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zenquotes

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?

Annotations already cover read-only/idempotent safety. The description adds valuable behavioral context: partial failure degradation with 5-30s latency on first bundlephobia measurement, a 'sources_failed' field for timeouts, and the fact it fans out across multiple services. These go beyond annotations to set expectations about performance and failure modes.

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?

The description is dense but every sentence adds essential information: purpose, usage trigger, return structure, ecosystem scope, and failure semantics. No filler or repetition of schema fields. It is front-loaded with the core purpose and flows logically.

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 explains return values including the summary block fields, per-advisory detail, links, alternative versions, and the 'sources_failed' field. It also covers scope (NPM only), service fan-out, and latency caveats, making the tool's behavior well-rounded despite external dependencies.

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 description coverage is 100% — both parameters are already documented with their names and types. The description adds no new parameter syntax or format details beyond the schema; it reinforces that 'package' is an npm package name but does not exceed baseline 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 states the tool's function: a composite check for 'should I add this npm package to my project', fanning out across deps.dev and bundlephobia. It names the verb 'check', the resource 'npm package', and distinguishes from siblings by specifying the NPM-only scope and directing other ecosystems to deps.dev:version.

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 states when NOT to use it, mentioning that PyPI/Maven/Cargo/Go fall under deps.dev:version directly, which identifies an alternative tool.

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, and the heavy overlap between ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research is mitigated by detailed usage guidance. A few pairs like ai_visibility_check vs scan_competitor_ai_presence or discover_tools vs suggest_questions could confuse an agent, but the descriptions generally draw clear boundaries.

Naming Consistency3/5

Names are all lowercase snake_case and several families are consistent (ask_pipeworx*, polymarket_*), but the overall set mixes styles: verb_noun such as list_quotes and resolve_entity, adjectival noun phrases such as random_quote and today_quote, and bare nouns like entity_profile and recent_changes. The patterns are readable but not predictable.

Tool Count2/5

34 tools is well above the 25+ threshold for 'too many,' and the server name zenquotes suggests a much smaller quote-focused surface. The count is defensible for a broad data gateway, but as a unified server it feels overstuffed with many unrelated feature areas.

Completeness4/5

The combined surface is quite complete for its apparent scope: quote retrieval, query/research, grounding, entity resolution, comparisons, change feeds, subscriptions, memory, and prediction-market analysis are all covered. Minor gaps exist, such as no direct quote search and no explicit tool for fetching a pipeworx:// citation URI.