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Glama

Monarch Initiative

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 the annotations (readOnlyHint, idempotentHint), the description adds critical behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and 'sources_failed' will list timeouts. This is exactly the kind of operational nuance the agent needs.

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 every sentence earns its place: it covers purpose, usage, return fields, ecosystem limitations, and failure behavior in a compact paragraph. No redundant or filler content.

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?

Since there is no output schema, the description fully enumerates the return structure (summary block fields, per-advisory detail, links, alternative versions). It also covers ecosystem scope and error handling, making it complete for a composite tool of this complexity.

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 covers both parameters with descriptions (package, version) including default behavior. The description adds no additional parameter-level guidance, but with 100% schema coverage, a baseline score 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 clearly states it is a composite 'should I add this npm package' check, with a specific verb (scan), resource (npm package), and scope (deps.dev + bundlephobia). It distinguishes itself from sibling tools by focusing on dependency evaluation, which none of the siblings address.

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') and provides exclusions ('NPM ecosystem only... PyPI/Maven/Cargo/Go fall under deps.dev:version directly'). This gives clear direction for selecting this tool over 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
Disambiguation4/5

Most tools have clear, distinct purposes, especially within the same domain (e.g., Polymarket betting tools each serve a specific function). However, the multiple data-querying tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) could cause confusion despite detailed descriptions.

Naming Consistency3/5

Many tools follow a verb_noun snake_case pattern (e.g., bet_research, compare_entities), but there are exceptions like ai_visibility_check, forget, and suggest_questions. The mix of imperative verbs and descriptive phrases creates inconsistency.

Tool Count2/5

With 35 tools, the server feels overloaded. While each domain (biomedical, financial, betting) is covered extensively, the sheer number of tools likely overwhelms agents, and many tools could be merged or split into separate servers.

Completeness4/5

The tool set is comprehensive for its declared purpose, covering biomedical queries, company data, betting analysis, memory management, and more. Minor gaps exist (e.g., no tool to delete a bet, no write operations for biomedical data), but the breadth is impressive.

Resources