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Glama

Doffin

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

A4.4/5.0
Behavior4/5

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

Annotations declare readOnly, idempotent, and not destructive. The description adds valuable behavioral details: partial failures degrade gracefully, bundlephobia timeouts (5-30s), sources_failed listing, and the response structure. While comprehensive, it could be slightly more explicit about rate limits or authentication.

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 primary purpose and provides essential details in a logical order. Each sentence adds value without redundancy. Slightly verbose phrases like 'fans out' and 'degrade gracefully' are acceptable but prevent a top 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 no output schema, the description comprehensively explains the return value, listing all fields in the summary block and additional components (advisory detail, links, alternative versions). It also covers error behavior (partial failures, timeouts) and ecosystem scope, leaving no significant gaps.

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?

Schema coverage is 100% with clear descriptions for both parameters (package name and version). The description repeats that version defaults to latest, adding minimal extra meaning. Baseline 3 is appropriate as the schema already does the heavy lifting.

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 deciding whether to add an npm package, integrating deps.dev and bundlephobia. It specifies the verb 'scan', the resource 'npm package', and distinguishes from siblings by focusing on dependency evaluation rather than general research.

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?

Provides explicit usage context: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also states limitations ('NPM ecosystem only in v1') and directs users to other tools for other ecosystems, offering clear when-to-use and when-not-to-use 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

A3.9/5.0
Disambiguation2/5

Several tool clusters have fuzzy boundaries: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and ask_pipeworx/deep_research/validate_claim overlap for factual research. Polymarket_arbitrage, polymarket_edges, and bet_research also all surface betting opportunities, while ai_visibility_check and scan_competitor_ai_presence serve nearly the same purpose.

Naming Consistency3/5

Names are readable and mostly snake_case, but the pattern is mixed: doffin_* and polymarket_* use domain-prefixed nouns, ask_pipeworx* uses verb+product, and remember/recall/forget/subscribe are bare verbs. There is no single predictable verb_noun convention, though each internal cluster is somewhat consistent.

Tool Count2/5

34 tools is heavy for a server named Doffin, and only three tools actually relate to Norwegian procurement. The remaining surface is mostly general Pipeworx data access, prediction-market analysis, and memory utilities, which feels like several servers bundled under one misleading name.

Completeness3/5

For Doffin read-only access, search + recent + notice detail covers the core workflow well. However, the server's actual domain is fragmented across procurement, Pipeworx research, prediction markets, memory, and subscriptions, which makes coverage difficult to reason about and leaves minor gaps such as no subscription update capability.

Resources