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Glama

Census Trade

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

A4.8/5.0
Behavior5/5

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

Discloses behavioral details beyond annotations: fans out across two services, partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed list on timeout. No contradiction with annotations (all readOnly, openWorld, idempotent, non-destructive).

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 front-loaded with the core purpose. Each sentence adds value, covering use cases, return details, and edge cases. Could be slightly more concise, but well-structured.

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 lists return fields (summary block, advisories, links, alternative versions) and explains partial failure behavior. It covers ecosystem scope and timing caveat, making it fully complete for agent use.

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% for both parameters. The description adds extra context: scoped packages accepted for 'package' and default to latest version for 'version', which adds meaningful semantic value beyond 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 the tool performs a composite check for adding npm packages, covering license, advisories, bundle size, etc. It uses specific verbs ('scan', 'fans out') and distinguishes itself from sibling tools by its unique functionality.

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 tells when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also specifies scope (NPM ecosystem only v1) and directs to alternative for other ecosystems via 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.

All Census Trade toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

There is substantial overlap among the question-routing tools: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, and validate_claim all funnel natural-language queries into similar source lookups. ai_visibility_check and scan_competitor_ai_presence also cover nearly the same capability, making tool selection genuinely ambiguous.

Naming Consistency3/5

All names are snake_case and many follow a verb_noun pattern (census_exports, list_subscriptions, validate_claim), but the convention drifts with noun-first names like entity_profile, pipeworx_trending, and recent_alerts, and bare verbs like remember, recall, forget, and subscribe. It is readable but not a tight, predictable pattern.

Tool Count2/5

35 tools is too many for a server named 'Census Trade' when only 4 of them actually relate to Census trade data. The remaining 31 form an unrelated general-purpose data, prediction-market, memory, and subscription toolkit, making the surface feel bloated and badly scoped relative to the server's apparent purpose.

Completeness3/5

The Census trade core covers exports, imports, trade balance, and monthly trends, which handles the central queries, but there is no HS-code catalog, country metadata, or state/port-level breakdown, leaving notable gaps for a trade-data domain. If judged as the broad Pipeworx gateway it appears to actually be, coverage is richer, but that contradicts the server name and weakens overall coherence.

Resources