get_common_namesGet vernacular (common) names in all languages for a TSN from ITIS, e.g. "Bobcat", "lynx roux". Pass a TSN from search_taxa. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| tsn | Yes | Taxonomic Serial Number, e.g. 180582. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "tsn": 180582
+ },
+ {
+ "tsn": "183654"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true, which cover safety and behavior. The description adds that it returns names 'in all languages' and gives examples. This adds mild context but does not contradict annotations. The description's contribution is adequate given the rich annotations.
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 two sentences: the first states the core functionality and includes concrete examples; the second provides usage guidance and a keyless note. Every sentence is informative and front-loaded, with zero wasted words.
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?
For a simple lookup tool with one parameter, no output schema, and no nested objects, the description is complete. It explains the tool's purpose, input source (TSN from search_taxa), and scope (all languages). No additional details are needed for correct 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?
The single parameter 'tsn' is fully described in the input schema with type, description, and examples. The description reiterates 'Taxonomic Serial Number, e.g. 180582', adding no new semantics beyond what the schema provides. With 100% schema coverage, baseline 3 is appropriate.
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 verb 'Get', the resource 'vernacular (common) names', and the input 'TSN'. It provides concrete examples like 'Bobcat' and 'lynx roux', and distinguishes from sibling tools like search_taxa and get_hierarchy by focusing specifically on common names.
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 explicitly tells users to 'Pass a TSN from search_taxa', establishing a clear prerequisite and usage context. The note 'Keyless' implies no authentication is needed. However, it does not explicitly state when not to use this tool or mention alternatives, but the direct link to search_taxa is sufficient guidance.
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.
Several tools occupy overlapping roles: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded/deep_research/validate_claim all provide grounded answering, and bet_research/polymarket_edges/polymarket_arbitrage all surface betting opportunities. The descriptions are detailed, but the boundaries between routers and research modes are fuzzy enough that an agent could easily select the wrong one. The taxonomy, memory, and subscription clusters are distinct, but they are drowned out by the overlapping meta-tools.
All names use snake_case, which provides some visual consistency, but the verb_noun pattern is not consistently applied: search_taxa/get_hierarchy are clean verb_noun, while deep_research, entity_profile, polymarket_edges, and bet_research are noun-ish or reversed patterns. There is good family-level consistency within ask_pipeworx_* and polymarket_*, but the overall set mixes conventions and requires reading descriptions to infer what each tool does.
34 tools is well over the 25-tool threshold and reflects a sprawling multi-domain server spanning taxonomy, structured-data lookup, prediction markets, subscriptions, memory, and AI visibility. Each cluster may be individually reasonable, but as a single MCP surface it is too heavy and forces agents to filter through many irrelevant tools.
For the broad data-research and prediction-market purpose, the surface is fairly complete: it covers routing, grounded answers, deep multi-source research, entity profiles, comparisons, claim validation, entity resolution, subscriptions, alerts, memory, and market edge/fill checks. Minor gaps exist—no direct web-search tool, the beta router adds no current behavior, and some patent endpoints soft-fail—but agents can generally work around them.