io.github.omarkeshk/council-ai

Permanently delete a document from the user's Council RAG library — the record, every indexed chunk, AND the stored file are removed. This cannot be undone; re-adding the document requires uploading it again. Get document IDs from library_list. Use when the user asks to remove a document or when the 200-document library cap blocks an upload. Confirm with the user before deleting anything they did not explicitly name. No model call, no budget consumption.

Send a prompt to a council of frontier AI models across 9 labs (Anthropic, OpenAI, Google, xAI, DeepSeek, Qwen, Mistral, Moonshot, z.ai). Returns each model's independent response plus a moderator-synthesized consensus answer with an agreement score and the key point of disagreement, when one exists. Use when a single-model answer might hallucinate or when verification across labs matters (research, contracts, architecture, legal, medical, code review). Adaptive-cost pattern: for a quick check, call with 2-3 models; if consensusScore comes back low (or keyDisagreement matters to the decision), escalate by re-running with more models — easy questions stay cheap, disputed ones get more compute. TIMEOUTS: a full council can run for minutes. If your MCP client has a fixed tool-call timeout, set async=true — you get a queryId back immediately and fetch the finished result with council_result. Call council_models first to pick specific model IDs, and council_usage to check remaining budget. Bills against the user's Council AI monthly budget.

Multi-model code review. Sends a unified diff (or code snippet) to multiple frontier AI models from different labs in parallel, each acting as an independent reviewer with an explicit verdict + findings contract. Returns a verdict-first synthesis: overall SHIP/NO-SHIP, consensus score, confirmed findings (flagged by 2+ models), then dissents (single-model findings with reasoning), then each reviewer's verdict. Diffs are capped at 14,000 characters — split larger changes by file or hunk and call once per chunk. Bills against the user's Council AI monthly budget like any council query.

Upload a document into the user's Council RAG library so future council_query_with_rag and library_search calls can retrieve it. Accepts PDF, Word (docx), text, and markdown files as base64 — images are not supported. Max 10MB per file via MCP (the web library at https://council-ai.app/settings?tab=library takes up to 50MB); libraries hold up to 200 documents. Ingestion (chunking + embedding) runs in the background: the returned document starts in "pending" status — check library_list for it to reach "ready" before querying against it. No model call, no budget consumption.

List the AI models available to the current user. Returns ID, provider, tier, context window, and capability flags (web search, vision, streaming). Use the IDs returned here as the `models` array argument to council_query / council_query_with_rag.

Like council_query, but first retrieves the most relevant passages from the user's personal Council RAG library (uploaded PDFs, Word docs, contracts, research papers, codebases) and injects them into every model's prompt. Use when the question is about content the user has uploaded — contract review, research synthesis across a paper library, code review against an architecture doc, etc. Ultra-tier only.

Fetch the result of a council_query started with async=true. Returns status "running" while the council is still working (wait 30-60 seconds and call again), the full synthesis + per-model responses once complete, or status "failed" with the error. Polling is free — the council run itself was already billed by council_query.

Return the user's current monthly cost-budget consumption (current spend, budget cap, percentage used, days until reset). Use to decide whether to warn the user before invoking another council_query, or to suggest using cheaper models. Per Council's rule: never show dollar amounts to the user in the response — use percentages.

List the documents in the user's Council RAG library. Returns id, filename, source type, ingestion status, chunk count, and upload date for each document. No retrieval, no budget consumption.

Semantic search over the user's Council RAG library (uploaded PDFs, Word docs, contracts, research papers, codebases). Returns top-K chunks with source filename and page number. No model call, no budget consumption. Use to find direct quotes, check what the library contains, or scope a follow-up council_query_with_rag call.