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Glama

Idigbio

by io.github.pipeworx-io

Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Discloses data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h depending on window). Annotations already cover hint, but description adds valuable behavioral context 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.

Conciseness5/5

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

Front-loaded with main purpose, then bulleted uses, then technical details. Every sentence adds value, no waste.

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

Completeness5/5

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

Given the simple tool (1 param, no output schema, rich annotations), the description covers purpose, usage, transparency, and parameter semantics completely.

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

Parameters4/5

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

Schema coverage is 100% with a single parameter (window) that has enum and description. Description adds nuance: short vs long windows surface different trends, but schema already documents it well.

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 it returns top tools, packs, and call volume over a recent window, using specific verbs and resource. It distinguishes from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on what other agents are calling.

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?

Explicitly lists three use cases with bullet points (discovering hot data, confirming canonical choice, seeing alignment). Does not explicitly state when not to use, but the context is clear.

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

All Idigbio toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools are easy to confuse: ask_pipeworx, ask_pipeworx_beta (currently identical by admission), and ask_pipeworx_grounded overlap heavily, and there are five different polymarket_* tools whose discovery/scanning/edge-checking purposes blur together. The iDigBio trio is distinct, but the rest of the surface has real selection risk.

Naming Consistency4/5

Nearly all names are lowercase snake_case and mostly verb-first / logical-grouped (search_specimens, get_specimen, ask_pipeworx, forget, list_subscriptions, etc.). Minor deviations like count_by_field, pipeworx_trending, recent_changes, and bare verbs recall/forget do not seriously undermine predictability.

Tool Count2/5

34 tools is already many, and the bulk of them are unrelated to the Idigbio context—only search_specimens, get_specimen, and count_by_field are actually iDigBio tools. The apparent scope is bloated and mixed, including prediction markets, platform feedback, memory, subscriptions, and general data-research tools, so the count does not fit a server named Idigbio.

Completeness2/5

The iDigBio portion supports the core search/get/count read patterns, but the overall toolset is a grab-bag rather than a coherent domain lifecycle: most functional areas are only partially present and no consistent end-to-end workflow emerges. The server seems stretched across iDigBio, Pipeworx, prediction markets, visibility checking, and memory utilities without fully owning the obvious outputs for any one domain.

Resources