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Glama

Cms

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.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds that the signal is derived from CF analytics-engine, no PII, and cached 5min-1h. 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.

Conciseness4/5

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

Description is informative but not overly long. Uses bullet points for use cases and additional details. Every sentence adds value.

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 one optional parameter, good annotations, and no output schema, the description explains the return (top tools, top packs, call volume) and caching. Adequate for a simple trending tool.

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?

Only one parameter (window) with enum and description. The description adds context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' Adds value beyond the schema.

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 returns 'top tools, top packs, and total call volume' over a window, with three specific use cases. It distinguishes itself from sibling tools like search or info tools by focusing on trending/recent demand.

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 (discovering hot data sources, confirming canonical tool, aligning use case). Mentions window options and their effects. Does not explicitly state when not to use, but the guidance is clear.

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

All Cms toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

Some tools have clearly distinct purposes (remember/recall/forget, subscribe/unsubscribe), but the ask_pipeworx family overlaps heavily — ask_pipeworx_beta is explicitly identical today, and ask_pipeworx_grounded/deep_research are variations on the same routing core. Polymarket tools and comparison/profile tools also have fuzzy boundaries, though detailed descriptions help agents choose.

Naming Consistency4/5

Most tools follow a lowercase snake_case verb_noun pattern (search_datasets, get_dataset, validate_claim, resolve_entity). A few deviate with bare verbs (remember, forget, recall) or noun-like names (dataset_info, entity_profile, pipeworx_trending), but the overall style is predictable and readable.

Tool Count2/5

34 tools is a heavy surface for one server, and the scope sprawls across CMS open data, general data research, prediction markets, memory storage, and subscription management. Many of these could be split into separate coherent servers, and several meta-routers (ask_pipeworx, deep_research, discover_tools, suggest_questions) overlap in purpose.

Completeness4/5

Within the broad data-research domain the set is fairly complete: search, retrieval, grounding, comparison, entity resolution, verification, subscriptions, and memory are all covered with no obvious dead ends. However, the server is named 'Cms' yet only three tools actually touch CMS datasets, leaving that narrow purpose under-covered relative to the rest.

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