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Glama

Temperature Random

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

A4.8/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint), the description discloses significant behavioral details: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30 seconds, and sources_failed will list timed-out sources. It also outlines the return structure, adding context not captured by annotations.

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 a single dense paragraph that covers all aspects efficiently. It front-loads the core purpose and usage. While it could be slightly more structured (e.g., bullet points for output fields), every sentence is necessary and there is 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?

Despite the absence of an output schema, the description thoroughly enumerates the return fields (summary block, per-advisory detail, links, alternatives) and explains failure modes. For a composite tool with two external services, the description provides sufficient context for an agent to understand what to expect.

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 schema already fully describes both parameters (package name and version) with 100% coverage. The description adds value by noting that scoped packages (e.g., '@types/node') are accepted and that version defaults to the latest, but this is incremental over 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 clearly states the tool's purpose as a composite check for evaluating npm packages, combining data from deps.dev and bundlephobia. It distinguishes itself from sibling tools by specifying its niche for npm packages only, and provides concrete example queries like 'is X safe / popular / small'.

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 states when to use the tool (for npm package evaluation queries) and when not to (for non-npm ecosystems, directing users to deps.dev:version directly). It also covers partial failure handling and mentions the potential delay for bundlephobia's first measurement.

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

There are several overlapping clusters: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions through the same 5,743-tool catalog, and ask_pipeworx_beta is currently an exact duplicate of ask_pipeworx. The Polymarket opportunity tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) also have fuzzy boundaries despite detailed descriptions.

Naming Consistency2/5

There is no consistent naming pattern across the set: some tools are verb-first (ask_pipeworx, compare_entities, validate_claim), some are noun-first (entity_profile, recent_changes, polymarket_edges), and some are compound/multi-word oddities (temperature_random_generate, ai_visibility_check). Small internal clusters like remember/recall/forget and subscribe/unsubscribe/list_subscriptions show mini-consistency, but the overall convention is mixed and unpredictable.

Tool Count2/5

With 32 tools, the server is over-stuffed, and many of them serve the same broad Pipeworx data/research purpose while one unrelated temperature tool rides along. The count is above the 25-tool threshold where a set starts to feel unwieldy, and several tools could be merged or dropped without losing real capability.

Completeness4/5

Assuming the intended scope is the Pipeworx data/agent platform implied by 31 of the 32 tools, coverage is strong: lookups, grounded verification, deep research, entity profiles, comparisons, entity resolution, claim validation, tool discovery, memory, subscription lifecycle, prediction-market analysis, execution risk, and feedback are all represented. The temperature_random_generate tool is a domain misfit rather than a completeness gap, and there are few obvious missing operations for the stated workflows.

Resources