biocontext
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NCBI_API_KEY | No | Optional NCBI Entrez API key. Raises rate limit from 3 to 10 requests per second. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| resolve_geneA | Resolve an ambiguous gene symbol, alias, or accession to an authoritative entity. Args: query: Gene symbol (e.g. TP53), alias (e.g. HER2, p53), or accession. taxon_id: NCBI Taxonomy ID (default: 9606 for human). chromosome: Optional chromosome hint for disambiguation (e.g. '17' or 'chr17'). locus_type: Optional biotype hint (e.g. 'protein-coding', 'pseudogene'). Returns: JSON string containing the resolved canonical gene entity, cross-references, and match audit trail. |
| get_protein_infoA | Fetch structured protein details from UniProt by primary accession. Args: accession: UniProt accession ID (e.g. P04637). Returns: JSON string containing protein name, gene, length, mass, and organism. |
| batch_resolve_genesA | Resolve a list of gene identifiers/aliases in batch with concurrency control and performance metrics. Args: queries: List of gene symbols or aliases (e.g. ["TP53", "HER2", "EGFR", "7157", "P04637"]). taxon_id: NCBI Taxonomy ID (default: 9606 for human). concurrency: Maximum concurrent requests (default: 10). Returns: JSON string containing the batch resolution summary, success rate, and resolved entities. |
| get_transcriptsB | Fetch transcripts, canonical isoform, and exon structures from Ensembl. Args: query: Ensembl Gene ID (e.g. ENSG00000141510) or gene symbol (e.g. TP53). species: Species name (default: 'homo_sapiens' or 'mus_musculus'). Returns: JSON string containing gene coordinates and transcript variants with exon models. |
| find_orthologsA | Identify corresponding orthologous genes across species via Ensembl. Args: query: Gene symbol (e.g. TP53) or Ensembl Gene ID (e.g. ENSG00000141510). target_species: Target species (default: 'mus_musculus', 'danio_rerio', etc.). source_species: Source species (default: 'homo_sapiens'). Returns: JSON string containing list of orthologous genes with identity and relation type. |
| get_mouse_geneA | Resolve mouse gene details and MGI identifiers (Mus musculus). Args: query: Mouse gene symbol (e.g. 'Trp53') or MGI ID (e.g. 'MGI:98834') or Ensembl ID. Returns: JSON string containing the resolved mouse gene entity with MGI identifier. |
| annotate_functionA | Fetch Gene Ontology functional annotations (MF, BP, CC) for a gene or protein. Args: query: Gene symbol (e.g. 'TP53'), alias, or UniProt accession (e.g. 'P04637'). taxon_id: NCBI Taxonomy ID (default: 9606 for human). aspect: Optional filter: 'molecular_function', 'biological_process', or 'cellular_component'. limit: Maximum number of annotations per aspect (default: 25). Returns: JSON string containing the functional profile structured into MF, BP, and CC with evidence codes. |
| get_go_termB | Fetch metadata and functional definition for a Gene Ontology (GO) term ID. Args: go_id: Gene Ontology identifier (e.g. 'GO:0006915'). Returns: JSON string containing term name, definition, aspect, and synonyms. |
| get_pathwaysB | Fetch biological pathways involving a gene or protein from Reactome. Args: query: Gene symbol (e.g. 'TP53'), alias, or UniProt accession (e.g. 'P04637'). taxon_id: NCBI Taxonomy ID (default: 9606 for human). species: Species name (default: 'Homo sapiens'). limit: Maximum number of pathways to return (default: 20). Returns: JSON string containing the list of pathways with names, stable IDs, and Reactome links. |
| get_pathway_detailsA | Fetch detailed information for a Reactome pathway by its stable identifier. Args: st_id: Reactome pathway stable ID (e.g. 'R-HSA-5357801' for Programmed Cell Death). Returns: JSON string containing pathway name, species, summary text, and diagram availability. |
| resolve_diseaseA | Resolve a disease name, synonym, or MONDO identifier against the MONDO Disease Ontology. Args: query: Disease name (e.g. 'Li-Fraumeni syndrome', 'Breast cancer'), synonym, or ID ('MONDO:0018875'). limit: Maximum number of matches to return (default: 5). Returns: JSON string containing the list of resolved disease entities with MONDO IDs, definitions, and cross-references. |
