MCP server

Football Atlas

app.futbolatlas/futbol-atlas

Checked 28 Oct 2026, 09:03 UTC · Connected to it and listed its tools

The Laws of the Game and world football's major competitions, every claim cited to primary sources

Source

At a glance

Type

MCP server

Publisher

futbolatlas.app (verified by the MCP Registry)

Status

Working at last check

Tools

9

Token cost

About 2,468 tokens per request

Sets up in

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

Last checked

28 Oct 2026, 09:03 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 futbol-atlas https://futbolatlas.app/mcp?via=manifest

Claude Desktop

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

Codex (config.toml)

[mcp_servers.futbol-atlas]
url = "https://futbolatlas.app/mcp?via=manifest"

Gemini CLI

{"mcpServers":{"futbol-atlas":{"httpUrl":"https://futbolatlas.app/mcp?via=manifest"}}}

Cursor

{"mcpServers":{"futbol-atlas":{"url":"https://futbolatlas.app/mcp?via=manifest"}}}

VS Code

{"servers":{"futbol-atlas":{"type":"http","url":"https://futbolatlas.app/mcp?via=manifest"}}}

Antigravity

agy mcp add futbol-atlas https://futbolatlas.app/mcp?via=manifest

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":"futbol-atlas","server_url":"https://futbolatlas.app/mcp?via=manifest","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"futbol-atlas","server_url":"https://futbolatlas.app/mcp?via=manifest"}

Tools

• answer ~207 tokens
  Answer a question from the corpus, or refuse. Returns only the claims that bear on the question, each with the sources it cites and its editorial confidence. When the corpus cannot answer, answered is false and abstention_reason plus missing_topics say what was not covered — a refusal is a real resu
• compare ~277 tokens
  Two to six knowledge objects side by side: their cards, every indexed attribute as a matrix (the same fields api/index.json publishes, null where an object does not say), the tags they share, the relations that run between them, the neighbours they have in common, and each one's claims with sources
• get_entity ~167 tokens
  Fetch one knowledge object by id, with its claims and the sources each claim cites. Use this once search, answer or get_topic has given you an id. An unknown id is not a dead end: the answer names near-miss ids you can retry with.
• get_latest ~171 tokens
  Most recently verified knowledge objects (freshness signal). Use this to judge how current the corpus is, or to see what changed since you last read it. It ranks by verification date and ignores your topic entirely — use search or get_topic when you want objects that are relevant rather than recent.
• get_overview ~95 tokens
  Corpus overview: what this instance knows, counts by type, published tags, freshness. Use this first when you land here and do not yet know whether this corpus can answer your question.
• get_related ~318 tokens
  Graph neighbours of an object: outgoing and incoming relations, each with its relation type. PAGED: 25 relations by default, 200 at most, and a response budget of about 64 KB per call — a page over budget comes back shorter WITH a next_cursor, never truncated. One cursor walks both directions, outgo
• get_sources ~493 tokens
  The instance's source registry — each entry with its evidence tier, reliability and access date. PAGED: 25 entries by default, 200 at most, and a response budget of about 64 KB per call — a page over budget comes back shorter WITH a next_cursor, never truncated. Read next_cursor and call again to co
• get_topic ~351 tokens
  List the knowledge objects carrying a tag (topics are content-backed tags). PAGED: 25 objects by default, 200 at most, and a response budget of about 64 KB per call — a page over budget comes back shorter WITH a next_cursor, never truncated. Read next_cursor and call again to continue, and its absen
• search ~389 tokens
  Full-text search over the knowledge graph. Matching ignores accents and apostrophes, so query in the user's own words; every hit carries the fields it matched and a score. BM25 relevance: each query term is weighted by how RARE it is in this corpus and by where it hits (name 3, tags 2, questions 2,

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