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Glama

Delaware DMV

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?

Discloses composite nature, fan-out to two services, potential 5-30s delay on first bundlephobia measurement, and graceful degradation via 'sources_failed'. Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) are consistent and the description adds rich behavioral context.

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?

Well-structured: front-loaded purpose, then details, then edge cases. Every sentence adds value, though slightly lengthy. No redundant phrasing.

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?

Without an output schema, the description fully specifies the return values: summary block fields, per-advisory details, links, and alternative versions. Also covers partial failures and ecosystem limitations. Complete for the tool's complexity.

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% (both parameters documented). The description adds useful context: version defaults to latest when omitted, package accepts scoped packages like '@types/node'. 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 it performs a composite check for adding an npm package, fanning out across deps.dev and bundlephobia. It uses a specific verb (scan) and resource (dependency), and distinguishes itself from sibling tools by focusing on npm packages and combining multiple data sources.

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 tells when to use: 'is X safe / popular / small' or 'what does adding lodash cost me'. Also states limitations: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' Provides clear context for selection.

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 are effectively duplicates or near-duplicates: ask_pipeworx and ask_pipeworx_beta are described as currently identical, ai_visibility_check and scan_competitor_ai_presence overlap heavily, and the polymarket_arbitrage/polymarket_edges/polymarket_fill_risk cluster has fuzzy boundaries. Even within the DMV subset, de_dmv_ev_adoption and de_dmv_vehicle_registrations both answer overlapping EV-count questions.

Naming Consistency2/5

The de_dmv_* tools follow one snake_case pattern, but the rest of the set mixes bare nouns, brand-prefixed verbs, and generic names (entity_profile, remember, generate_llms_txt, ask_pipeworx_beta). There is no consistent verb_noun or domain-prefix convention across the 36 tools.

Tool Count2/5

36 tools is over the threshold where a tool set becomes hard to navigate, and most of them have nothing to do with a Delaware DMV server. Only five tools are DMV-related; the rest are a general-purpose Pipeworx data, memory, and prediction-market toolkit, which makes the set feel bloated and mis-scoped.

Completeness2/5

For a server named Delaware DMV, the surface is missing core DMV capabilities like driver licenses, vehicle titling, registration renewals, appointments, or fee lookups. The five de_dmv_* tools cover only EV adoption, rebates, charger rebates, crash stats, and registration counts, leaving obvious domain gaps.

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