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Glama

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds valuable behavioral context: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed list. 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 dense paragraph, informative but somewhat packed. It effectively uses front-loading but could benefit from bullet points for readability. Still concise and to the point.

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 no output schema, the description fully covers return values (summary block fields, per-advisory detail, links, alternative versions). It also explains partial failure behavior, timing caveats, and ecosystem limitations, making it 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.

Parameters3/5

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

Schema coverage is 100%, so parameters are well-documented in schema. The description adds minor nuance (scoped packages accepted, default version behavior) but does not significantly extend schema meaning.

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 is a composite check for npm packages, combining deps.dev and bundlephobia data to answer whether to add a package. It uses specific verbs like 'scan' and 'checks', and distinguishes from siblings by focusing on npm dependency evaluation.

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 tells when to use: when an agent asks about safety, popularity, or size of an npm package. It also clearly scopes to NPM ecosystem and directs other ecosystems to deps.dev:version, providing clear alternatives.

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

A3.8/5.0
Disambiguation3/5

Several tools overlap in purpose, especially the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) which all answer factual questions but differ in grounding/depth. The beta version is explicitly identical to the stable one currently, creating confusion. The Polymarket analysis tools also have overlapping scopes, though their descriptions help differentiate them. Overall, most tools have distinct roles but the heavy overlap in the query router cluster makes misselection a real risk.

Naming Consistency3/5

Tool names mix several conventions: bare verbs (remember, subscribe, forget), verb_noun (compare_entities, resolve_entity), noun_phrase (entity_profile, pipeworx_feedback), and a brand-prefixed family (ask_pipeworx*, polymarket_*). While some prefixes are consistent, the overall pattern is inconsistent and not predictable. Names are readable but do not follow a single style.

Tool Count2/5

With 33 tools, the server is heavily over-scoped for its name 'Unpaywall', which implies a narrow open-access search utility. Even though the actual functionality is broad, the tool count is excessive and will overwhelm agents. Many tools (llms_txt generation, dependency scanning, memory) are unrelated to the core data-query function.

Completeness4/5

For the actual domain revealed by the descriptions—a comprehensive data research and prediction-market gateway—the tool surface is quite complete: it covers discovery, retrieval, grounding, entity resolution, comparison, validation, subscriptions, memory, and prediction-market analysis. Minor gaps exist (e.g., no subscription update tool, no direct tool to list all data packs), but agents can work around them. If the domain is strictly 'Unpaywall/open access', it's severely incomplete, but the descriptions clearly indicate a broader scope.