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Glama

Tarot Draw

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?

Annotations already mark the tool as read-only and idempotent. The description adds critical behavioral details: composite fan-out, partial failure handling (bundlephobia first measurement 5-30s, sources_failed field), and graceful degradation. No contradiction with 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 a single, well-structured paragraph. It front-loads the core purpose, then covers usage, return details, ecosystem scope, and failure behavior. Every sentence provides unique information with no redundancy.

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?

Although there is no output schema, the description fully enumerates the return fields (summary block with 8 fields, per-advisory detail, links, alternative versions). It also explains failure modes. For a tool with 2 parameters, this is exceptionally complete.

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. The description adds value beyond the schema by explaining the version default ('latest published version when omitted') and confirming scoped packages are accepted. This provides useful context for parameter usage.

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 precisely states the tool's purpose: a composite check for npm packages across deps.dev and bundlephobia. It clearly identifies the resource (npm package) and action (scan for safety, popularity, size). This 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?

Explicit usage guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also specifies scope ('NPM ecosystem only in v1') and points to alternatives for other ecosystems (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

A3.7/5.0
Disambiguation2/5

Multiple tool families have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, suggest_questions and discover_tools are near-duplicates, and ai_visibility_check is a subset of scan_competitor_ai_presence. The five polymarket_* tools are heavily overlapping in purpose and rely on long descriptions to distinguish them, which an agent must read carefully to avoid misselection.

Naming Consistency3/5

All names are lowercase snake_case and there are helpful prefixes (polymarket_*, ask_pipeworx_*, pipeworx_*), but the verb/noun ordering is inconsistent: verb-first names (generate_llms_txt, resolve_entity, scan_dependency) sit alongside noun-first names (bet_research, entity_profile, recent_changes) and bare verbs (forget, recall, remember). Sub-families are internally consistent, but the set as a whole follows no single convention.

Tool Count2/5

32 tools is over the 'too many' threshold, and the scope is a grab-bag rather than a focused server: data research, prediction-market analysis, npm dependency checks, llms.txt generation, memory utilities, subscriptions, and exactly one tarot tool. The server is named 'Tarot Draw' yet 31 of 32 tools serve a completely different purpose, making the count wildly mismatched to the apparent identity.

Completeness2/5

For the inferred Pipeworx data/prediction-market domain the coverage is genuinely deep — ask/grounded/deep research, entity resolution, profiles, comparisons, validation, subscriptions, alerts, edge tracking, and arbitrage all exist. But for the stated purpose ('Tarot Draw'), the surface is one draw tool with no deck details, spreads, reading history, or reversal support, and the data tools' domain is so diffuse that an agent cannot rely on the set forming a coherent workflow.

Resources