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Glama

Nass

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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds valuable behavioral details: internal composite fan-out, partial failures with graceful degradation, bundlephobia's first measurement delay of 5-30s, and sources_failed field.

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 well-structured with critical info front-loaded. Each sentence adds value, covering purpose, usage, output format, ecosystem scope, and failure behavior. Slightly dense but without fluff.

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 lists all return fields and details. It also covers partial failures, timing, and ecosystem scope. For a composite tool that aggregates complex data, this is comprehensive.

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?

Input schema has 100% description coverage. The description does not add significant meaning beyond the schema's parameter descriptions. It mentions that version defaults to latest, which is already in the schema.

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's a composite check for evaluating an npm package, fanning out across deps.dev and bundlephobia. It distinguishes itself from all sibling tools, none of which are npm package evaluation tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also indicates NPM ecosystem only, with other ecosystems handled by deps.dev:version directly. Lacks explicit 'when not to use' but provides clear context.

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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially the specialized ones like nass_crop_progress and bet_research. However, there is potential confusion between ask_pipeworx, ask_pipeworx_grounded, deep_research, and bet_research, as they all involve querying structured data. The descriptions do attempt to differentiate them, but the overlap could still cause misselection.

Naming Consistency4/5

Tool names consistently use lowercase with underscores and follow patterns like descriptive prefixes (nass_, pipeworx_, polymarket_) and action verbs (generate_, resolve_, validate_). Minor inconsistencies exist, such as 'search_within' versus 'discover_tools', but overall the naming is predictable and clear.

Tool Count3/5

With 36 tools, the server is on the heavier side but still fits within a reasonable range for a comprehensive data platform. The tools cover a wide variety of domains (agriculture, finance, prediction markets, memory, etc.), and each tool appears to add value. However, the count is borderline high, and some tools like ask_pipeworx could potentially replace many specialized ones.

Completeness4/5

The tool surface is comprehensive for its purpose: answering questions over structured data with support for analysis, comparison, monitoring, and feedback. There are minor gaps, such as the lack of a direct update mechanism for subscriptions beyond canceling, but the core CRUD operations are covered. The inclusion of meta-tools like ask_pipeworx and deep_research fills many potential gaps.

Resources