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Glama

Ripe Stat

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

A4.8/5.0
Behavior5/5

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

The description goes beyond the read-only and idempotent annotations by detailing graceful degradation: bundlephobia's first measurement may take 5-30s and sources_failed lists timeouts. It also describes the return structure (summary, advisories, links, alternative versions), providing full behavioral 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 front-loaded with the core purpose and efficiently covers usage, behavior, and limitations. While lengthy, every sentence provides necessary context without redundancy. The structure is logical and easy to parse.

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 check, multiple data sources, partial failures) and the absence of an output schema, the description is remarkably complete. It explains inputs, outputs, behavior, edge cases, and graceful degradation, leaving no critical gaps.

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 descriptions for both parameters. The description adds value by noting that scoped packages are accepted for 'package' and that 'version' defaults to the latest when omitted, enriching the schema's minimal 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 the tool's composite purpose ('should I add this npm package to my project' check) and lists the data sources (deps.dev and bundlephobia). It distinguishes from siblings by specifying the NPM ecosystem scope and referencing alternative tools for other ecosystems.

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?

Explicit usage guidance is provided: 'Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me''. It also clarifies limitations ('NPM ecosystem only in v1') and directs to alternative methods for other package managers.

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
Disambiguation4/5

Most tools have clearly distinct purposes, especially in networking and domain-specific queries. However, the multiple `ask_pipeworx` variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and `deep_research` have overlapping functionality, which could cause occasional misselection.

Naming Consistency2/5

Tool names use inconsistent conventions: some are concatenated (geoloc, whois), some use underscores (abuse_contact, as_overview), and some are multi-word (scan_competitor_ai_presence, ask_pipeworx_grounded). No consistent pattern across the set.

Tool Count3/5

39 tools is high for a single server, but justified by the broad scope covering networking, finance, prediction markets, AI visibility, and more. However, many tools are meta-tools for the Pipeworx ecosystem, making the set feel slightly bloated.

Completeness5/5

The server covers an exceptionally wide range of domains comprehensively, with tools for networking, company data, FDA datasets, prediction markets, weather, news, and more. It includes memory and subscription management, leaving few obvious gaps for its stated data-retrieval purpose.

Resources