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Glama

H1b

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.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds rich behavioral context: fans out across two services, partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed list on timeout. Adds significant value beyond 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 moderately long but every sentence adds value. It is front-loaded with the composite purpose and ends with graceful degradation notes. Could be slightly tighter but no redundancy.

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?

No output schema exists, so the description fully explains return values: summary block with specific fields, per-advisory detail, links, alternative versions. It also covers edge cases like partial failures. With only 2 parameters, this is a complete specification.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

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

Schema coverage is 100% with descriptions for both parameters. The description adds meaning: 'NPM ecosystem only in v1' (ecosystem constraint), and the version default behavior ('Defaults to the latest published version when omitted'). This extra context makes parameter semantics excellent.

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 opens with a clear composite purpose: 'Composite should I add this npm package to my project check in ONE call', specifying the exact resources (npm package) and the action (check). It distinguishes from all sibling tools, none of which are npm-related.

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: 'Use whenever an agent asks is X safe/popular/small or what does adding lodash cost me'. Also gives clear exclusion: 'NPM ecosystem only in v1; PyPI/Maven/Cargo/Go fall under deps.dev:version directly'.

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

A3.7/5.0
Disambiguation2/5

Several tool families have fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,743 tools with only subtle differences in grounding/fan-out, and the six polymarket_* tools overlap heavily in purpose. Even with detailed descriptions, an agent would frequently need to read the full text to pick the right one, and the beta variant is admitted to be currently identical to the stable router.

Naming Consistency4/5

All tool names use snake_case with clear family prefixes (ask_pipeworx, polymarket_*, h1b_*, pipeworx_*, subscribe/unsubscribe), making the set look organized. The minor inconsistency is that some names start with an imperative verb (ask, compare, validate, scan) while others are bare nouns (entity_profile, deep_research, bet_research), so the verb_noun pattern is not universal.

Tool Count2/5

34 tools is well beyond the recommended range for an MCP server, and many are near-duplicates (three ask_pipeworx variants, six prediction-market analyzers, three memory/three subscription tools). The breadth may reflect a genuinely large data catalog, but exposing it all as top-level MCP tools makes the surface heavy and hard to navigate.

Completeness3/5

For the actual data-platform scope, coverage is solid: lookups, research, memory, subscriptions, and validation are all present. However, a direct fetch tool for the advertised pipeworx:// citation URIs is missing (deep_research even conditions citations on resources/read existing), and the server's stated H-1B identity is underrepresented with only three specialized tools.

Resources