mcp server
council-ai
Multi-LLM council MCP: parallel frontier models, consensus scoring, verdict-first code review
Description as published by the maintainer. Source
- version 1.0.0
- active
- code review
- evaluation
active — Registry entry last updated 2026-07-30. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
What this server can do
9 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
council_models- 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.
council_query(models, prompt, synthesize, synthesisDirective)- Send a prompt to 25+ frontier AI models across 9 labs (Anthropic, OpenAI, Google, xAI, DeepSeek, Qwen, Mistral, Moonshot, z.ai) in parallel. 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. 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. Required: prompt.
council_query_with_rag(models, prompt, project_id, synthesize, library_query, retrieval_limit)- 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. Required: prompt.
council_review(diff, focus, models, context)- 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. Required: diff.
council_usage- 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.
library_delete(documentId)- 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. Required: documentId.
library_list- 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.
library_search(limit, query, project_id)- 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. Required: query.
library_upload(tags, filename, mimeType, projectId, contentBase64)- 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. Required: filename, contentBase64.
Last successful function declaration observed on . Source: https://mcp.council-ai.app/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://mcp.council-ai.app/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| Latest published version | 1.0.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-07-30 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-07-30 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| mcp tools declared | 9 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | mcp.council-ai.app | |
| mcp endpoint status | ok | The server listed 9 functions when asked. | as of probe | mcp.council-ai.app |
Where to get it
This record as data
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