get_descriptorGet full detail for a MeSH descriptor by its ID (e.g. "D003920" = Diabetes Mellitus): its preferred label, entry terms (synonyms MeSH indexes under it), allowable qualifiers (subheadings like "drug therapy", "epidemiology"), and related see-also headings. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| mesh_id | Yes | A MeSH descriptor ID, e.g. "D003920" (Diabetes Mellitus) or "D001241" (Aspirin). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "mesh_id": "D003920"
+ },
+ {
+ "mesh_id": "D001241"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive, and open-world. The description adds value by listing the specific fields returned and noting the tool is 'Keyless' (no authentication). For a simple retrieval tool, this 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?
Two sentences, front-loaded with action and resource. Every sentence adds meaningful information without 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?
No output schema, but the description covers the main return fields. It does not mention error handling or what happens with invalid IDs, but for a simple get-by-ID tool, the description is largely 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 100%, so baseline is 3. The description adds context beyond the schema by providing examples and clarifying the format of the mesh_id parameter (e.g., 'D003920' = Diabetes Mellitus).
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 the action ('Get'), the resource ('MeSH descriptor'), and what is returned (preferred label, entry terms, qualifiers, see-also). The example ID 'D003920 = Diabetes Mellitus' and the phrase 'by its ID' differentiate it from sibling search tools like search_descriptors.
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 implies use when you have a specific MeSH ID, providing examples of ID format. However, it does not explicitly state when not to use it (e.g., use search_descriptors if you don't have an ID).
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.
The three ask_pipeworx variants are a genuine confusion risk — ask_pipeworx_beta is currently described as identical to ask_pipeworx, and ask_pipeworx_grounded differs only in extraction strictness. search_descriptors and resolve_term also overlap heavily (both map a term to MeSH descriptor IDs). The six Polymarket tools are differentiated by rich descriptions but still form a dense cluster where misselection is plausible.
Most tools follow a clean verb_qualifier pattern (ask_, bet_, compare_, resolve_, search_, validate_) with the brand as a namespace (ask_pipeworx, pipeworx_trending, polymarket_*). Minor deviations exist: pipeworx_feedback and pipeworx_trending lead with a noun, the memory trio (remember, recall, forget) are bare verbs, and the ask_pipeworx family uses a brand name rather than a resource noun — but the overall pattern stays predictable and readable.
At 34 tools this exceeds the threshold where a set feels heavy, and the bloat is visible: three near-identical ask_pipeworx routers and a six-tool Polymarket suite dominate. Several tools (generate_llms_txt, scan_dependency, bet_research) feel like accreted one-offs rather than part of a coherent surface. The broad platform purpose justifies some breadth, but the count is inflated by redundant variants.
The core purpose — universal access to authoritative structured data — is well covered via the router, grounded mode, deep_research, entity/profile/compare/validate wrappers, and discovery tools. The subscription lifecycle (subscribe/unsubscribe/list/alerts) and memory (remember/recall/forget) have no dead ends. Minor gaps exist: non-polymarket prediction-market workflows lack the depth of the Polymarket cluster, and the single-purpose niche tools fit awkwardly, but agents won't hit dead ends.