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Glama

California 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?

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), the description adds: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed lists timeouts. 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.

Conciseness4/5

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

The description is detailed but front-loaded with key purpose and usage. Every sentence adds value, though slightly long. Could be slightly tighter but well-structured.

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 details return fields (summary block, advisories, links, alternatives) and error behavior (partial failures, sources_failed). 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.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Both parameters are described in schema (100% coverage). Description adds: scoped packages accepted, version defaults to latest, and context that checking a new version may trigger first-time measurement delay.

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 npm packages, combining deps.dev and bundlephobia, and answers questions like 'is X safe/popular/small'. It distinguishes from siblings by being npm-specific and composite.

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 says when to use: when an agent asks about safety, popularity, size, or cost. Also notes ecosystem limitation (NPM only) and points to alternative for other ecosystems (deps.dev:version directly). Provides clear context.

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

The six ca_dmv_* tools are clearly distinct, but the set also contains near-duplicates like ask_pipeworx and ask_pipeworx_beta (currently functionally identical), five overlapping Polymarket analysis tools, and two overlapping AI-visibility probes. An agent would frequently struggle to select the right tool among these overlapping families.

Naming Consistency3/5

Names are mostly lowercase snake_case and individually readable, but conventions are mixed: ca_dmv_* prefix vs. bare verbs like forget, compound names like generate_llms_txt, and near-identical pairs like polymarket_edges vs. polymarket_edge_tracker. The pattern is inconsistent enough to impede quick scanning.

Tool Count1/5

37 tools for a server named 'California DMV' is an extreme scope mismatch: only 6 tools relate to DMV while 31 are general-purpose research, prediction-market, memory, and subscription tools. The DMV-specific subset would be well-scoped at 6 tools, but the bundled platform makes the server feel bloated and off-topic.

Completeness2/5

The DMV portion covers licenses, registrations, EV adoption, offices, forms, and insurance codes, but misses common DMV needs like title transfers, fee estimates, or appointment booking. The broader research platform is extensive, but that does not compensate for the server's stated purpose, leaving the DMV surface with significant gaps.

Resources