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Glama

Regrid

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

A4.9/5.0
Behavior5/5

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

Annotations already indicate read-only, open-world, idempotent. Description adds critical behavioral details: fans out across two services, bundlephobia first measurement can take 5-30s, sources_failed will list timeouts, and partial failures degrade gracefully. 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?

Highly efficient: front-loaded purpose, then use cases, ecosystem limitation, performance note. Every sentence adds value; no wasted words.

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 complexity (composite call, two data sources, potential timing), description covers return fields, failure modes, and version alternatives. No output schema, but return fields are listed, making the tool fully understandable.

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 already achieves 100% coverage. Description adds context: package is npm package name (accepts scoped packages), version defaults to latest. Adds value beyond schema without redundancy.

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 it's a composite check for npm packages covering licenses, advisories, and bundle size. Distinguishes from sibling tools by specifying ecosystem scope and mentioning alternatives for other ecosystems.

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 says 'Use whenever an agent asks...' and provides when-not-to-use: 'NPM ecosystem only in v1; PyPI/Maven/Cargo/Go fall under deps.dev:version directly.' Also addresses partial failures and graceful degradation.

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
Disambiguation3/5

Most tools have detailed guidance, but several sets blur together: ask_pipeworx_beta is currently identical to ask_pipeworx, polymarket_edges and polymarket_arbitrage both scan for opportunities, and discover_tools/suggest_questions both serve discovery. The descriptions are strong enough to prevent frequent misselection, but the boundaries are not always crisp.

Naming Consistency3/5

Names are consistently snake_case, but the stylistic pattern is mixed: verb_noun names like generate_llms_txt and list_subscriptions sit alongside bare verbs like remember/forget and noun-phrase names like entity_profile, recent_alerts, and polymarket_arbitrage. Prefixes like polymarket_*, pipeworx_*, and regrid_parcel_* add some order, but the set is not uniform.

Tool Count2/5

33 tools is well above the point where a tool set remains easy to navigate, and several tools are near-duplicates or wrappers: ask_pipeworx_beta duplicates ask_pipeworx, scan_competitor_ai_presence is a wrapper around ai_visibility_check, and the prediction-market scanners overlap. The broad domain explains some of the bulk, but the surface still feels overweight.

Completeness3/5

The server covers a wide range of workflows: lookup, deep research, claim validation, entity profiles, comparisons, subscriptions, memory, prediction-market analysis, and parcel lookup. However, the Regrid parcel side is thin with only address and point lookup, and there is no direct tool for parcel-ID/owner/sales/tax queries. These are real gaps, though the universal router helps agents work around them.

Resources