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Glama

Ipma Pt

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

A4.9/5.0
Behavior5/5

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

The description adds behavior beyond annotations: mentions partial failures, potential 5-30s delay for first bundlephobia measurement, and that sources_failed lists timeouts. Annotations already indicate readOnly and idempotent but description adds operational nuances.

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?

Single paragraph efficiently packs all key info: purpose, sources, usage, constraints, return structure, and edge cases. No wasted words, every sentence earns its place.

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?

For a tool with 2 params and no output schema, the description fully explains return content (summary block, advisories, links, alternatives) and error behavior. It's self-contained enough for an agent to invoke correctly.

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 already describes both parameters well (100% coverage). Description adds value by noting scoped package support and version defaulting to latest, which are not in schema descriptions.

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 it's a composite npm package evaluation tool, specifying it checks license, advisories, version history, bundle size, and ESM/tree-shake support. This differentiates it from sibling tools like scan_competitor_ai_presence and other unrelated tools.

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 advises use when an agent asks about package safety, popularity, or size impact. Also clarifies scope: NPM only in v1, with alternatives for other ecosystems. This helps the agent choose correctly.

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

All Ipma Pt toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

The ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research cluster overlaps heavily on question-routing, and the six polymarket_* tools all operate on prediction-market edges and can be confused. The ipma_*, memory, and subscription tools are distinct, but the overlapping clusters create real misselection risk.

Naming Consistency3/5

Snake_case is used throughout and there are clear prefix groups (ipma_*, ask_pipeworx, polymarket_*), but the rest mix verb-first (compare_entities, discover_tools), noun-first (entity_profile, bet_research), and bare verbs (remember, recall, forget). Readable overall, but no consistent verb_noun convention.

Tool Count2/5

36 tools is heavy, and the server name 'Ipma Pt' implies a narrow Portugal-weather service while 31 of the tools belong to a broad Pipeworx data/prediction-market platform. The scope mismatch makes the count feel bloated rather than curated.

Completeness4/5

The dominant Pipeworx domain is well covered: querying, grounded answers, deep research, entity resolution, comparison, claim validation, subscriptions, memory, and tool discovery are all present with few dead ends. Minor gaps exist (e.g., no general web search tool, thin IPMA historical/warning coverage), but agents can work around them.

Resources