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Glama

Oxylabs

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?

Disclosure of fan-out to two external services, partial failure degradation, and time delay (5-30s for new version bundlephobia measurement) goes beyond annotations which already mark as read-only, open-world, idempotent, and non-destructive. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that front-loads the main purpose and follows with essential details. Every sentence adds value, and it is not overly verbose for the complexity of the tool.

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 no output schema, the description fully compensates by detailing the return structure (summary block fields, per-advisory detail, links, alternative versions). It also covers partial failure behavior, making the tool fully understandable.

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 description coverage is 100%, so baseline is 3. The description adds value by noting that scoped packages are accepted for 'package' and that 'version' defaults to latest when omitted, slightly enhancing clarity.

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 licenses, advisories, version history, and bundlephobia metrics. It specifies the verb 'scan' and resource 'dependency' with clear scope, and it is distinct from sibling tools which are unrelated to package scanning.

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 tells when to use: when agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. Also notes ecosystem limit (NPM only in v1) and directs to other tools for other ecosystems, providing clear use and non-use guidance.

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

All Oxylabs toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

Several tools overlap in purpose, particularly the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) where beta currently matches stable exactly, and the polymarket_* cluster with similar names. However, detailed descriptions clarify each tool's specific role, so an agent can usually select correctly with careful reading.

Naming Consistency3/5

All names use snake_case and are generally descriptive, but they mix conventions: many are verb_noun (list_subscriptions, validate_claim), while others are noun phrases (entity_profile, recent_changes). Prefixes like oxylabs_ and polymarket_ are consistent, but the lack of a uniform verb-first pattern reduces predictability.

Tool Count2/5

With 34 tools, the server exceeds the 25-tool threshold for 'too many'. While the broad scope (data lookup, scraping, prediction markets, memory, subscriptions) warrants a larger surface, the sheer number makes it difficult for agents to quickly identify the right tool without extensive scanning.

Completeness4/5

The tool set covers the core domain thoroughly: question answering, deep research, entity resolution, comparison, validation, web scraping, prediction market analysis, memory management, and subscriptions. Minor gaps like limited e-commerce scraping beyond Amazon and no direct data-writing tools exist, but they do not critically hamper workflows.

Resources