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Glama

Urlscan Io

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds substantial behavioral context: fans out to deps.dev and bundlephobia, returns specific fields, partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed will list timeouts.

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 with a clear front-loaded purpose. It uses a list-like structure for return fields. While efficient, it could be slightly more structured (e.g., bullet points) for easier scanning.

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, so description carries full burden. It lists all summary fields, mentions per-advisory details, links, alternative versions, and explains failure behavior. This provides a complete picture of what the tool returns and how it behaves.

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. The description adds extra value beyond schema: clarifies that scoped packages (e.g., @types/node) are accepted and that version defaults to latest when omitted.

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 it is a composite check for npm packages covering license, advisories, size, and tree-shake support. It distinguishes itself from sibling tools by being specific to dependency scanning; no other sibling tool covers this domain.

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?

Explicit usage context is given: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also notes the NPM-only scope in v1 and directs to deps.dev for other ecosystems, providing alternative guidance.

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

B3.3/5.0
Disambiguation3/5

Many tools have distinct purposes with thorough descriptions, but there is meaningful overlap among ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research, all of which route questions to data sources. The multiple polymarket tools (edges, arbitrage, fill_risk, edge_tracker, kalshi_spread) also require careful reading to differentiate. The urlscan tools (domain, ip, search, submit, result) are distinct, but the overall set mixes several unrelated domains, increasing misselection risk.

Naming Consistency2/5

Naming is a mix of conventions: short urlscan verbs (domain, ip, search, submit), noun-first names (entity_profile, recent_changes, deep_research), verb_noun names (compare_entities, resolve_entity, generate_llms_txt), and prefixed families (polymarket_*, pipeworx_*). There is no uniform verb_noun pattern or consistent prefix convention across the set. This inconsistency makes it hard to predict what a tool does from its name alone.

Tool Count2/5

With 36 tools, the set is far above the 3-15 range typical for a coherent server, even for a broad data API. The inclusion of meta-tools like discover_tools and suggest_questions suggests the count is so high that agents need help navigating it. The load is compounded by tools spanning urlscan.io, Pipeworx, prediction markets, memory, subscriptions, and feedback, making the server feel like a grab bag rather than a focused service.

Completeness3/5

The urlscan portion is complete for searching, submitting, and retrieving scan results, and the Pipeworx side covers a wide range of data and analysis capabilities. However, the server is named 'Urlscan Io' while most tools are unrelated to urlscan, creating a mismatch between the stated purpose and the actual surface. There are no obvious gaps for the included features, but the lack of a coherent domain makes it hard to assess what 'complete' means for this set.

Resources