Corollary — scientific AI models, tools and databases
Find open biology and chemistry AI models, bioinformatics tools and databases, and run them online.
Should I use this
Quality & Safety
Findings (2)
- LOWTool 'list_science_tasks' description lacks action verbin list_science_tasks
- LOWTool 'list_scientific_ai_tasks' description lacks action verbin list_scientific_ai_tasks
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"corollary": {
"url": "https://corollary-labs.com/mcp"
}
}
}Remote endpoints
https://corollary-labs.com/mcpstreamable-httpWhat it can do
Tool inventory
Tools (6)
🟢search_scientific_ai(query, kind, limit)
Search Corollary's catalogue of open scientific AI models (protein structure prediction, protein design, docking, antibodies, genomics, single-cell, chemistry/ADMET, simulation, pathology, weather), bioinformatics programs (BUSCO, samtools, BLAST…), scientific databases (UniProt, PDB, gnomAD…), nf-core pipelines and datasets. Each result says what it does, its licence and whether it can be run online on Corollary with no GPU or installation, with the page URL. Use it when a user asks which model or tool to use for a biology or chemistry task, or where to run one.
Input Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Name or task, e.g. 'ESMFold', 'protein-ligand binding affinity', 'phage annotation'."
},
"kind": {
"type": "string",
"enum": [
"any",
"model",
"dataset",
"software",
"database",
"pipeline"
],
"description": "Restrict to one kind. Default: any."
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 25,
"description": "Default 10."
}
},
"required": [
"query"
],
"additionalProperties": false
}🟢get_scientific_ai_details(ref)
Full reference for one entry: what it is, how it works, key facts, uses, strengths, limitations, citation, licence, and how to run it on Corollary. Pass a name ('RFdiffusion'), an id ('esmfold2', 'nf-core/rnaseq', 'facebook/esm2_t33_650M_UR50D') or a corollary-labs.com URL.
Input Schema
{
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "Name, id or Corollary page URL."
}
},
"required": [
"ref"
],
"additionalProperties": false
}🟢compare_scientific_ai_for_task(task)
For a task such as 'protein structure prediction', 'protein design', 'antibody design', 'molecular docking', 'single-cell', 'ADMET', 'phage genomics' or 'CRISPR', return every model or tool Corollary lists for it — what each does, licence, whether it runs online — and guidance on how to choose between them.
Input Schema
{
"type": "object",
"properties": {
"task": {
"type": "string",
"description": "The task, in plain words."
}
},
"required": [
"task"
],
"additionalProperties": false
}🟢how_to_run_science_task(task)
For a piece of scientific work someone wants done — 'annotate a phage genome', 'deconvolve bulk RNA-seq into cell types', 'check assembly completeness', 'dock a ligand', 'fit an IC50', 'process accelerometer data', 'call variants', 'find CRISPR arrays' — return how it is done on Corollary: the stages in order, the programs, models and databases used, what commonly goes wrong, and the page that starts it. Use this when the user wants a task done rather than a model named.
Input Schema
{
"type": "object",
"properties": {
"task": {
"type": "string",
"description": "The work the user wants done, in plain words."
}
},
"required": [
"task"
],
"additionalProperties": false
}🟢list_science_tasks
Every scientific analysis Corollary has a page and a method for, with its URL.
Input Schema
{
"type": "object",
"properties": {},
"additionalProperties": false
}🟢list_scientific_ai_tasks
The task categories Corollary compares models and tools in, with a page URL for each.
Input Schema
{
"type": "object",
"properties": {},
"additionalProperties": false
}Community
Evidence