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Glama

Data Gov Sg

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?

Adds beyond annotations: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed will list timeout. Annotations already declare readOnly/idempotent safe.

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?

Dense single paragraph, front-loaded with main purpose. Every sentence adds value but could be broken into bullet points for clarity.

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, but description thoroughly explains return values (summary block, advisories, links, alternative versions) and partial failure behavior. Highly complete for 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 good descriptions. Description adds value: mentions scoped packages accepted for 'package' and default behavior for 'version'. Baseline 3, slight improvement.

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?

Description clearly states composite check for npm packages with specific verb 'scan' and resource 'dependency'. Distinguishes from siblings by noting npm-only scope and mentioning alternative for other ecosystems.

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 says 'Use whenever an agent asks...' and provides when-not: '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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and the Polymarket and company-research toolsets overlap significantly (bet_research vs polymarket_edges, entity_profile vs compare_entities vs recent_changes). Even with strong descriptions, an agent can easily misselect among these near-duplicate entry points.

Naming Consistency3/5

Names are readable but mix conventions: verb_noun forms (search_datasets, query_dataset, generate_llms_txt, validate_claim) coexist with noun/adjective forms (air_quality_pm25, taxi_availability, entity_profile, polymarket_edges). There is no single predictable pattern, though the domain-prefix style for Singapore data tools is consistent.

Tool Count2/5

40 tools is far too many for a server nominally scoped to Singapore government data. The bulk of the surface is a general-purpose Pipeworx/prediction-market/research toolkit that has nothing to do with Data Gov Sg, so the actual Singapore dataset tools are buried under dozens of unrelated capabilities.

Completeness4/5

For the core data.gov.sg use case, the surface is solid: search_datasets, get_dataset, and query_dataset cover dataset discovery and retrieval, supplemented by live-data tools (weather_now, air_quality_psi, traffic_incidents, taxi_availability, uv_index). The broader Pipeworx side also includes helpful auxiliary lifecycle tools like discover, subscribe, recent_alerts, memory, and feedback, so there are no critical dead ends.

Resources