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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

A4.4/5.0
Behavior5/5

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

Annotations already provide readOnly, openWorld, idempotent, and destructive hints. The description adds significant behavioral context: it fans out across two services, handles partial failures gracefully, and notes that bundlephobia's first measurement can take 5-30 seconds. No contradictions 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.

Conciseness4/5

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

The description is front-loaded with the core purpose and is information-dense. It could be slightly more concise, but each sentence adds value (ecosystem scope, return structure, failure behavior). No wasted words.

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 lacking an output schema, the description fully explains the return structure (summary block fields, advisory details, links, alternative versions) and behavioral edge cases (partial failures, timing). For a composite tool with two dependencies and no output schema, this is complete.

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 the baseline is 3. The description adds minimal new meaning: it confirms that 'package' accepts npm scoped packages and that 'version' defaults to latest. These are already implied by the schema descriptions.

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 the tool's purpose: a composite check for evaluating npm packages (license, advisories, size) in one call. It distinguishes from potential alternatives by specifying 'NPM ecosystem only in v1' and directing other ecosystems to deps.dev:version. The use case is explicitly tied to agent queries about safety, popularity, and size.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is given: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also warns about bundlephobia timing and partial failures. The description mentions ecosystem limitations and points to alternatives, though it doesn't cover all possible exclusion scenarios.

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

B3.1/5.0
Disambiguation2/5

Several tools have overlapping purposes: champion_mastery and summoner_top_mastery both return mastery data, and the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) plus deep_research all handle question-answering. The inclusion of an entire unrelated Pipeworx research suite under a Riot Games server creates cross-domain ambiguity, making it difficult to know which tool to select.

Naming Consistency3/5

Most tools use snake_case, but the pattern varies: resource_by_key (account_by_puuid), verb_noun (generate_llms_txt, compare_entities), bare verbs (forget, recall, remember), and standalone nouns (match, status). Pipeworx and polymarket tools share consistent prefixes, but the Riot tools and meta-tools break the pattern, resulting in a mixed but still readable convention.

Tool Count2/5

42 tools is far above the typical 3-15 for a focused server. Only 10 are Riot Games-specific; the remaining 28 are unrelated Pipeworx, data-research, Polymarket, and memory tools. The excessive count dilutes the server's purpose and makes it feel bloated.

Completeness3/5

The Riot Games domain is covered reasonably well with accounts, summoners, mastery, matches, and rankings, but misses common endpoints like champion static data and live match info. The extensive non-Riot tools do not fill these gaps and instead add an unrelated, separately complete surface that distracts from the server's apparent purpose.