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Glama

Washington State Open Data

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

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description discloses partial failure behavior (bundlephobia first measurement takes 5-30s, sources_failed listing), NPM-only limitation, and return structure. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, front-loading purpose, then usage, then technical details and behavior. Every sentence adds value with 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?

Despite no output schema, the description fully explains the return value: a summary block with key fields, per-advisory details, links, and alternative versions. It also covers partial failures and ecosystem scope, making it complete for effective use.

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?

Schema coverage is 100% and both parameters are described in the schema. The description repeats the parameter details but adds no new semantic information beyond what is already in the input 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 that the tool performs a composite check for adding an npm package, across deps.dev and bundlephobia. It specifies the verb 'scan' and resource 'dependency', and the scope (NPM ecosystem) distinguishes it from sibling tools like scan_competitor_ai_presence.

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?

The description explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"' and notes that other ecosystems fall under deps.dev:version directly, providing clear when-to-use and when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Washington State Open Data toolsInspect
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TDQS

A3.6/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions, but there is overlap between ask_pipeworx, ask_pipeworx_grounded, and deep_research, all serving data retrieval. Also, prediction market tools (e.g., bet_research, polymarket_arbitrage) are closely related, causing potential ambiguity.

Naming Consistency2/5

Naming conventions are inconsistent: mostly snake_case (ask_pipeworx, entity_profile) but includes camelCase (ai_visibility_check). No clear pattern, mixing verb_noun and other structures.

Tool Count1/5

33 tools is excessive for a server named 'Washington State Open Data'; only 3 tools (datasets, metadata, query) are directly relevant, while the rest are unrelated Pipeworx/Polymarket tools. The count does not match the server's stated scope.

Completeness1/5

The tool set severely misaligns with the server name: it covers general data and prediction markets rather than Washington State Open Data. Only basic query and metadata tools exist for the stated domain, leaving many common dataset operations (e.g., CRUD) missing and providing entirely irrelevant functionality.

Resources