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Glama

Idigbio

by io.github.pipeworx-io

Count By Field

count_by_field
Read-onlyIdempotent

Get taxonomic/geographic specimen counts grouped by a field (top values + counts) across iDigBio. Optionally scope the counts with taxonomy/locality filters. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldYesField to group by: one of country, stateprovince, family, genus, institutioncode, basisofrecord.
genusNoOptional filter to scope the counts.
familyNoOptional filter to scope the counts.
countryNoOptional filter to scope the counts.
top_countNoNumber of top buckets to return (default 10, max 25).
scientific_nameNoOptional filter to scope the counts.

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: +[
+  {
+    "field": "country"
+  },
+  {
+    "field": "family",
+    "genus": "acer",
+    "top_count": 20
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare safe, non-destructive behavior (readOnlyHint, idempotentHint, destructiveHint=false). The description adds the context 'Keyless', suggesting no API key required, and specifies that scoping is optional. No contradictions with annotations.

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 efficiently convey purpose, scope, and key feature (optional filters). No wasted words; front-loaded with the core action.

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 count tool with good annotations, the description covers the main functionality. It mentions 'top values + counts', matching the top_count parameter. However, it does not describe the return format (e.g., JSON structure), which would improve completeness.

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 all parameters described. The description adds no new parameter details beyond restating the grouping field and optional filters, so 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 verb 'Get', the resource 'taxonomic/geographic specimen counts grouped by a field', and the scope 'across iDigBio'. It distinguishes itself from sibling tools like 'search_specimens' by focusing on counts rather than specimen records.

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

Usage Guidelines3/5

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

The description mentions optional filters but does not explicitly guide when to use this tool versus alternatives. It implies usage for aggregated counts, but lacks explicit when-to-use or when-not-to-use guidance.

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