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Glama

Google_calendar

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

A4.6/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations, including partial failure handling (graceful degradation, sources_failed list), timing details (bundlephobia first measurement takes 5-30s), and what is returned (summary block, per-advisory detail). 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 a single paragraph that is front-loaded with the main purpose and uses concise language. While it contains multiple details, each sentence adds value without redundancy.

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 no output schema, the description thoroughly explains the return structure (summary block, details, links, alternatives) and covers edge cases like partial failures. It is complete for an agent to understand what to expect.

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?

The input schema already describes both parameters with 100% coverage, including scoped packages and default version behavior. The description adds no new semantic meaning beyond what is in the schema, meeting the baseline.

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 as a composite check for evaluating npm packages, covering license, advisories, version history, bundle size, and tree-shaking. It uses specific verbs like 'scan' and 'check' and explicitly defines the resource ('npm package').

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?

The description explicitly tells when to use the tool: when an agent asks about safety, popularity, or size of a package. It also provides an alternative for other ecosystems ('deps.dev:version directly'), effectively differentiating usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Google_calendar toolsInspect
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TDQS

B3.3/5.0
Disambiguation1/5

The tool set is a mix of 5 Google Calendar tools and 31 unrelated Pipeworx tools (e.g., ask_pipeworx, deep_research, entity_profile). Even within Pipeworx, tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded have highly overlapping purposes, making it very difficult for an agent to distinguish which tool to use.

Naming Consistency1/5

Naming conventions are chaotic. The Google Calendar tools follow a consistent gcal_ prefix, but the rest use various patterns: pipeworx_*, polymarket_*, single words (remember, recall), and others (bet_research, scan_dependency). There is no overarching pattern.

Tool Count1/5

With 36 tools, the count is high, but only 5 are relevant to Google Calendar. The remaining 31 tools are from a completely different domain (Pipeworx data access, Polymarket betting, etc.), making the tool count severely inappropriate for the server's stated purpose.

Completeness2/5

For Google Calendar, the tools provide basic CRUD (create, get, list, search, list_calendars) but lack update and delete functionality. The vast number of unrelated tools does not compensate for these gaps. The overall surface is incomplete for the calendar domain.

Resources