entity":"gene","query":"BRCA1","filters":{"species":9606}}

输入参数limitquery必填cursorentity必填outputfiltersfreshness

get_record

Get record and evidence sections

Fetch one record by ID with optional evidence sections, e.g. gene expression/eqtl/diseases/druggability, drug label/safety, article fulltext, trial eligibility, protein domains/structures. Variants: get_variant_evidence. List an entity's sections with get_capabilities(entity=...). e.g. {"entity":"gene","id":"PCSK9","species":9606}

输入参数id必填entity必填sourcesspeciessectionsfreshnesssource_versionssection_parameters

search_literature

Search literature

Search papers across PubMed, Europe PMC, PubTator and LitSense with date, author, journal, article-type and open-access filters. Deduplicates across sources and reports each source's outcome. Read abstracts or full text with get_record(entity=article). e.g. {"query":"PCSK9 LDL cholesterol"}

输入参数limitquery必填cursoroutputfiltersfreshness

search_trials

Search clinical trials

Search clinical trials (ClinicalTrials.gov, NCI) by condition, intervention, phase and recruitment status. Read arms, eligibility or outcomes with get_record(entity=trial). Does not assess an individual's eligibility. e.g. {"query":"familial hypercholesterolemia","filters":{"status":"RECRUITING"}}

输入参数limitquery必填cursoroutputfiltersfreshness

get_variant_evidence

Get variant evidence

Get evidence for one variant (rsID, gene + protein change, HGVS or chr:pos:ref:alt). Defaults: ClinVar, gnomAD population, CIViC; add predictions (AlphaMissense), eve, cosmic, oncokb, hotspots, gwas or cbioportal. Ambiguous assembly or transcript is reported, not guessed; predictions keep source and uncertainty. e.g. {"id":"PCSK9 R46L"}

输入参数id必填sourcesassemblysectionsfreshnesssection_parameters

analyze_study

Analyze genomics study

Analyze an installed public cancer genomics study: gene_profile, mutation_cohort, survival, compare, co_occurrence, filter_cohort, top_mutated. List installed studies with get_capabilities(source=study); ubi_synthetic is invented demo data. Report sample denominators and exclusions. Private data analysis uses the Ubi platform. e.g. {"analysis":"gene_profile","study":"ubi_synthetic","gene":"TP53"}

无需输入参数。

resolve_entity

Resolve identifiers

Map a name, alias or ambiguous identifier to exact species-qualified IDs before joining evidence. Returns candidates when ambiguous; a provider match score alone is not identity. Skip when you already hold an exact ID. e.g. {"entity":"gene","query":"LDL receptor","species":9606}

输入参数query必填entityspeciesassembly

build_dossier

Build research dossier

Build a multi-section dossier in one call: verified identity, default record sections and top related papers, with citations and per-source outcomes. Workflows: target_dossier, model_input_bundle (gene), drug_review, disease_landscape, pathway_review, variant_dossier, negative_evidence (bounded literature check; never proves absence). Add sections with get_record. e.g. {"workflow":"target_dossier","focus":"LDLR","species":9606}

输入参数focus必填speciesassemblysectionsworkflow必填section_parameters

query_source

Query source directly

Run a source-specific operation that other tools do not cover, e.g. ChEMBL bioactivities, Ensembl coordinates, PDB structure quality, OmniPath interactions, NCBI Datasets genomes, ncbi-records for GenBank/GEO/SRA. Read get_capabilities(source=...) for operations and params first. e.g. {"source":"gtex","operation":"get_metadata","params":{"info_type":"tissues","dataset_id":"gtex_v10"}}

输入参数paramssource必填freshnessoperation必填

run_batch

Run batched reads

Run up to ten independent read calls in parallel with one quota reservation; each result keeps its own outcome. build_dossier and utilities are not batchable. e.g. {"calls":[{"operation":"get_record","arguments":{"entity":"gene","id":"PCSK9","species":9606}}]}

输入参数calls必填idempotency_key

get_capabilities

Inspect get_capabilities

List enabled sources, entity sections, dossier workflows and source operations with exact parameter schemas. Narrow with source or entity; diagnose=true probes source health. Examples: biomedmcp://guide. e.g. {}

输入参数entitysourcediagnose

get_quota

Check quota

Show remaining research allowance and reset times; signed-in accounts get higher limits. e.g. {}

无需输入参数。

版本历史

2
  1. v0.2.5最新Nov 6, 2026
  2. v0.2.4Nov 5, 2026

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