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Glama

Hash

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.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive. Description adds transparency about composite nature, possible bundlephobia latency (5-30s), partial failures (sources_failed listed), and graceful degradation. No contradictions.

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?

Single paragraph but densely packed with information. Front-loads purpose and usage, then details outputs and caveats. Could be slightly more structured (e.g., bullet points) but efficient for the content.

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?

No output schema, but description thoroughly explains return fields (summary block, advisories, links, alternative versions), failure modes, ecosystem scope, and latency. Covers all necessary context for agent to use correctly.

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 both parameters well-described. Description adds value by clarifying scoped packages (e.g., '@types/node') and default version behavior (latest). This goes beyond schema, but schema already does a good job.

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 states the tool performs a composite check for npm packages across multiple sources (deps.dev, bundlephobia) and returns a summary. Differentiates from siblings by specifying npm ecosystem and purpose ('should I add this package?').

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?

Explicitly defines when to use: when agent asks about safety, popularity, size, or cost of adding a package. Also notes ecosystem limitations (npm only in v1) and mentions alternative tools 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.

All Hash toolsInspect
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TDQS

A3.7/5.0
Disambiguation4/5

Most tools have distinct purposes despite overlapping domains like prediction markets, but detailed descriptions help agents differentiate. A few tools like `ask_pipeworx` and `deep_research` could be confused without careful reading.

Naming Consistency2/5

Naming patterns are inconsistent, mixing `ask_`, `polymarket_`, `scan_`, `recent_`, `entity_`, etc., with no unifying convention. The server name 'Hash' mismatches the tool set entirely.

Tool Count3/5

32 tools is on the high side for a focused server, but the set covers many areas. The count is slightly above the typical 3-15 range, yet each tool has a clear purpose.

Completeness2/5

The tool set feels like a collection of unrelated utilities rather than a coherent domain. Core hashing functionality is minimal, while other areas like prediction markets are over-represented with gaps elsewhere.

Resources