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Bundlephobia

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.

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the readOnly/idempotent annotations, disclosing the composite fan-out, graceful partial failure behavior, 5-30s first-measurement timeout, and the sources_failed field. This gives the agent an accurate model of runtime behavior.

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?

Dense and information-rich, with the purpose and usage front-loaded and each sentence conveying a distinct aspect (purpose, usage, return values, ecosystem scope, failure behavior). Slightly long but justifiable given the tool's 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?

There is no output schema, so the description must explain return values, and it does comprehensively, listing the summary block fields, per-advisory detail, links, and alternative versions. It also covers ecosystem limitations and failure handling, making it complete for an agent to invoke and interpret results.

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?

The input schema already fully documents both parameters with descriptions, achieving 100% coverage. The description does not add extra parameter-level semantics beyond what the schema provides, so the baseline is appropriate.

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?

Clearly identifies a specific composite check for npm packages, naming the exact question it answers and the data sources it combines. It is well-differentiated from the sibling tools, which focus on AI visibility, research, or prediction markets.

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 states 'Use whenever an agent asks...' and gives concrete examples like 'is X safe / popular / small' or 'what does adding lodash cost me'. It also defines the boundary by noting NPM-only scope and pointing to deps.dev directly for other ecosystems.

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

A3.9/5.0
Disambiguation2/5

There is substantial overlap among tools in the Pipeworx group: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route natural-language queries to the same 5,578 tools and sources, with only subtle differences in mode (beta vs stable, grounded vs standard, single vs multi-part). Similarly, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk are heavily intertwined, making differentiation difficult. Tools like similar, size, history, and scan_dependency from the bundlephobia side are distinct, but the Pipeworx family muddies the set.

Naming Consistency3/5

The bundlephobia tools follow a consistent noun pattern (size, similar, history), and the Pipeworx meta-tools use snake_case verbs (ask_pipeworx, resolve_entity, compare_entities, validate_claim). However, the naming is inconsistent across the two families—bundlephobia's simple nouns (size, similar, history) clash with the verbose descriptive verbs—and naming like ai_visibility_check, scan_competitor_ai_presence, and generate_llms_txt break from the Pipeworx pattern. The set mixes short names, camelCase-ish compounds, and snake_case, so no single consistent convention holds.

Tool Count2/5

35 tools is too many for a server that ostensibly serves two domains (bundle-size analysis and Pipeworx data research). The bundle-size analysis needs only a handful (size, history, similar, recent_searches, scan_dependency), yet there are over 30 tools dominated by a sprawling meta-research layer including multiple ask_pipeworx variants, several polymarket tools, plus meta-cognitive tools (remember, recall, forget, discover_tools) that are not core to either domain. This bloats the surface and makes call routing difficult.

Completeness4/5

Each functional domain is fairly complete: bundlephobia covers size measurement, history, alternatives, search, and dependency vetting; the Pipeworx side covers lookup, research, entity resolution, comparison, verification, subscriptions, and feedback. However, there are gaps—e.g., no tool for directly reading an npm package's README or license beyond scan_dependency's summary, and no explicit tools for some administrative actions like account management or subscription editing beyond create/cancel/list. The completeness is strong for what's advertised but not exhaustive.

Resources