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Glama

holidays

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 (readOnly, idempotent, openWorld), the description discloses partial failure behavior, a timeout risk and duration ("bundlephobia's first measurement... can take 5-30s"), and how failures are surfaced ("sources_failed will list it"). It also explains the return structure in detail, covering summary fields and links.

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: it starts with purpose, then usage, then output, then limitations, then failure behavior. Every sentence adds new information and no filler is present. Despite length, it earns its place.

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?

For a composite tool with no output schema, the description thoroughly explains the return payload (summary block, per-advisory details, links, alternatives), ecosystem scope, and error handling. Combined with schema-covered parameters, the agent has complete information to invoke it correctly.

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% for both parameters, and the description does not add meaning beyond the schema. It reiterates that 'package' is an npm name and 'version' defaults to latest, but these details are already present in the schema. No additional semantic value is provided.

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 in ONE call, naming exact data sources (deps.dev, bundlephobia). It clearly distinguishes itself from sibling tools by describing its unique multi-source aggregation and output summary.

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: "Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'." Also provides an ecosystem exclusion and alternative: "PyPI / Maven / Cargo / Go fall under deps.dev:version directly," which prevents misuse.

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

All holidays toolsInspect
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TDQS

A3.6/5.0
Disambiguation1/5

Multiple tools serve near-identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical today, ask_pipeworx_grounded and deep_research overlap heavily, and bet_research/polymarket_edges/polymarket_arbitrage all scan prediction-market opportunities. An agent reading these names and descriptions cannot reliably pick one tool without reading very long descriptions.

Naming Consistency2/5

The names are all lower_snake_case, but the naming style is not consistent across the server: verb_noun (get_holidays, validate_claim), bare noun phrases (next_holidays, entity_profile, bet_research), is/are predicates (is_today_holiday), and separate pipeworx/polymarket prefixed groups. There are recognizable subgroups, but no coherent naming convention unifies the full tool surface.

Tool Count2/5

34 tools is over the healthy range and the majority have nothing to do with holidays; they belong to a broader Pipeworx data, prediction-market, and subscription platform. The holiday-specific surface is only three tools buried inside a much larger, unrelated toolkit, making the server feel overloaded and mislabelled.

Completeness4/5

For the actual holiday domain the core read workflows are covered: all public holidays by country/year, today's holiday status, and upcoming holidays. A small gap is the lack of a supported-country/region listing or date-range filtering, but these are easily worked around because get_holidays returns the full year set.

Resources