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Glama

Data Vermont

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

A4.9/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds valuable behavioral details: bundlephobia's first measurement can take 5-30s, partial failures degrade gracefully with sources_failed field, and the return structure (summary, advisories, links, versions). No contradiction 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 appropriately sized for a complex tool. It is front-loaded with purpose, then usage, then behavioral details. Every sentence adds value, and there is no redundancy or extraneous information.

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 lacking an output schema, the description adequately covers the return structure: summary block with specific fields, per-advisory detail, links, and alternative versions. For a tool with this complexity, the description provides sufficient context for correct invocation and expectation.

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%, so baseline is 3. The description adds value by noting that scoped packages are accepted and that version defaults to latest when omitted. While schema already includes descriptions, the description reinforces and adds minor extras.

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: a composite check for adding npm packages, combining data from deps.dev and bundlephobia. It specifies the verb (check), resource (npm package), and scope (license, advisories, bundle size, etc.), distinguishing it from any sibling tools.

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?

Explicit guidance on when to use: when an agent asks about safety, popularity, size, or cost of adding a package. Also notes ecosystem limitations (NPM only in v1) and suggests alternatives for other ecosystems. Provides context on partial failures and timeouts.

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

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the ask_pipeworx trio (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the polymarket cluster (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) could cause confusion without careful reading. However, the detailed descriptions effectively differentiate each tool's specific role.

Naming Consistency5/5

All tool names follow consistent snake_case convention with a verb-noun pattern (e.g., compare_entities, recent_changes, resolve_entity). There are no mixed conventions or chaotic naming, making the set predictable and easy to navigate.

Tool Count4/5

At 34 tools, the set is extensive but justified by the server's broad scope covering Vermont open data, Pipeworx data platform, prediction markets, and utility features. Each tool earns its place, though the count is slightly high compared to typical servers.

Completeness4/5

The tool surface covers data retrieval, entity analysis, prediction markets, memory, subscriptions, feedback, and claim validation. Minor gaps exist (e.g., no direct compliance tools), but the set is comprehensive for its stated purpose of data integration and analysis.

Resources