| get_target_diseasesA | Fetch evidence-backed therapeutic target-disease associations from Open Targets Platform. Args: gene: Gene symbol (e.g. 'TP53', 'BRAF', 'EGFR') or Ensembl Gene ID ('ENSG00000141510'). limit: Maximum number of top disease associations to return (default: 10). Returns: JSON string containing associated diseases, evidence scores (0.0-1.0), and datatype evidence breakdowns. |
| get_supporting_publicationsA | Fetch authoritative supporting scientific literature from Europe PMC / PubMed. Args: query: Gene symbol (e.g. 'TP53'), disease name, or scientific query string. limit: Maximum number of peer-reviewed publications to return (default: 5). Returns: JSON string containing publication titles, journal, year, authors, abstract, and citation counts. |
| get_publication_detailsA | Fetch detailed metadata for a specific scientific paper by PMID, PMCID, or DOI. Args: identifier: Publication identifier (e.g. PMID '30514107', PMCID 'PMC6280721', or DOI '10.1038/s41586-018-0774-4'). Returns: JSON string containing full paper metadata, abstract, and direct web links. |
| build_entity_subgraphA | Construct an ontological Knowledge Subgraph centered on a biological entity (PRD-21). Connects the seed gene across multiple biological layers:
Args: query: Gene symbol (e.g. 'TP53', 'BRCA1', 'EGFR') or alias. taxon_id: NCBI Taxonomy ID (default: 9606 for human). include_pathways: Include Reactome pathway nodes and edges (default: True). include_go: Include Gene Ontology nodes and edges (default: True). include_diseases: Include Open Targets disease association nodes and edges (default: True). include_literature: Include Europe PMC publication nodes and edges (default: True). Returns: JSON string representing the connected subgraph nodes, edges, weights, and provenance. |
| traverse_biological_graphA | Enumerate graph paths from a source gene to a target entity (PRD-22). Paths follow edges drawn from curated databases; each hop reports its source database and score if the source provides one. Scores are products of source-provided weights, not probabilities. Literature edges are keyword hits and are only used when target_type is 'publication'. Args: source_query: Origin gene symbol or alias (e.g. 'TP53'). target_query: Target identifier (e.g. 'MONDO:0005575', 'R-HSA-69563') or a whole-phrase name (e.g. 'colorectal cancer'). max_hops: Maximum edges to traverse, 1 to 6 (default: 3). min_score: Minimum product of source-provided scores, 0.0 to 1.0 (default: 0.0). target_type: Optional node type filter: gene, protein, transcript, pathway, disease, publication, go_term. Returns: JSON string with paths, matched targets, and warnings. On invalid input, a JSON object with an 'error' field. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 16 tools
Each tool has a clearly distinct purpose: gene resolution, protein/transcript retrieval, GO term lookup, GO annotation, pathway retrieval, disease resolution, literature search, and graph construction/traversal. The two graph tools differ in intent (build a subgraph vs. enumerate paths), and batch_resolve_genes is an explicit batch counterpart to resolve_gene. Descriptions are detailed enough to prevent misselection.
All tool names use snake_case and follow a verb_noun pattern (e.g., get_go_term, resolve_gene, build_entity_subgraph). The consistent use of action verbs like get_, resolve_, build_, traverse_, find_, and annotate_ makes the set predictable and readable.
With 16 tools, the server is slightly above the ideal 3-15 range, but the breadth of biological data sources (genes, proteins, transcripts, pathways, GO, diseases, literature, graphs) justifies each tool. No tool appears redundant or trivial.
The tool set covers core multi-omics retrieval, including gene/protein/transcript details, orthologs, GO annotations, pathways, disease associations, literature, and graph operations. Minor gaps exist, such as explicit sequence retrieval (FASTA) or variant-level annotation, but the surface is robust for most workflows.