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Glama

Semver

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?

Annotations indicate read-only, idempotent, non-destructive. Description adds key behavioral details: fans out to two services, returns specific fields, warns about bundlephobia latency (5-30s), explains partial failure handling (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?

Description is information-dense and well-structured, front-loading the core purpose. Slightly long but every sentence adds value; could be trimmed slightly without losing meaning.

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?

Despite no output schema, the description fully explains return values (summary block fields, per-advisory detail, links, alternative versions), addresses timing nuances, and covers error cases. Comprehensive for a complex tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers both parameters with descriptions. Description adds value by noting that scoped packages are accepted and that version defaults to latest when omitted.

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 as a composite npm package check, specifying the resources used (deps.dev, bundlephobia) and what it returns. It distinguishes itself from siblings by focusing on npm packages and mentioning that other ecosystems are handled by different 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 tells when to use: 'whenever an agent asks is X safe / popular / small or what does adding lodash cost me'. Also provides exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.'

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

A3.7/5.0
Disambiguation2/5

Several clusters overlap significantly: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve general data-query purposes, and entity_profile, compare_entities, recent_changes, and validate_claim pull from the same SEC/news/data sources in similar ways. The semver utilities are distinct, but they sit alongside unrelated prediction-market, memory, subscription, and AI-visibility tools that make the overall boundary of each tool much fuzzier.

Naming Consistency3/5

Some tools follow a clean verb_noun pattern (parse_semver, compare_semver, compare_entities, resolve_entity), but others are noun phrases (entity_profile, polymarket_edges, recent_changes) or branded/verb-first names (ask_pipeworx, deep_research, bet_research, pipeworx_trending). The mix is readable but inconsistent, with no unifying convention across the 34 tools.

Tool Count2/5

34 tools is too many for a server named Semver, whose actual semver-related surface is only a few utilities. Even viewed as a broad data platform, the count is heavy and padded with unrelated capabilities like prediction-market arbitrage, memory storage, subscriptions, and AI-visibility checks that do not belong together in one server.

Completeness2/5

As a Semver server it covers parse, compare, and range satisfaction but lacks obvious operations like version bumping/incrementing or validating a version list, making the core surface incomplete. As a general data platform the domain is unclear and the unusual mix of semver, market, memory, and marketing tools prevents any coherent completeness assessment.

Resources