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Glama

Movies

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

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint/idempotentHint annotations, the description discloses partial failure behavior, latency expectations (5-30s for first bundlephobia measurement), and the sources_failed field. It also details what the tool returns, giving agents a clear behavioral model.

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?

Though the description is longer than typical, every sentence contributes: purpose, use cases, output fields, ecosystem scope, and failure handling. It is front-loaded with the core question and uses dashes and structured clauses to maintain scannability.

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 enumerates the return values (summary block fields, per-advisory details, links, alternatives). It also covers ecosystem limitations, partial failure modes, and timing, making it self-sufficient for a complex composite tool.

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 input schema already documents both parameters comprehensively (package name, version default behavior), so the description adds little new semantic detail. However, it does contextualize the parameters within the composite check by showing how they feed into the fan-out, which is marginal added value.

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 defines the tool as a composite 'should I add this npm package to my project' check, explicitly naming the data sources (deps.dev, bundlephobia) and the specific analysis dimensions. It distinguishes itself from siblings by focusing on npm package risk, size, and adoption, with a clear verb-resource structure.

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?

The description explicitly states when to use the tool ('Use whenever an agent asks...') and gives concrete examples like 'what does adding lodash cost me'. It also provides an explicit exclusion and alternative for non-NPM ecosystems, directing to 'deps.dev:version directly', which is excellent guidance.

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

A3.5/5.0
Disambiguation2/5

Many tools have heavily overlapping purposes—ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, while entity_profile, compare_entities, and recent_changes all pull overlapping company data. The server is named 'Movies' but only 4 of 35 tools relate to movies, making it impossible to infer what the tool set is actually for.

Naming Consistency2/5

Naming is a mix of verb_noun (search_movies, get_tv_schedule), bare nouns (remember, recall), brand prefixes (pipeworx_*, polymarket_*), and ad-hoc verbs (ask_pipeworx vs validate_claim). There is no consistent convention; even the pipeworx family uses ask_ vs grounded vs beta suffixes that don't follow a predictable pattern.

Tool Count2/5

35 tools is heavy for a single server, and for a 'Movies' server it is extreme overkill since the vast majority have nothing to do with movies. Even if the intent was a general data/betting server, 35 tools exceed the upper bound of the well-scoped range and would be better split into focused servers.

Completeness2/5

As a movies/TV server it is severely incomplete: there is no get_movie, no reviews, no watchlist, no person/actor search—only search_movies, search_tv_shows, get_tv_show, and get_tv_schedule. If instead the domain is Pipeworx data, coverage is better but still lacks mutation tools (e.g., no create/update for subscriptions beyond subscribe/unsubscribe) and the movie tools become dead weight.

Resources