gene_diseases"What diseases is gene [NCBIGene:N] associated with" / "diseases linked to gene [X]" / "TP53 disease associations" — fetch diseases associated with a gene ID (NCBIGene:N format). Use for gene-to-disease lookups in rare-disease research, variant prioritization. Example ID: NCBIGene:7157 (TP53).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety and behavior are clear. The description adds context about input format (NCBIGene:N) and use cases, which is beneficial. No contradictions are present. The description does not detail rate limits or pagination, but given the strong annotations and simple operation, it is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, with no wasted words. It front-loads the purpose with typical question forms, then provides context and an example. Every sentence adds value, making it easy for an agent to quickly understand the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, output schema exists), the description covers the fundamental aspects: purpose, input format, use cases, and an example. It does not mention error handling or missing gene IDs, but the output schema likely covers return structure. For a basic lookup tool, it is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It specifies the 'id' parameter format as 'NCBIGene:N' and gives an example (NCBIGene:7157). However, the input schema example shows '2309' without the prefix, creating a slight inconsistency. The description adds meaning but could be more precise about the prefix requirement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches diseases associated with a gene ID, provides typical query phrasings ('What diseases is gene...'), and gives an example with NCBIGene:7157 (TP53). The verb 'fetch' and resource 'diseases associated with a gene ID' are specific and unambiguous. While no explicit sibling differentiation is given, the purpose is distinct enough given the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use for gene-to-disease lookups in rare-disease research, variant prioritization', providing explicit context. However, it does not mention when NOT to use this tool or suggest alternatives like disease_phenotypes for the reverse direction. The guidance is present but lacks exclusionary or comparative advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have distinct purposes, but there is overlap among the Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_kalshi_spread) and between ai_visibility_check and scan_competitor_ai_presence. Detailed descriptions help, but some tools could still be confused.
All tool names follow a consistent verb_noun snake_case pattern (e.g., ai_visibility_check, resolve_entity, validate_claim). No mixing of conventions.
With 27 tools spanning HPO, data queries, betting, memory, and utilities, the server is over-scoped. It aggregates multiple domains that would be better split into separate servers. The count feels excessive for a coherent set.
Within each domain (HPO ontology, Pipeworx data, Polymarket betting, etc.), the tool surface is reasonably complete. However, the overall server lacks a single clear purpose, making it hard to assess completeness holistically.