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Glama

Scrapingdog

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

A4.6/5.0
Behavior4/5

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds behavioral details: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed listing. No 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 thorough but slightly long. It front-loads purpose and each sentence provides value. Could be slightly more structured, but effective.

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 fully details return shape including summary block fields, per-advisory detail, links, alternative versions, and partial failure behavior. Complete for a composite tool.

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 has 100% coverage. Description adds semantics: scoped packages accepted, version defaults to latest when omitted, which goes beyond the schema.

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's a composite check for npm packages covering license, advisories, version history, and bundle size. It distinguishes from siblings by explicitly noting 'NPM ecosystem only in v1' and directing to deps.dev:version 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 defines when to use: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also specifies NPM only and alternative for other ecosystems.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Scrapingdog toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ai_visibility_check overlaps with scan_competitor_ai_presence, and the six Polymarket tools all orbit the same edge-detection concept. Descriptions are detailed, but an agent would frequently have to read long text to decide which near-overlapping tool to call.

Naming Consistency2/5

Naming is mostly snake_case but semantically inconsistent: some names are verb-led (ask_pipeworx, validate_claim, remember), some noun-led (polymarket_edges, entity_profile), and some use a vendor prefix (scrapingdog_scrape, scrapingdog_amazon_product). The polymarket_edges vs polymarket_edge_tracker singular/plural pairing adds further confusion.

Tool Count2/5

34 tools is above the 25+ threshold and the count is not justified by a single clear purpose. The server is named Scrapingdog but most tools are unrelated Pipeworx research, memory, subscription, and prediction-market functionality, making the set feel overstuffed and unfocused.

Completeness3/5

The data-research and subscription/memory lifecycles are fairly complete, with create/read/delete coverage for those areas. However, relative to the Scrapingdog scraping identity, the surface is thin: only three scraping tools exist, and there is no direct way to fetch a Pipeworx record by URI or manage scraped-data artifacts.

Resources