gene_to_diseaseDiseases associated with a gene (NCBIGene:… or HGNC:… id).
| Name | Required | Description | Default |
|---|---|---|---|
| gene_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| total | No | Total matching diseases | |
| associations | No | Gene-to-disease associations |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "gene_id": "NCBIGene:672"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Diseases associated with a gene",
+ "properties": {
+ "associations": {
+ "description": "Gene-to-disease associations",
+ "items": {
+ "properties": {
+ "id": {
+ "description": "Association identifier",
+ "type": "string"
+ },
+ "object": {
+ "description": "Disease curie identifier",
+ "type": "string"
+ },
+ "predicate": {
+ "description": "Association predicate",
+ "type": "string"
+ },
+ "subject": {
+ "description": "Gene curie identifier",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "total": {
+ "description": "Total matching diseases",
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds the input format constraint (NCBIGene/HGNC) but does not disclose other behavioral aspects like possible errors or response characteristics.
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 a single, succinct sentence that front-loads the core behavior and includes the essential input format. Every word earns its place with no redundancy.
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 (one required parameter, output schema present), the description is mostly complete. It clearly states the input format but does not mention what the output looks like; however, the output schema presumably covers that. No critical gaps for basic usage.
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 description coverage is 0%, so the description must compensate. It does add meaning by specifying the expected format for gene_id (NCBIGene:… or HGNC:…), but it leaves ambiguity about whether bare gene symbols are accepted or how strict the format is.
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 indicates the tool returns diseases for a given gene and specifies the acceptable input formats (NCBIGene or HGNC). It is specific enough to distinguish from the reverse tool 'phenotype_to_gene', but it lacks an explicit verb like 'get' or '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 use case is implied: when you have a gene identifier and want associated diseases. However, there is no explicit guidance on when to use this tool versus alternatives such as phenotype_to_gene, nor any mention of exclusions or prerequisites.
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.
Most tools have clear, distinct purposes, especially within the same domain (e.g., Polymarket betting tools each serve a specific function). However, the multiple data-querying tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) could cause confusion despite detailed descriptions.
Many tools follow a verb_noun snake_case pattern (e.g., bet_research, compare_entities), but there are exceptions like ai_visibility_check, forget, and suggest_questions. The mix of imperative verbs and descriptive phrases creates inconsistency.
With 35 tools, the server feels overloaded. While each domain (biomedical, financial, betting) is covered extensively, the sheer number of tools likely overwhelms agents, and many tools could be merged or split into separate servers.
The tool set is comprehensive for its declared purpose, covering biomedical queries, company data, betting analysis, memory management, and more. Minor gaps exist (e.g., no tool to delete a bet, no write operations for biomedical data), but the breadth is impressive.