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Glama

Prh Fi

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

A4.8/5.0
Behavior5/5

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

Annotations declare readOnly, idempotent, non-destructive. Description adds critical behavioral context: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed will list timeouts. Also documents return structure, which is essential 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.

Conciseness4/5

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

The description is front-loaded with the main purpose and use case, then details. While relatively long, every sentence adds meaningful information (return fields, partial failures, ecosystems). Could be slightly more concise, but no 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 return values (summary block fields, per-advisory detail, links, alternative versions) and handles edge cases (partial failures, timing). With only 2 parameters fully documented, the description is complete and leaves no critical gaps.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by noting that scoped packages (e.g., '@types/node') are accepted and that version defaults to latest when omitted. This provides practical usage details beyond the schema.

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 composite nature of the tool, specifying it checks npm packages across deps.dev and bundlephobia. It uses a specific verb ('scan') and resource ('npm package dependency'), and distinguishes from siblings by limiting to npm ecosystem. The purpose is immediately understandable.

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?

Explicit guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' Also specifies that other ecosystems like PyPI fall under different tools (deps.dev:version directly), providing clear when-not-to-use and alternatives.

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

A3.7/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, and compare_entities all retrieve factual data about entities. Additionally, multiple polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) are highly specialized but can be confused without careful reading of descriptions.

Naming Consistency1/5

Naming conventions are highly inconsistent: snake_case (ai_visibility_check, generate_llms_txt), lowercase phrases (ask_pipeworx, forget, recall), and mixed styles (entity_profile, scan_competitor_ai_presence). No clear pattern like verb_noun; tools like 'remember' and 'forget' are single words while others are lengthy phrases.

Tool Count3/5

32 tools is on the high side for a single server, but the coverage is broad (data retrieval, prediction markets, Finnish registry, memory, utilities). However, many tools are redundant or overly specialized, making the count feel bloated. A more focused set could be 20-25 tools.

Completeness3/5

The Pipeworx tools provide deep coverage for factual data retrieval, and the Finnish registry has basic operations. However, there are gaps in other areas, and the set lacks a unified domain. The inclusion of memory tools and a random llms.txt generator feels out of place, breaking the coherence.

Resources