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Glama

Kraken

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

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses that it fans out to two services, degrades gracefully on partial failures, and may take 5-30s for first-time bundlephobia measurements. This is valuable behavioral context the annotations do not provide.

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?

Five sentences, each earning its place. The description front-loads the purpose, then moves to usage, return fields, ecosystem scope, and failure behavior. It is dense but not bloated, with no fluff.

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?

With no output schema, the description fully specifies the return payload (summary block fields, advisory details, links, alternative versions) and the sources_failed signal. It also covers ecosystem limitation and latency behavior, making the tool's behavior predictable in edge cases.

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?

The input schema already documents both parameters at 100% coverage. The description adds one meaningful extra: the 'version' parameter defaults to the latest published version when omitted. The scoped-package detail is redundant with the schema but still reinforces the syntax.

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?

Clearly identifies a composite 'should I add this npm package' check, naming both deps.dev and bundlephobia and listing the specific dimensions evaluated. Distinguishes from sibling tools by explicitly limiting to the NPM ecosystem.

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 when-to-use guidance by mapping to agent intents like 'is X safe / popular / small' or 'what does adding lodash cost me'. Also gives a clear when-not-to-use by directing non-NPM ecosystems to deps.dev:version directly.

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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as the four ask_pipeworx variants and several Polymarket analysis tools. While descriptions are detailed, the distinctions are nuanced, and an agent may struggle to select the correct tool without careful reading.

Naming Consistency3/5

Tool names mix styles: Kraken tools are short nouns (ticker, depth), while Pipeworx tools use various patterns (verb_noun, noun_noun). No single convention dominates, but names are generally readable and descriptive.

Tool Count2/5

40 tools is high, combining two distinct domains. Many tools serve overlapping purposes, making the set feel bloated. A more focused server or consolidation of similar tools would improve scope.

Completeness3/5

The Pipeworx data tools cover a broad range of retrieval and analysis, including prediction markets, memory, and subscriptions. However, the Kraken tools lack trading functionality, and there are redundant tools that could be merged.

Resources