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Glama

Mime

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral details: self-aggregating from CF analytics-engine, no PII, caching policy (5min-1h). No contradiction.

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?

Three compact sentences with bullet points for use cases. Every sentence adds unique value with no redundancy.

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 a simple single-parameter tool with rich annotations, the description is complete: it explains what it returns, how it works, and important notes (no PII, caching). No output schema needed.

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 enum parameter fully described. The description adds context on window semantics (short for hot, long for steady-state), enhancing the schema's meaning.

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 what other AI agents are calling on Pipeworx, including top tools, packs, and call volume. It uses specific verbs (returns, discovering, confirming, seeing) and distinguishes from siblings by focusing on trending metrics.

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?

Three explicit use cases are listed, providing clear context for when to use the tool. While it doesn't explicitly mention when not to use, the guidance is strong enough for agents to decide alignment.

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

A4/5.0
Disambiguation3/5

Several tools have overlapping purposes, such as the ask_pipeworx family (standard, beta, grounded) and the multiple Polymarket analysis tools (edges, arbitrage, fill_risk, edge_tracker, kalshi_spread, bet_research). The detailed descriptions help differentiate them, but an agent could still misselect between deep_research vs ask_pipeworx or polymarket_edges vs polymarket_arbitrage.

Naming Consistency3/5

Naming patterns are mixed: many tools use verb_noun (discover_tools, validate_claim, compare_entities), but others are noun_noun (entity_profile, polymarket_edges), single verbs (remember, recall, forget), or unusual forms (extension_for, search_within, ask_pipeworx). The polymarket_ prefix and ask_pipeworx family provide some consistency, but overall the style is not uniform.

Tool Count2/5

33 tools is a large number for the server's scope. While it covers many domains (data querying, entity research, Polymarket analysis, subscriptions, memory, utilities), there is redundancy: three ask_pipeworx variants and six Polymarket-specific tools inflate the count. Several tools could be merged or dropped without losing functionality, making the set feel heavier than necessary.

Completeness4/5

The toolset covers its core domains well: data querying (ask_pipeworx, deep_research), entity resolution (resolve_entity, entity_profile, compare_entities), Polymarket analysis (research, arbitrage, risk, edges, tracking, cross-venue), subscriptions (subscribe/unsubscribe/list/alerts), memory (remember/recall/forget), and utilities (MIME lookup, dependency scan). Minor gaps exist, such as no direct fetch tool for pipeworx:// citation URIs and no update operation for subscriptions, but these are not critical.

Resources