get_specimenGet the full normalized record for a single iDigBio specimen by its uuid. e.g. uuid "0746b188-c390-4ab1-bd20-5489a9c6c33c" (a Puma concolor record). Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| uuid | Yes | iDigBio record uuid, e.g. "0746b188-c390-4ab1-bd20-5489a9c6c33c". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "uuid": "0746b188-c390-4ab1-bd20-5489a9c6c33c"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds context that it returns a 'full normalized record' and provides an example, but does not significantly extend beyond 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?
Two sentences, no wasted words. First sentence states core purpose. Second provides example and key attribute. Efficient and front-loaded.
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 retrieval tool with one parameter and no output schema, the description covers purpose, gives an example, and mentions keyless access. It omits return format but annotations provide safety and idempotency info. Reasonably 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 covers the uuid parameter with 100% description coverage. Description adds an example of a uuid and the note 'Keyless', but this does not add substantial meaning beyond the schema 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?
Description clearly states 'Get the full normalized record for a single iDigBio specimen by its uuid', providing a specific verb and resource. It distinguishes from sibling tool 'search_specimens' which returns multiple specimens.
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?
Description implies usage for a single specimen by uuid but does not explicitly guide when to use this tool versus alternatives like 'search_specimens'. No exclusions or when-not-to-use guidance provided.
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 route to the same underlying Pipeworx engine (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research), and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) and the entity-investigation tools (entity_profile, compare_entities, recent_changes, resolve_entity) also have heavily overlapping purposes that an agent could easily confuse.
Most tools use snake_case, but the set mixes verb-first names (get_specimen, search_specimens, resolve_entity, validate_claim) with noun-first names (entity_profile, recent_alerts, polymarket_edges, pipeworx_trending). The polymarket_ and pipeworx_ prefixes give some internal consistency, but the overall pattern is not uniform.
34 tools is heavy for a server named Idigbio, especially since only 3 of the 34 tools (count_by_field, get_specimen, search_specimens) actually relate to iDigBio specimen data. The remaining 31 are a sprawling Pipeworx/prediction-market/marketing/memory toolkit, making the tool count mismatched with the server's apparent identity.
The Pipeworx side is quite complete: querying, grounded answers, deep research, entity profiles, comparisons, claim validation, subscriptions, memory, and feedback are all present. However, the iDigBio side, which the server name advertises, is only minimally covered with search/get/count and lacks any collection or media download operations, so the overall surface has notable gaps relative to the server's stated focus.