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Glama

Rdap

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?

The description adds significant behavioral context beyond annotations: it is a composite call that fans out to deps.dev and bundlephobia, describes partial failure behavior (bundlephobia timeout, sources_failed list), and notes the 5-30 second delay for first measurements. Annotations already indicate readOnlyHint, openWorldHint, etc., so description complements them without contradiction.

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 somewhat lengthy but each sentence adds value. It is well-structured: starts with overall purpose, then usage, return format, ecosystem scope, and finally failure behavior. A minor cut could be made, but it is not verbose.

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 the tool's complexity (composite, multiple data sources, partial failures, version defaults, ecosystem constraint), the description covers all essential aspects. It explains the return structure, failure modes, and scope, making it fully self-contained despite lacking an output schema.

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%, but the description adds useful context beyond schema: it mentions that scoped packages (e.g., '@types/node') are accepted for 'package', and that 'version' defaults to latest. This enriches the parameter understanding.

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 explicitly states it is a composite check for adding npm packages, covering license, advisories, bundle size, and tree-shaking. It clearly distinguishes from sibling tools, none of which are package-analysis tools, making the purpose unambiguous.

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 provides explicit use cases ('is X safe / popular / small', 'what does adding lodash cost me') and specifies that it is for npm only, directing other ecosystems to alternative tools. This effectively tells the agent when and when not to use it.

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

B3.1/5.0
Disambiguation2/5

The RDAP tools (domain, ip, asn, nameserver, entity) are distinct, but the larger Pipeworx collection creates significant overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates, and the polymarket_* family (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread) has heavily overlapping purposes. ai_visibility_check and scan_competitor_ai_presence also overlap.

Naming Consistency3/5

Most tools use snake_case with a descriptive verb_noun pattern (e.g., ask_pipeworx, discover_tools, recent_changes), but the set mixes short single nouns (domain, ip, asn, entity) with compound names, and there are oddities like generate_llms_txt and ask_pipeworx_grounded that deviate from a clear uniform convention.

Tool Count2/5

At 36 tools, the server is far heavier than expected for an RDAP service. The RDAP core only needs ~5 tools; the rest are an unrelated Pipeworx mega-suite (data queries, prediction markets, memory, subscriptions, feedback) that makes the server feel like a dumping ground rather than a focused toolset.

Completeness4/5

For the RDAP portion, coverage is complete: domain, IP, ASN, nameserver, and entity records are all present. The broader Pipeworx features also cover their own workflows (query, research, comparison, memory CRUD, subscription lifecycle), but the server's stated RDAP identity is muddied by these extras, and some integration points (e.g., no direct update for RDAP data, which is read-only anyway) are inherent limitations.

Resources