Revuo

products.get

Read-only

Get full details for a product by slug, including MCP integration data, product-bound agent artifacts (Agent Skills, Grok Bots), and agent readiness scores. Agent artifacts are a property of the product — not directory categories. Response: { product: { slug, name, tagline, description, websiteUrl, logoUrl, pricingModel, currentScore (0-100), tier (free|verified|featured), unverified (true when no approved vendor claim), verifiedAt (ISO; absent if never crawled), categories[], knownLimitations?[] (sourced weaknesses, each with evidenceUrl — weigh before recommending), alternatives?[] (neutral, score-ranked same-category options, self excluded, houseProduct disclosed) }, mcp?: { hasMcpSupport, totalToolCount, totalUseCount, servers[] }, agentArtifacts[] (kind agentSkill|grokBot, install URLs, related product slugs), agentReadiness?: { compositeScore (0-100), aiSummary } }. Errors: { error: { code: 'not_found', ... } }.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	Yes	Product slug (URL-friendly identifier)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mcp`	No
`error`	No
`product`	No
`agentArtifacts`	No
`agentReadiness`	No

TDQS

A4/5.0

Behavior4/5

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

Annotations already include readOnlyHint=true and openWorldHint=true, and the description does not contradict them. It adds valuable behavioral context: agent artifacts are product-bound, knownLimitations carry evidenceUrl to weigh before recommending, and alternatives are neutral with houseProduct disclosed. This exceeds the annotation baseline.

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 long but deliberately structured around the response shape, front-loading the core purpose. Every sentence conveys meaning about fields or interpretation, with no fluff. The length is justified by the complexity of the returned object.

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?

The description completely specifies the response structure, optional fields, edge cases (unverified, verifiedAt absent), error format, and important caveats (knownLimitations, alternatives). For a get-by-slug read operation, nothing essential is missing.

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 fully documents the sole parameter 'slug' as 'Product slug (URL-friendly identifier)', giving 100% coverage. The description adds no further semantic detail beyond the schema, so it remains at the baseline of 3.

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 states a precise verb and resource ('Get full details for a product by slug') and enumerates unique content (MCP integration, agent artifacts, readiness scores). It clearly differentiates from sibling search/find tools that operate by criteria rather than a single slug.

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

Usage Guidelines3/5

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

The description implies when to use it: when a specific slug is known and comprehensive product details are needed. However, it does not explicitly contrast with related tools like products.get_mcp_setup or products.find_agent_ready, leaving some routing ambiguity.

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

A4.4/5.0

Disambiguation4/5

The namespace grouping separates products, tools, taxonomy, and rankings cleanly, and each tool's description points to its intended query mode. Some overlap exists between products.search and products.find_by_capability, and between tools.search and tools.find_for_task, but the descriptions are detailed enough that an agent can usually pick correctly. categories.list and directory.overview also overlap, though directory.overview is explicitly marked as the preferred entry-point.

Naming Consistency4/5

The set follows a predictable `resource.action` dotted convention, with all-lowercase names and snake_case within actions. Minor deviations such as `directory.overview`, `tools.changes`, and `mcp.score_server` mix noun actions and verb+object phrases, but the overall pattern remains readable and easy to guess.

Tool Count4/5

14 tools is appropriate for a read-only directory covering products, capabilities, rankings, MCP setup, and tool discovery. The count is slightly high because the search/find family has several close variants, but each variant has a distinct query style and use case.

Completeness5/5

The surface covers the domain thoroughly: taxonomy, product search and details, capability lookup, agent-readiness scoring, rankings, MCP setup, tool discovery, and schema-drift detection. Cross-references such as products.get_mcp_setup supplying the hash consumed by tools.changes close the main workflow loops without dead ends.

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