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Glama

Colorado Information Marketplace

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

A4.8/5.0
Behavior5/5

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds that the tool fans out across services, bundlephobia measurements may take 5-30s, and partial failures degrade gracefully with sources_failed listed. No contradictions.

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 paragraph that efficiently packs key information, front-loaded with purpose and use cases. While slightly long, every sentence adds value without 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 thoroughly lists all return fields (summary block, per-advisory details, links, alternative versions) and explains error handling. This fully compensates for the missing output schema.

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 descriptions for both parameters. The description adds context: package accepts scoped names, version defaults to latest, and scoped packages like '@types/node' are accepted, providing extra clarity.

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 npm packages covering safety, popularity, and size via deps.dev and bundlephobia. It distinguishes itself from sibling tools by specifying 'NPM ecosystem only in v1' and contrasting with broader deps.dev queries.

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 clarifies that other ecosystems fall under different tools. It also warns about potential delays with bundlephobia's first measurement.

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

All Colorado Information Marketplace toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

The tool set has several overlapping clusters: ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded, entity_profile / recent_changes / compare_entities, and a half-dozen prediction-market tools. The descriptions are unusually detailed and mostly steer an agent correctly, but ask_pipeworx_beta is currently identical to ask_pipeworx and the prediction-market tools still require careful reading to pick the right one.

Naming Consistency3/5

All names are lowercase snake_case, but the conventions vary: many are verb_noun (resolve_entity, compare_entities, validate_claim), some are bare nouns (datasets, metadata, query), some are bare imperatives (remember, forget, subscribe), and there are separate prefix families like polymarket_* and pipeworx_*. It is readable and consistent in style, but not a single predictable pattern.

Tool Count2/5

34 tools exceeds the 25+ threshold for 'too many,' and the set is not tightly scoped: only datasets, metadata, and query directly relate to the stated Colorado Information Marketplace purpose. The bulk are Pipeworx data-research, prediction-market, memory, and subscription utilities, making the server feel like a broad platform bolted onto a state-data catalog.

Completeness4/5

For the core read-only lifecycle of the Colorado data catalog, search (datasets), schema inspection (metadata), and data retrieval (query) are covered. Minor gaps exist elsewhere: there is no explicit tool for fetching a pipeworx:// citation record directly, and some utilities like generate_llms_txt or scan_dependency are unrelated to the server's stated purpose, but most cited workflows can still complete.

Resources