resolve_termMap a free-text term or everyday synonym to its canonical MeSH descriptor(s) — the preferred heading to use when searching PubMed. Tries an exact descriptor match first; if none, falls back to a fuzzy contains match (flagged fuzzy). Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | A term or synonym to canonicalize, e.g. "aspirin", "high blood pressure", "diabetes". | |
| limit | No | Max matches (default 10, max 25). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "text": "aspirin"
+ },
+ {
+ "limit": 15,
+ "text": "high blood pressure"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral details beyond annotations, such as the two-step matching strategy (exact then fuzzy) and the fact that it is keyless, without contradicting the read-only and idempotent hints.
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 extremely concise, with two sentences: the first defines the purpose and the second explains the matching algorithm. No redundant information.
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?
The description covers the core behavior and usage, but lacks details on the return format (no output schema). However, given the simple mapping function and existing schema/annotations, it is mostly 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?
The schema already covers both parameters with descriptions, but the description adds algorithmic context (exact then fuzzy fallback) that enhances understanding, though not necessary for parameter usage.
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 tool maps free-text terms to canonical MeSH descriptors, distinguishing it from siblings like resolve_entity and get_descriptor by specifying the resource and verb.
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 explains when to use the tool (mapping synonyms to PubMed headings) and its matching behavior, but does not explicitly mention when not to use it or contrast with alternatives like search_descriptors.
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.