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Tenor

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?

Beyond annotations (readOnlyHint, etc.), the description details the composite behavior, the services used (deps.dev, bundlephobia), the return fields (summary block with is_latest, license, etc.), and potential latency ('first measurement on a new version can take 5-30s'). 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.

Conciseness4/5

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

The description is a single paragraph of about 150 words. It front-loads the main purpose and includes dense, useful details. While every sentence adds value, it could be broken into multiple sentences for improved readability.

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 the tool's complexity (two-service composite, partial failures, multiple return fields) and lack of output schema, the description fully covers return values (summary block, per-advisory detail, links, alternative versions), ecosystem scope, and error handling. An agent has sufficient context to invoke the tool correctly.

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 covers both parameters with descriptions (package name, version defaults to latest). The description adds minimal extra meaning (e.g., scoped packages accepted) but schema coverage is 100%, so baseline 3 applies.

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, combining deps.dev and bundlephobia data. It distinguishes from sibling tools by specifying NPM ecosystem only and noting alternative tools for other ecosystems.

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?

The description explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also clarifies ecosystem scope (npm only in v1) and handles partial failures, providing 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

A3.6/5.0
Disambiguation2/5

Several tools have nearly identical purposes: autocomplete and search_suggestions both return query completions, ask_pipeworx and ask_pipeworx_beta are currently identical, and ai_visibility_check overlaps with scan_competitor_ai_presence. The detailed descriptions help for some, but the overlapping clusters create real confusion for an agent selecting a tool.

Naming Consistency3/5

Naming is a mix of single-word nouns (search, featured, posts, categories) and snake_case verb phrases (resolve_entity, compare_entities, ask_pipeworx), with modifier suffixes like _beta and _grounded. While the snake_case is consistent where used, the overall pattern is not uniform across the server.

Tool Count2/5

38 tools is far too many for a server named 'Tenor' whose core GIF API needs only a handful. Much of the surface belongs to Pipeworx data, polymarket analytics, memory, and subscriptions—scope that belongs in a different server or a clearly separated package.

Completeness5/5

The Pipeworx side is remarkably complete: query (ask_pipeworx), grounded answers, deep research, entity profiles, comparisons, validation, discovery, memory, subscriptions, and specialized polymarket tools all cover their domain thoroughly. The Tenor side has search, browse, categories, trending, suggestions, and post retrieval—no critical dead ends.

Resources