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Glama

Plos

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?

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds substantial behavioral detail: multi-source fan-out, graceful degradation, 5-30s first-measurement delay, and the sources_failed mechanism. It goes far beyond annotations.

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, opening with the core purpose, then a bullet-like enumeration of return fields, and finally failure-mode caveats. Every sentence provides unique value and unnecessary words are absent.

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 tool's complexity (multiple sources, no output schema, potential timeouts), the description covers the full set of returned fields, source behaviors, failure handling, and ecosystem boundaries. It leaves the agent with a complete mental model.

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 has 100% description coverage for both parameters, so the baseline is 3. The description doesn't add new parameter-level semantics, but it reinforces version usage context ('specific version to check' and default behavior) in a way that aligns with 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 explicitly states the tool performs a composite 'should I add this npm package to my project' check, naming the exact data sources (deps.dev, bundlephobia) and key output fields. This clearly distinguishes it from sibling tools like scan_competitor_ai_presence.

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 gives explicit usage triggers ('whenever an agent asks "is X safe / popular / small"'), specifies NPM-only scope, and directs users of other ecosystems to deps.dev:version directly. This is model behavior for when-to-use and exclusion guidance.

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

B3.2/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical in function, while compare_entities and entity_profile both fan out across data sources. The PLOS-specific tools (article, search, recent) are distinct, but the broader set creates confusion about which entry point to use for general data questions.

Naming Consistency2/5

The naming is a mix of single-word nouns (article, recent), verb phrases (search_authored_by, validate_claim), and adjective_noun constructions (recent_changes, recent_alerts). While most multi-word names use snake_case, the lack of a consistent prefix or verb_pattern (e.g., some start with action verbs, others with data categories like polymarket_ or pipeworx_) makes the set feel inconsistent.

Tool Count2/5

At 35 tools, the server is heavily over-scoped for a PLOS journal interface—only 4 tools (article, search, search_authored_by, recent) actually relate to PLOS. The remaining 31 tools form a broad Pipeworx/Polymarket data platform, which suggests the server is trying to do far more than its name implies.

Completeness2/5

For the stated domain (PLOS), the surface is thin: basic search, fetch, recent, and author search lack advanced features like citation metrics, journal browsing, or full-text download links. Conversely, the Pipeworx tools are extensive but unrelated to PLOS, so the set is simultaneously over-complete in unrelated areas and incomplete for its apparent purpose.

Resources