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Glama

Google_analytics

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations, the description discloses composite fan-out behavior, partial failure degradation, a 5-30s first-measurement latency, and the sources_failed field. It also enumerates the output structure. This significantly exceeds annotation coverage and aids the agent in anticipating side effects and delays.

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 dense but well-structured: purpose, usage, outputs, ecosystem limitation, and failure behavior. Each sentence earns its place, and the most decision-relevant information is front-loaded.

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?

Given the tool's moderate-to-high complexity (composite sources, multiple output fields) and no output schema, the description fully covers return values, failure modes, and limitations. It leaves minimal ambiguity for an agent invoking this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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

Schema coverage is 100%, and the description does not add meaning beyond the schema for package and version. It reinforces that version defaults to latest but does not introduce new constraints or semantic depth, so the baseline of 3 is appropriate.

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 opens with a specific composite purpose ('should I add this npm package to my project' check) and details the fan-out across deps.dev and bundlephobia, clearly distinguishing it from any sibling tool. It names the exact data sources and categories, making the tool's function unmistakable.

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 states when to use ('whenever an agent asks is X safe / popular / small') and provides exclusions ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'). This is textbook guidance for tool 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

A3.5/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research both route queries to the same 5,529 tools, and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates. Only the small ga_* subset is clearly distinct.

Naming Consistency2/5

Conventions are mixed: GA tools use a ga_ prefix, but the majority use arbitrary names like ask_pipeworx, deep_research, remember, scan_dependency, and polymarket_arbitrage. No consistent verb_noun pattern across the set.

Tool Count2/5

35 tools is heavy, and the vast majority (31) are unrelated Pipeworx utilities rather than Google Analytics functionality. Only 4 tools actually serve the stated GA purpose, making the count inappropriate for the server name.

Completeness2/5

For Google Analytics, the surface is minimal: list properties, metadata, realtime, and run report—no property management, user management, or data mutation. As a Pipeworx toolkit it's broad but lacks full lifecycle coverage for any single domain.

Resources