MCP server

FutureSmart AI Demos

com.aidemos/catalogue

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Search evidence-backed AI-tool reviews, rankings, use cases, comparisons & toolkits (read-only).

Website

At a glance

Type

MCP server

Publisher

aidemos.com (verified by the MCP Registry)

Status

Working at last check

Tools

17

Token cost

About 3,684 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http catalogue https://mcp.aidemos.com/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.catalogue]
url = "https://mcp.aidemos.com/mcp"

Gemini CLI

{"mcpServers":{"catalogue":{"httpUrl":"https://mcp.aidemos.com/mcp"}}}

Cursor

{"mcpServers":{"catalogue":{"url":"https://mcp.aidemos.com/mcp"}}}

VS Code

{"servers":{"catalogue":{"type":"http","url":"https://mcp.aidemos.com/mcp"}}}

Antigravity

agy mcp add catalogue https://mcp.aidemos.com/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"catalogue","server_url":"https://mcp.aidemos.com/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"catalogue","server_url":"https://mcp.aidemos.com/mcp"}

Tools

• answer ~376 tokens
  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.
• compare_tools ~203 tokens
  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 artifact
• get_evidence ~394 tokens
  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), cri
• get_persona ~114 tokens
  Given a persona slug (from list_personas), return everything tagged with it: persona, use_cases[], rankings[], compares[], toolkits[], tools[]. Mirrors a persona landing page.
• get_ranking ~513 tokens
  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
• get_tool ~534 tokens
  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
• get_use_case ~276 tokens
  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).
• list_categories ~70 tokens
  List the category vocabulary in use with per-type counts and a source flag ("collection"|"derived"), sorted by total.
• list_compares ~123 tokens
  List published head-to-head comparison pages: id, title, slug, url, tool_a, tool_b, personas[], shared_use_cases[], updated_at.
• list_personas ~75 tokens
  List personas with published-page counts per type {use_cases, rankings, tools, compares, toolkits}. The slugs are valid input for get_persona.
• list_rankings ~127 tokens
  List published ranking pages ("best X"): id, title, slug, url, use_case, persona, category, tools_count, winner, tested_as_of, updated_at.
• list_toolkits ~109 tokens
  List published toolkit pages (curated bundles): id, title, slug, url, category.
• list_tools ~110 tokens
  List published AI tool pages: id, name, slug, url, domain, personas[], categories[].
• list_use_cases ~115 tokens
  List published use-case pages (how-to guides): id, title, slug, url, persona, category, updated_at.
• rankings_for_tool ~100 tokens
  Given a tool id, return the tool {id,name,slug} and every ranking it appears in [{id,title,slug,url,use_case,rank,badge}].
• search ~332 tokens
  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 embeddi
• tools_in_ranking ~113 tokens
  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).

Browse

• Website screenshots
• Converting to Markdown
• Searching your documents (RAG)

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