Corollary — scientific AI models, tools and databases
Find open biology and chemistry AI models, bioinformatics tools and databases, and run them online.
사용해야 할까요
품질 및 안전성
발견 사항 (2)
- LOWTool 'list_science_tasks' description lacks action verblist_science_tasks에서
- LOWTool 'list_scientific_ai_tasks' description lacks action verblist_scientific_ai_tasks에서
도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.
컨텍스트 비용
이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.
설치
원클릭 설치
`claude_desktop_config.json` 파일에 다음을 추가하세요:
{
"mcpServers": {
"corollary": {
"url": "https://corollary-labs.com/mcp"
}
}
}원격 엔드포인트
https://corollary-labs.com/mcpstreamable-http할 수 있는 일
도구 목록
도구 (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.
입력 스키마
{
"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.
입력 스키마
{
"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.
입력 스키마
{
"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.
입력 스키마
{
"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.
입력 스키마
{
"type": "object",
"properties": {},
"additionalProperties": false
}🟢list_scientific_ai_tasks
The task categories Corollary compares models and tools in, with a page URL for each.
입력 스키마
{
"type": "object",
"properties": {},
"additionalProperties": false
}커뮤니티
증거