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Glama

Connecticut 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

A4.9/5.0
Behavior5/5

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

Annotations declare read-only and non-destructive. The description adds behavioral details: fans out to two services, bundlephobia first measurement can 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.

Conciseness5/5

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

The description is information-dense but well-structured: overview, usage, output, ecosystem scope, error handling. Every sentence adds value with no repetition or fluff.

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?

No output schema, but the description fully lists return fields, including per-advisory detail, links, and alternative versions. Also covers error handling and timeout behavior, making it complete for a multi-service tool.

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%, baseline 3. The description adds that scoped packages (like @types/node) are accepted for the 'package' parameter, and version defaults to latest when omitted. This clarifies usage beyond the 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 identifies the tool as a 'composite check' for npm packages, specifying it queries deps.dev and bundlephobia for license, advisories, bundle size, etc. It distinguishes itself from other tools (deps.dev:version) by stating NPM-only in v1.

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 instructs when to use: when an agent asks 'is X safe/popular/small' or 'what does adding lodash cost me'. Also clarifies non-npm ecosystems are handled elsewhere and mentions graceful degradation on partial failures.

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

A3.5/5.0
Disambiguation2/5

Several tools are near-identical in purpose: ask_pipeworx and ask_pipeworx_beta are explicitly the same right now, while ai_visibility_check and scan_competitor_ai_presence overlap heavily. The only DMV-specific tool is otherwise buried among generic research, prediction-market, memory, and subscription tools that an agent would struggle to separate.

Naming Consistency3/5

Most tools use snake_case, but conventions are mixed: some are verb_noun (list_subscriptions, generate_llms_txt), some are bare verbs (remember, forget), some are brand-prefixed nouns (pipeworx_trending, polymarket_edges), and ask_pipeworx lacks a conventional verb pattern. Still readable, but not a cohesive naming scheme.

Tool Count1/5

32 tools is already heavy, but nearly all of them are unrelated to the stated Connecticut DMV scope. The server would be better served by a handful of DMV-focused tools; the current count is an extreme mismatch between name and content.

Completeness1/5

The only DMV tool is ct_dmv_ev_registrations, covering EV registration counts from a single February 2025 snapshot. There is no general vehicle registration lookup, driver licensing, plate/ VIN search, appointment, or form coverage, so the DMV domain is severely incomplete.

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