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Glama

Quiver

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

A4.8/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds valuable behavioral details: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed will list timed out sources. 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 somewhat lengthy but every sentence adds value. It is front-loaded with the core purpose and key details. Could be slightly more concise, but structure is logical and effective.

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 details the return structure (summary block, per-advisory detail, links, alternative versions) and explains error handling. This is highly complete for a complex composite tool.

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. Description adds extra context: scoped packages like '@types/node' are accepted, and version defaults to latest when omitted. This goes beyond what schema provides.

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 is a composite check for npm packages covering license, advisories, version history, and bundle size. It uses specific verbs like 'fans out across deps.dev and bundlephobia' and distinguishes from sibling tools like 'deep_research' or 'validate_claim'.

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 states 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also scopes to npm ecosystem in v1 and mentions where to find other ecosystems, providing clear context and alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Quiver toolsInspect
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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with detailed descriptions. Overlapping areas like polymarket tools are well-differentiated by function (research, arbitrage, edge tracking, fill risk, cross-venue spread). The meta-tools (discover_tools, suggest_questions) further reduce ambiguity.

Naming Consistency5/5

All tool names use a consistent snake_case pattern (e.g., ask_pipeworx, bet_research, polymarket_edges). The naming is descriptive and predictable, aiding agent selection.

Tool Count3/5

34 tools is on the high side for typical MCP servers. While many are justified by the platform's breadth, the count slightly exceeds the ideal range, potentially overwhelming agents.

Completeness4/5

The tool set covers a wide array of domains (company data, drugs, prediction markets, economics, news, etc.) with CRUD-like operations for subscriptions and memory. Minor gaps exist (e.g., no direct social media or custom API tool), but overall the surface is well-matched to the server's data platform purpose.

Resources