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Glama

Urlhaus

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?

Adds significant behavioral context beyond annotations: fans out across multiple services, partial failures degrade gracefully with 'sources_failed' list, bundlephobia first measurement can take 5-30s. No contradiction with annotations (readOnlyHint, idempotentHint, etc.).

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?

Description is moderately long but front-loaded with purpose and key details. Each sentence serves a purpose, though some minor redundancy could be trimmed. 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?

Despite no output schema, the description lists all return fields (summary block, per-advisory, links, alternative versions) and covers edge cases like partial failures and timing. This provides complete context for an agent to understand the tool's behavior and output.

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%, so baseline is 3. Description adds value by clarifying that scoped packages (e.g. '@types/node') are accepted and that version defaults to latest published. This extra context raises the score above baseline.

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: a composite check for adding an npm package, covering license, advisories, version history, and bundle size metrics. It specifies 'NPM ecosystem only in v1', distinguishing from other ecosystems handled by deps.dev directly. No sibling tool overlaps.

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 states when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also provides alternatives for other ecosystems (PyPI, etc.), giving clear guidance on when not to use this tool.

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

The server contains three ask_pipeworx variants, with ask_pipeworx_beta explicitly documented as 'currently matches ask_pipeworx exactly' — two functionally identical tools right now — and ask_pipeworx_grounded differing only in extraction mode. Five polymarket_* tools also occupy overlapping territory (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread), and meta-tools like discover_tools, suggest_questions, and pipeworx_trending blur together. Only the four URLhaus lookup tools are cleanly distinct.

Naming Consistency3/5

There is good internal consistency within subgroups (lookup_host/payload/url, ask_pipeworx*, polymarket_*, remember/recall/forget), but across the full set conventions are mixed: verb-driven names (get_recent, list_subscriptions, validate_claim, generate_llms_txt) sit alongside plain noun names (entity_profile, bet_research, deep_research, recent_changes). The pattern is readable but lacks a single unifying scheme.

Tool Count2/5

At 35 tools this is excessive for any single scope, but the deeper problem is that the server is named Urlhaus while roughly 30 of its 35 tools are unrelated Pipeworx/Polymarket infrastructure. The actual malware-mapping surface is only 4 tools; the rest is bolted-on and inflates the count well past the 25+ threshold for a heavy set.

Completeness2/5

For the server's intended URLhaus domain, the surface is read-only lookups (recent, host, payload, url) with no submission, payload-download, or tag-management operations, leaving obvious lifecycle gaps. Meanwhile the Pipeworx portion is all meta-framing around a single ask_pipeworx router, so neither domain is deeply or fully covered — the set is wide but shallow across many unrelated areas.

Resources