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Get Gene Cross-References

get_gene_cross_references
Read-onlyIdempotent

Retrieve external database cross-references for a gene symbol or HGNC ID. Filter by sources like NCBI, Ensembl, UniProt, OMIM, and adjust detail level.

Instructions

Return external database cross-references for a gene (forward identifier mapping): NCBI Gene, Ensembl, UniProt, RefSeq, MANE Select, OMIM, UCSC, VEGA, CCDS, MGI, RGD, Orphanet, COSMIC, PubMed. response_mode sets the default field set: minimal=NCBI+Ensembl ids; compact (default)=the high-value ids (NCBI, Ensembl, UniProt, RefSeq, MANE Select, OMIM, CCDS); standard/full=every populated field. databases optionally filters to specific sources by field key OR friendly label (e.g. 'mane', 'ncbi', 'uniprot') and OVERRIDES the response_mode tier; an unknown key is rejected with invalid_input + did-you-mean. Resolve the gene from an id/symbol/alias first. Signature: get_gene_cross_references(query, databases=, response_mode=).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesA gene symbol (current/previous/alias, case-insensitive) or HGNC id (HGNC:1100 or 1100).
databasesNoOptional cross-reference filter: a list of field keys or friendly labels (e.g. 'ncbi', 'ensembl', 'uniprot', 'refseq', 'mane', 'omim', 'ucsc', 'vega', 'ccds', 'mgi', 'rgd', 'pubmed'). Overrides the response_mode tier; an unknown key is rejected with invalid_input + did_you_mean.
response_modeNoVerbosity: minimal | compact | standard | full (default compact).compact
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, open-world. The description adds valuable behavioral details: unknown database keys yield invalid_input+did-you-mean, databases overrides response_mode, and the signature. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and parameter highlights, but includes a signature line. Nearly all sentences add value; minor redundancy with schema examples. Overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema is provided, but the description does not explain the return format (e.g., list of objects with fields). For a complex tool with three parameters, this gap reduces completeness. However, the description covers input and behavior well.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Parameters have 100% schema coverage. The description adds meaning beyond schema by listing database options, explaining response_mode tiers (minimal, compact, standard, full), and the override behavior with databases.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool returns external database cross-references for a gene, listing many databases and explaining response_mode and databases parameters. It differentiates from sibling tools like get_gene (gene details) and resolve_gene_by_xref (reverse mapping).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions resolving the gene first and how parameters work, but does not explicitly compare to alternatives like resolve_gene_by_xref for reverse mapping. Usage context is clear but lack of when-not-to-use slightly lowers score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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