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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.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds valuable context: composite fan-out across multiple services, partial-failure degradation with graceful timeout handling, and the 5-30s delay for first bundlephobia measurement. No contradiction with annotations.

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 dense but every sentence earns its place: purpose, usage triggers, return fields, scope, and failure behavior. It is front-loaded with the primary purpose and reads as a well-organized summary for a complex composite 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?

Despite having no output schema, the description lists the return summary block fields, mentions per-advisory detail and links, explains the NPM-only limitation, and details timeout behavior. This fully equips the agent to understand what the tool returns and how it behaves under partial conditions.

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

Parameters3/5

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

Schema description coverage is 100%, so parameters are fully documented. The description adds minimal new parameter semantics—'package' is npm package name and 'version' defaults to latest, both already in the schema. Baseline 3 is appropriate as the description does not need to compensate.

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 'should I add this npm package to my project' check that fans out across deps.dev and bundlephobia. The verb 'check' with resource 'npm package' is specific, and the composite nature distinguishes it from sibling tools like deep_research or validate_claim.

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 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"' and also states the NPM-only scope with alternatives for other ecosystems ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'). This provides clear when-to-use and when-not-to-use 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

C2.9/5.0
Disambiguation2/5

Many tools overlap significantly: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, while deep_research, discover_tools, and suggest_questions all serve meta/onboarding purposes. The Steam-specific tools are distinct, but they are drowned out by a large unrelated set (Polymarket, Pipeworx, npm scanning) that makes selection confusing.

Naming Consistency2/5

Tool names follow no single convention: some are verb_noun (resolve_vanity_url, generate_llms_txt), some are noun-only (app_details, player_stats), and others use domain prefixes inconsistently (polymarket_arbitrage, ask_pipeworx_grounded, deep_research). The mix of descriptive and vague names (process, run, execute) adds to the inconsistency.

Tool Count2/5

At 45 tools, the server is heavily overloaded, especially for a server named 'Steam' where only about a third of the tools actually relate to Steam. The rest belong to Pipeworx, Polymarket, and other unrelated domains, making the scope unclear and the tool count far too large for a focused purpose.

Completeness3/5

For the Steam domain, the server covers a reasonable range: app details/news, player counts, friends, owned games, achievements, stats, bans, levels, and summaries. However, notable gaps exist such as store search, reviews, wishlist, or any user inventory/trading features. The non-Steam tools add breadth but do not address these missing Steam operations.