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Glama

Bright Data

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?

Beyond annotations (readOnly, idempotent, non-destructive), the description discloses partial failure behavior, timing for bundlephobia (5-30s first measurement), and that 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 fairly long but each sentence adds value. It is front-loaded with purpose and then details. Slightly verbose but justified by complexity.

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 and covers usage, constraints, and failure modes. It is complete for an agent to make informed decisions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and description adds context: explains that version defaults to latest, accepts scoped packages, and summarizes all return fields giving meaning to parameter outcomes.

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 what the tool does: a composite check for npm packages using deps.dev and bundlephobia, with the specific verb 'scan' and resource 'dependency'. It distinguishes from siblings by noting the npm ecosystem limitation.

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 when to use: when asked about safety, popularity, size, or cost of adding a package. Also states when not: for non-npm ecosystems (PyPI, Maven, etc.), directing to deps.dev directly.

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

A3.8/5.0
Disambiguation2/5

The ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) has significant boundary overlap, and ask_pipeworx_beta is explicitly identical to ask_pipeworx today. The six polymarket_* tools plus bet_research also cover heavily overlapping prediction-market territory, so an agent could easily route a query to the wrong one despite detailed descriptions.

Naming Consistency4/5

Most tools follow a consistent snake_case verb_noun pattern (ask_pipeworx, compare_entities, generate_llms_txt, list_subscriptions, validate_claim, scan_dependency). Minor deviations exist — entity_profile is noun-noun, brightdata_serp/brightdata_unlock use a vendor prefix, and remember/recall/forget are bare verbs — but the overall style is predictable and readable.

Tool Count2/5

At 33 tools this exceeds the 25+ threshold that signals an over-heavy surface. While the server covers multiple domains (data lookup, prediction markets, memory, subscriptions, AI visibility), many of those domains carry redundant variants that could be consolidated, making the count feel bloated rather than well-scoped.

Completeness4/5

The surface covers the core workflows well: querying (ask_pipeworx variants), deep research, entity resolution, entity profiles, comparisons, change feeds, claim verification, discovery/onboarding, subscriptions (list/subscribe/unsubscribe), memory (remember/recall/forget), and feedback. Minor gaps exist — there is no direct tool to fetch a returned pipeworx:// citation URI, and memory lacks an explicit update operation — but agents can work around these.

Resources