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Glama

universities

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. Added

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses meaningful behavioral traits: it is self-aggregating, derived from an analytics engine, contains no PII, returns only (pack, tool, count), and is cached for 5min-1h depending on window. This adds real context about data provenance and freshness.

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?

The description opens with a clear purpose sentence, lists returns and use cases, then adds essential transparency details about data source, PII, and caching. Every sentence earns its place; the bullet-like use-case list improves scannability without padding.

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?

With no output schema, the description compensates by specifying what is returned (top tools, top packs, total call volume) and the data shape (pack, tool, count). It also covers caching, privacy, and the meaning of window choices, making the tool's behavior fully understandable for an agent.

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?

The only parameter (window) is already fully described in the input schema, including its default and the semantic difference between shorter and longer windows. The tool description mentions the window values but does not add meaning beyond the schema, so the baseline of 3 applies.

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 uses a specific verb ('Returns') and names the exact resource and outputs: top tools, top packs, and total call volume over a recent window. It clearly distinguishes itself from siblings by focusing on aggregate trending data from other AI agents, not a specific lookup or research tool.

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 provides an explicit 'Useful for' list with three concrete use cases, giving clear guidance on when to invoke the tool. It does not explicitly name alternatives or say when not to use it, but the use cases provide strong contextual direction.

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

A3.5/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers, and the five polymarket_* tools all deal with prediction-market edge detection and filling risk. Memory and subscription tools are clear, but the data-access and research tools require careful reading to avoid selecting the wrong entry point.

Naming Consistency3/5

All names use snake_case and many follow a verb_noun pattern such as search_universities and resolve_entity, but the set mixes product-prefixed names (polymarket_*, pipeworx_*), bare verbs (remember, recall, forget), and noun phrases (entity_profile, deep_research). The ask_pipeworx_beta suffix also introduces a naming convention not used elsewhere.

Tool Count2/5

At 32 tools, the server exceeds the heavy threshold, and almost all tools are unrelated to the apparent 'universities' domain—only search_universities matches the server name. The count might suit a broad data-research platform, but it is poorly scoped for this server's stated identity.

Completeness1/5

For the domain implied by the server name, the surface is severely incomplete: only a name/country university search exists, with no university detail, ranking, program, admissions, or comparison coverage. Agents would dead-end immediately after finding a list of universities.

Resources