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Glama

Duffel

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?

Despite comprehensive annotations (readOnlyHint, etc.), the description adds significant behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s and may time out, and the sources_failed field will list it. This goes beyond the annotations' binary hints.

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 densely packed but front-loaded with purpose. Each sentence adds distinct information (purpose, usage, returns, limitations, ecosystem scope, error behavior). While lengthy, it is efficient and well-organized, earning a high score.

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 (composite check, multiple sources, partial failures), the description is remarkably complete. It covers inputs, outputs (summary block, per-advisory, links, alternatives), error handling, and ecosystem restrictions. No output schema exists, but the description lists return fields explicitly.

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 clarifying that scoped packages (like @types/node) are accepted and that version defaults to latest when omitted. This extra context justifies a score above baseline.

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's purpose: a composite check for npm packages (license, advisories, version history, bundle size, ESM/tree-shake). It uses specific verbs ('scan', 'fans out across') and resource identifiers, distinguishing it from sibling tools that focus on different domains.

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?

The description explicitly tells when to use the tool: 'whenever an agent asks is X safe / popular / small' or 'what does adding lodash cost me'. It also provides an exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', guiding the agent to 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

A3.5/5.0
Disambiguation2/5

Several tools are near-duplicates or have blurry boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical today, and the six polymarket tools (edges, arbitrage, bet_research, fill_risk, edge_tracker, kalshi_spread) all target opportunity-finding in overlapping ways. Detailed descriptions help, but an agent can easily pick the wrong one.

Naming Consistency3/5

Most names are readable snake_case with clear verbs like ask_, compare_, resolve_, and validate_, but conventions are mixed: noun-style names (entity_profile, recent_alerts, polymarket_edges) sit beside imperative verbs and domain-prefixed families. There is no camelCase or total chaos, so it is inconsistent but navigable.

Tool Count2/5

32 tools is heavy and exceeds the well-scoped zone; the set spans flight search, general data Q&A, prediction markets, memory, subscriptions, and feedback. Many are platform meta-tools that could be consolidated or hidden behind the main ask_pipeworx router.

Completeness2/5

If Duffel is the intended domain, only duffel_flight_search is present—there is no offer detail, booking, order management, or cancellation, so travel workflows dead-end. As a generic Pipeworx data platform the surface is broader, but that only highlights the mismatch with the server name and a missing coherent domain.

Resources