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Glama

Stat Lv

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

A4.7/5.0
Behavior5/5

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

Annotations already mark the operation as read-only and idempotent; the description adds behavioral details such as fan-out to external services, performance expectations (5-30s first measurement), and graceful partial failure with sources_failed. It also enumerates all returned fields, which is crucial given no output schema.

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 uses a logical flow: core purpose, usage triggers, return summary, ecosystem limitation, and failure behavior. There is no fluff; every sentence adds operational value and front-loads the primary purpose.

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 lack of an output schema, the tool description fully enumerates the return block fields and mentions the alternative versions list and per-advisory detail. It also covers timeout behavior and ecosystem boundaries, making it complete for a complex composite 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?

The input schema already has 100% description coverage for both package and version, including defaults. The description does not add parameter-specific information beyond what the schema provides, so the baseline 3 applies because the schema carries the full burden.

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 is a composite npm package evaluation check, combining deps.dev and bundlephobia data, and specifies it returns summary metrics and advisories. This distinguishes it from siblings by the composite nature and npm ecosystem scope.

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?

It explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"' and notes the NPM-only limitation, directing other ecosystems to deps.dev:version directly. This provides clear if-then guidance and names an alternative for non-NPM cases.

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

A3.8/5.0
Disambiguation3/5

Several tools form overlapping families (ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research, plus the six polymarket_* tools), and ask_pipeworx_beta is currently an exact behavioral duplicate. The long descriptions usually disambiguate them, but an agent could still struggle to quickly choose between similar research and edge-detection tools.

Naming Consistency3/5

Names are uniformly snake_case and prefix families like pipeworx_* and polymarket_* help, but there is no consistent verb_noun pattern: subjects, table_meta, recent_alerts, and entity_profile are noun phrases while remember, generate_llms_txt, and compare_entities are action-first. The mixed conventions are readable but less predictable than a uniform pattern.

Tool Count2/5

34 tools is well into the 'too many' range for a single tool set, even if each is individually documented. Several could plausibly be consolidated, such as ask_pipeworx_beta, the polymarket edge tools, and the AI-visibility pair.

Completeness4/5

For its broad stated purpose, the set covers the full lifecycle: lookup/research, entity profiles, comparisons, claim validation, prediction-market edge analysis, memory, subscriptions, and discovery. Minor gaps exist, such as no direct fetch-by-URI tool or subscription update path, but most workflows have a clear route.

Resources