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Glama

Utah 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

A4.4/5.0
Behavior4/5

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

The description adds behavioral context beyond annotations, such as partial failures degrading gracefully and bundlephobia's first measurement taking 5-30 seconds. It does not contradict the readOnlyHint and idempotentHint 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 a single informative paragraph that front-loads the main purpose and then details return fields and behavior. It is concise but could benefit from slight structuring for readability.

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 the tool's complexity and lack of output schema, the description thoroughly explains return fields (summary block, per-advisory, links, alternatives) and handles edge cases like partial failures. It is complete for effective usage.

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?

With 100% schema coverage, the description adds value by clarifying that scoped packages are accepted and that version defaults to latest. This goes beyond the schema's basic 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's purpose as a composite check for npm packages, combining deps.dev and bundlephobia data to answer 'should I add this package?' It distinguishes from sibling tools by focusing on npm dependency analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly specifies when to use: 'whenever an agent asks is X safe / popular / small or what does adding lodash cost me'. It also notes the NPM-only limitation for v1 and directs to deps.dev for other ecosystems, providing clear context even without naming specific sibling tools.

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

All Utah Open Data toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

The toolset contains several near-duplicate clusters: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route the same kinds of questions, and the six polymarket tools have heavily overlapping scopes. An agent would frequently struggle to pick the right variant despite the detailed descriptions.

Naming Consistency4/5

Names consistently use lowercase snake_case with a verb-first or domain-prefixed pattern (ask_pipeworx, resolve_entity, validate_claim, polymarket_arbitrage). Minor deviations like bare nouns (datasets, metadata) are acceptable but not perfectly uniform.

Tool Count2/5

34 tools is far beyond what a Utah Open Data server needs; only 3 tools actually relate to the named domain. The rest form a sprawling general-purpose Pipeworx/prediction-market platform, making the surface feel bloated for its stated purpose.

Completeness3/5

For the actual Utah Open Data catalog, datasets/query/metadata is a complete read-only surface. But for the broader Pipeworx functionality the set actually delivers, there are odd gaps (no account management beyond subscriptions) and many irrelevant tools, so overall coverage is uneven.

Resources