FutureSmart AI Demos

ANSWER a buyer question in ONE call: which tool is best at a specific capability, with proof. Returns the resolved verdict our testing team's evidence supports — a named winner FOR THE ASKED CRITERION, every tested tool ranked with a comparable score /5, the CONDITIONS each result holds under (e.g. 'clean tables yes; nested headers no'), dissenting observations preserved as openable links, the tie-break reason, and artifact proof URLs. Answers are materialized from the evidence substrate — the same question returns the same answer. Honestly refuses (coverage: not_tested) when we never tested the topic. Start HERE for any 'which tool is best at X' / 'A or B for X' question; use get_evidence for the raw cells behind it.

List published use-case pages (how-to guides): id, title, slug, url, persona, category, updated_at.

List published ranking pages ("best X"): id, title, slug, url, use_case, persona, category, tools_count, winner, tested_as_of, updated_at.

List published AI tool pages: id, name, slug, url, domain, personas[], categories[].

List published head-to-head comparison pages: id, title, slug, url, tool_a, tool_b, personas[], shared_use_cases[], updated_at.

List published toolkit pages (curated bundles): id, title, slug, url, category.

List personas with published-page counts per type {use_cases, rankings, tools, compares, toolkits}. The slugs are valid input for get_persona.

無需輸入參數。

List the category vocabulary in use with per-type counts and a source flag ("collection"|"derived"), sorted by total.

無需輸入參數。

Search the published catalogue across use cases, rankings, tools, comparisons, and toolkits. Returns ranked light refs [{ kind, id, title, slug, url, snippet, score, meta }] — then call get_tool / get_ranking / get_use_case for full detail. `mode`: keyword (substring), semantic (meaning, via embeddings — finds pages by what they cover), or hybrid (default, fuses both). Optional filters: type[], persona, category.

Given a ranking id, return the ranking {id,title,slug} and its ranked tools [{id,name,slug,url,rank,badge}] (badge: Best/Usable/Needs work/Unstable/Failed).

Given a tool id, return the tool {id,name,slug} and every ranking it appears in [{id,title,slug,url,use_case,rank,badge}].

Given a persona slug (from list_personas), return everything tagged with it: persona, use_cases[], rankings[], compares[], toolkits[], tools[]. Mirrors a persona landing page.

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).

Full ranking detail as a JSON+Markdown envelope: ranked tools (rank/badge/scores), criteria, winner, breakdown (JSON) + verdicts / final take (Markdown). Every ranked tool carries `proof`: real artifact URLs from the exact evidence run this page is bound to, each with the /evidence permalink for the finding it proves — verdict and proof arrive together, no second call needed to cite evidence. `proof.artifact_count` is the true total and `proof.all_findings` is the get_evidence call that returns all of it. null if unknown. Pass `fields` to project to only the keys you need (token-efficient).

Full use-case detail as a JSON+Markdown envelope: step guide, pros/cons, FAQ, tools used (JSON) + the full narrative guide (Markdown, full_md). null if unknown. Pass `fields` to project to only the keys you need (token-efficient).

Query the evidence graph: observation cells (tool × test-scenario × criterion) → verdict, score, the researcher's note, and the REAL artifacts (input/output screenshots) that prove it. The ground truth behind every ranking — filter any combination of tool(s), scenario (slug, group tag, or name), criterion, verdict, or evidence state. evidence_state: "verified" = artifact-backed, "observed" = noted without artifact, "scored-only" = number only.

Evidence-aligned comparison of two tools, honesty enforced structurally: head_to_head (cells from the SAME test input — provable same-input comparison), related_not_same_input (same dimension, different runs — flagged), and each tool's unique evidence. Built from real observation cells with artifacts, not prose.