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Glama

Idigbio

by io.github.pipeworx-io

Search Specimens

search_specimens
Read-onlyIdempotent

Search ~150M digitized natural-history museum specimen records (plants, animals, fossils) from US collections via iDigBio. Filter by any combination of taxonomy and locality. At least one filter is required. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
genusNoGenus, e.g. "acer", "quercus", "puma".
limitNoMax records to return (default 10, max 25).
familyNoFamily, e.g. "felidae", "sapindaceae".
countryNoCountry, e.g. "united states", "canada".
recorded_byNoCollector / recorded-by name, e.g. "macginitie".
state_provinceNoState or province, e.g. "florida", "wyoming".
scientific_nameNoScientific name, e.g. "puma concolor", "quercus alba". Matched case-insensitively.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "limit": 10,
+    "scientific_name": "puma concolor"
+  },
+  {
+    "country": "united states",
+    "genus": "quercus",
+    "limit": 20
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive hints. The description adds context: it is keyless (no auth), searches a large dataset (~150M), and requires at least one filter. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with high information density: first sentence covers purpose and scope, second covers filters and constraint. No wasted words, well front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with no output schema, the description covers key behaviors: data source, size, required filters, and keyless access. Lacks details on pagination beyond limit, but examples in schema help. Minor gap prevents a 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for each parameter. The description groups parameters into taxonomy and locality categories, adding slight semantic value, but mostly reiterates filtering capability. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches ~150M natural-history museum specimen records from US collections via iDigBio, using a specific verb ('Search') and resource. It distinguishes from sibling tools like 'get_specimen' by focusing on multiple records and taxonomic/locality filtering.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly requires at least one filter, guiding appropriate usage. It implies use for filtered batch queries but does not explicitly state when not to use or name alternatives, though siblings provide clear contrast.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count2/5

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.

Completeness3/5

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.

Resources