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Glama

Digimon

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. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description details the tool's composition (deps.dev and bundlephobia), return fields, and a critical behavior: bundlephobia's first measurement may take 5-30 seconds and can time out, with failures reflected in sources_failed. No contradiction with annotations.

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 paragraph but packs essential information efficiently, starting with the core purpose. While slightly dense, it avoids unnecessary words; a bullet-list structure could improve readability but does not detract from content quality.

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 fully covers the return structure (summary fields, per-advisory details, links, alternative versions), ecosystem constraints, and graceful degradation. It is complete for the tool's complexity.

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%, so baseline is 3; the description adds value by clarifying that scoped packages are accepted for 'package' and that 'version' defaults to latest. It also provides context on how parameters relate to the composite check.

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's purpose: a composite check for deciding whether to add an npm package, covering license, advisories, and bundle size. It is distinct from sibling tools, none of which offer this specific npm-focused analysis.

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 describes when to use the tool ('is X safe / popular / small' questions) and provides a clear exclusion: non-npm ecosystems should use deps.dev:version directly. It also notes partial failure behavior, aiding correct invocation.

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

All Digimon toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

Multiple tools overlap significantly: the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are near-identical routers, and the six polymarket_* tools plus bet_research cover overlapping prediction-market territory. The server name 'Digimon' adds confusion since almost all tools are unrelated to Digimon, making it hard for an agent to tell what this server is actually for.

Naming Consistency3/5

All names use snake_case, which is consistent, but the pattern is mixed: some tools start with verbs (get_digimon, search_digimon, list_subscriptions, validate_claim), others with nouns (entity_profile, polymarket_arbitrage, pipeworx_trending), and a few are bare verbs (remember, recall, forget). This irregularity makes the naming less predictable than a uniformly verb-first set.

Tool Count2/5

With 33 tools, the server exceeds the 'too many' threshold and feels bloated. The tools span Digimon data, Pipeworx research, Polymarket betting, memory, subscriptions, and even llms.txt generation—an incoherent grab-bag that doesn't form a focused, well-scoped toolkit.

Completeness4/5

For the broad data-research and prediction-market domain implied by the majority of the tools, the surface is impressively comprehensive: universal routing, grounded answers, deep research, entity resolution, company/drug profiles, comparisons, claim validation, memory, subscriptions, discovery, and multiple specialized Polymarket tools. Minor gaps exist (e.g., no direct order execution on Polymarket), but ask_pipeworx routes to thousands of sources, covering most needs.

Resources