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Glama

Data Nl

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?

Discloses beyond annotations: fans out across deps.dev and bundlephobia, partial failures degrade gracefully with sources_failed field, and first measurement can take 5-30s. No contradiction with annotations (readOnlyHint, idempotentHint, etc.).

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 packed with useful information but somewhat lengthy. It is well-structured with clear sections (purpose, usage, returns, ecosystem, behavior). Every sentence adds value, though slight trimming could improve conciseness.

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 thoroughly explains return format (summary block, advisories, links, versions). Covers parameter details, ecosystem scope, timing, and fallback behavior, making it fully actionable for an agent.

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%, so baseline is 3. The description adds context: explains that version defaults to latest and scoped packages are accepted, enhancing understanding beyond the parameter descriptions.

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 evaluates npm packages for safety, popularity, and size, with a specific verb ('check') and resource ('npm package'). It distinguishes from siblings by specifying NPM ecosystem 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?

Explicitly states when to use ('is X safe / popular / small', 'what does adding lodash cost me') and when not to use (other ecosystems, which fall under deps.dev:version). Provides clear context for selection.

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
Disambiguation2/5

Several tool families have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates (the beta is currently identical), and the six polymarket_* tools plus bet_research heavily overlap in prediction-market analysis. ai_visibility_check and scan_competitor_ai_presence also serve the same core function. An agent would frequently need to read lengthy descriptions to pick the right one, and could easily misselect.

Naming Consistency3/5

Most tools follow a readable snake_case pattern, but the style is mixed: some are verb-first (ask_pipeworx, search_datasets, resolve_entity), some are domain-prefixed nouns (polymarket_edges, pipeworx_feedback), and a few are bare nouns or adjective-noun phrases (dataset, entity_profile, recent_alerts). It is not chaotic, but there is no single predictable verb_noun convention across the set.

Tool Count2/5

With 34 tools, this is above the 25+ threshold considered too many for a coherent toolset. The count is inflated by near-duplicate families (three ask_pipeworx variants, six polymarket tools) that could reasonably be consolidated. While the server covers a broad domain, the number of top-level choices creates unnecessary selection burden for agents.

Completeness4/5

For a read-focused data/research gateway, the surface is quite complete: general lookup, grounded verification, deep research, entity resolution, comparisons, change tracking, memory, subscriptions, and feedback are all present. Minor gaps exist, such as no direct fetch-by-URI tool for the pipeworx:// citations that other tools return, and the Dutch open-data tools are strictly read-only. These are workarounds rather than dead ends.

Resources