FutureSmart AI Demos

get_tool

Full tool detail as a JSON+Markdown envelope: identity, pricing, per-feature scores, fit, FAQ, relationships (JSON) + our_take / in-depth review (Markdown). Includes proof: real artifact URLs (input/output screenshots, recordings) from the runs that tested it, each with the /evidence permalink for the finding it proves — you can cite evidence from THIS call. null if unknown. Pass fields to project to only the keys you need (token-efficient).

Input Schema

TableJSON Schema

Name	Required	Description
`slug`	Yes	Tool seo_slug, e.g. "affinda" (from list_tools).
`proof`	No	How many proof artifacts to inline. "sample" (default) = up to 6 per tool, spread across distinct criteria so you see breadth; "full" = every artifact; "none" = counts only, no URLs. Counts (artifact_count / finding_count) are the TRUE totals in every mode, so you can always tell what you did not receive.
`fields`	No	Optional projection: return ONLY these top-level fields (identity id/name/title/slug/url is always included) to control response size. Available: id, name, slug, url, heading, website, domain, category, sub_category, content_type, personas, tags, rating, testing_history, features, pricing, fit, use_case_track_record, related_pages, related_reads, similar_tools, faq, demo_video, our_take, in_depth_review_md, proof.

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it explains the output envelope, proof modes, true artifact counts, null behavior for unknown proof, and that identity fields are always included. It does not explicitly state read-only/authorization expectations, but this is implicitly a fetch operation, and the disclosed behaviors are substantial.

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 each sentence earns its place: the first defines the envelope, the second explains proof artifacts and count behavior, the third explains field projection and token efficiency. It is well-structured with backticks and lists, not bloated.

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 no output schema, the description thoroughly covers the return structure (JSON+Markdown envelope), proof behavior, counts, projection, and always-included identity keys. This gives an agent a complete mental model of the response before invoking the tool.

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

Parameters4/5

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

The schema already has 100% coverage, but the description adds value beyond it: 'fields' is explained as token-efficient projection, identity keys are always included, and 'proof' modes are described with behavior for counts and spread. This is more than the schema alone provides.

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 returns 'Full tool detail' and enumerates the exact contents (identity, pricing, per-feature scores, fit, FAQ, relationships, our_take, in-depth review, proof). This distinguishes it from sibling tools like list_tools and compare_tools, which serve different purposes (listing and comparison).

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?

The description provides clear context for when to use the tool (fetching full details for a single tool) and hints at a workflow by noting the slug comes 'from list_tools'. However, it does not explicitly name alternatives or exclusions (e.g., when to use get_evidence instead of inline proof), so it stops short of a 5.

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

A3.8/5.0

Disambiguation5/5

Every tool targets a distinct entity or action: list tools enumerate different content types, get tools retrieve specific entities, search finds across the catalogue, compare_tools does head-to-head comparison, and the two cross-reference tools (rankings_for_tool and tools_in_ranking) are inverse operations. No two tools have overlapping purposes.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern (list_*, get_*, compare_tools, search), but 'rankings_for_tool' and 'tools_in_ranking' deviate with a noun_prep_noun pattern. These two are internally consistent with each other and remain readable, so the overall convention is only slightly disrupted.

Tool Count4/5

16 tools is slightly above the ideal 3-15 range but still well-scoped for a catalogue covering seven entity types, search, comparison, and cross-references. Each tool has a distinct role and none are redundant, so the count feels justified rather than bloated.

Completeness4/5

The tool surface covers all major content types with list and get (except categories and toolkits, which only have list endpoints), plus search, comparison, and inverse cross-references. Minor gaps like missing get_toolkit or get_category are workaround-able since list_toolkits and list_categories expose sufficient metadata.

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