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About BioContext7

Up-to-date bioinformatics documentation for AI

BioContext7 eliminates hallucinated bioinformatics tool APIs. When AI coding assistants need to call a bioinformatics tool, BioContext7 injects real-time documentation — correct parameters, current versions, and validated install commands — directly into the conversation context.

The problem

Large language models hallucinate bioinformatics tool parameters, reference outdated Bioconductor APIs, suggest wrong conda channels, and fabricate function signatures. In computational biology, a single wrong parameter can silently corrupt an entire analysis pipeline.

  • Hallucinated function parameters that don't exist
  • Outdated API versions from training data cutoff
  • Wrong package managers and install commands
  • Fabricated citation DOIs and publication metadata

The solution

BioContext7 injects real-time documentation from authoritative registries — bio.tools, PyPI, Bioconda, Bioconductor, and Dockstore — directly into AI coding assistants via the Model Context Protocol (MCP). Every parameter, version, and install command is sourced from the registry, not from model memory.

  • Real-time docs from 5 curated bio registries
  • Validated parameters, types, and defaults
  • Current install commands for conda, pip, and R
  • Verified citations with real DOIs

How it works

A four-layer pipeline that crawls authoritative registries, indexes tools offline, and serves verified documentation to AI assistants.

1

Registry Crawlers

Fetch tool metadata from bio.tools, Bioconda, Bioconductor, nf-core, and Dockstore

2

SQLite Index + Export Pipeline

Normalize, deduplicate, and score tools into a searchable FTS5 index

3

Pre-indexed Static JSON on Vercel

Exported documentation served as static files — no backend required

4

MCP Server

Delivers verified docs to Claude, Cursor, and other AI assistants (local stdio or remote API)

End result

Claude or Cursor receives verified, up-to-date tool documentation — correct parameters, current versions, and validated install commands — eliminating hallucinated APIs.

What sets BioContext7 apart

Built specifically for bioinformatics — not a general-purpose documentation tool adapted for science.

Curated for Safety
We intentionally gate our registry to 1,000-2,000 highly verified tools to guarantee AI generation safety, rather than dumping untested repositories into your context window.
EDAM Ontology
Tools are annotated with EDAM topics and operations, enabling semantic search by biological domain rather than keyword guessing.
Academic Impact Tracking
Citation counts, co-citation networks, and publication metadata surface the most trusted tools in each domain.
Verified Runtime
Every tool includes validated parameters, exact version pinning, and verified installation commands (Bioconda, PyPI, R) that actually work in modern environments.
Deep AI Skills
Beyond simple documentation, top tools include deep integration skills for Claude and Cursor to autonomously orchestrate complex multi-step pipelines.
Multi-Registry Deduplication
Unified cross-referencing across bio.tools, PyPI, Bioconda, Bioconductor, and Dockstore to ensure the AI always pulls the authoritative source.

Built by Hordago Labs

Hordago Labs builds developer infrastructure for computational biology. We believe AI coding assistants should be grounded in authoritative data sources — not trained guesses. BioContext7 is our flagship project: an open-source platform that connects AI to the bioinformatics registries researchers already trust.

Open Source
2,000+ Tools
5 Registries