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Glama

trivia

by io.github.pipeworx-io

Scan Dependency

scan_dependency
Read-onlyIdempotent

Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYesnpm package name. Scoped packages (e.g. "@types/node") are accepted.
versionNoSpecific version to check (e.g., "18.3.1"). Defaults to the latest published version when omitted.

Schema Changelog

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

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description adds valuable behavioral context: it mentions the composite fan-out to two services, potential latency (5-30s for bundlephobia first measurement), graceful degradation with sources_failed, and the exact return structure. This is rich transparency.

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?

The description is a single dense paragraph with high information density. It front-loads the core purpose, then details usage, returns, and failure modes. While long, every sentence earns its place; minor structural improvements could be made with bullets, but it's still effective.

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 tool's complexity (composite sources, partial failures, latency), the description is remarkably complete. It lists all expected output fields, explains the ecosystem scope, and covers degradation behavior—everything an agent needs to invoke and interpret results correctly.

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 schema covers 100% of parameter descriptions (package name, version default). The description adds only minor context (NPM ecosystem limitation), but since schema coverage is full, a baseline of 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 identifies the tool as a composite check for 'should I add this npm package' combining deps.dev and bundlephobia data. It specifies exact data points (license, advisories, bundle size, etc.) and distinguishes itself from unrelated siblings by focusing on npm packages.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' Also provides an alternative for non-npm ecosystems ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'), giving clear usage boundaries.

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 have notably unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and polymarket_edges, polymarket_arbitrage, and bet_research heavily overlap in surfacing betting opportunities. Entity_profile, recent_changes, and compare_entities also share overlapping research scope, making misselection likely.

Naming Consistency3/5

Most names use snake_case and a roughly readable verb_noun style (resolve_entity, compare_entities, list_categories), but conventions vary: some are bare nouns (entity_profile), some are plain verbs (remember, forget), and ask_pipeworx/pipeworx_* break the pattern. It is readable overall, but not a consistent scheme.

Tool Count2/5

34 tools is far more than the 'trivia' name implies, and most of them (Pipeworx research, Polymarket analysis, memory, subscriptions) are unrelated to trivia. The set reads as an entire platform bundled together rather than a purpose-scoped server.

Completeness2/5

For the stated trivia purpose, the surface is missing core lifecycle features like quiz sessions, answer validation, or scoring; the few trivia tools are just category/reference lookups. As a general data/research server it is broad, but there are significant gaps and no coherent domain model tying the tools together.

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