Council Ai

io.github.omarkeshkPublished by GitHub account omarkeshkv1.0.1Updated Oct 24, 2026

Multi-LLM council: 25+ frontier models in parallel, consensus scoring, verdict-first code review.

VerifiedStreamable HTTPWeb executableDeveloper Tools

Overview

AI-generated overview

Lets an assistant query 25+ frontier models in parallel for consensus scoring and verdict-first code review.

What you can do
It lets an assistant query more than 25 frontier models in parallel on the same task and combine their answers into consensus scores. Its main use is code review, with verdict-first output and per-model scoring.
When to use it
Use it when you want several models to review code together and get a verdict-first answer with consensus scores. It is unnecessary if a single model's answer is enough.
Requirements
Needs a connection to the remote endpoint and a Bearer token in the Authorization header. The token is minted by the user on the service's settings page and is limited to the Ultra tier.
Before you install
Requires an Authorization token, a sensitive credential to keep private. Check tier eligibility and cost before use, and note that code content is sent to a third-party service.

Installation

In SourceWeft

  1. Open Council Ai in the dashboard and add it to a workspace.
  2. Connect Authorization token credentials. They are stored privately and never shown on public pages.
  3. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.omarkeshk%2Fcouncil-ai/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "council-ai": {
      "type": "http",
      "url": "https://mcp.council-ai.app/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_TOKEN>"
      }
    }
  }
}

Tools

10

council_query

Council Query

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.

ParametersmodeasyncmodelspromptrequiredsynthesizesynthesisDirective

council_result

Council Result

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.

ParametersqueryIdrequired

council_query_with_rag

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.

Parametersasyncmodelspromptrequiredproject_idsynthesizelibrary_queryretrieval_limit

council_review

Council Code Review

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.

Parametersdiffrequiredasyncfocusmodelscontext

library_list

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.

Takes no parameters.

library_upload

Library Upload

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.

ParameterstagsfilenamerequiredmimeTypeprojectIdcontentBase64required

library_delete

Library Delete

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.

ParametersdocumentIdrequired

council_models

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.

Takes no parameters.

council_usage

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.

Takes no parameters.

Version history

1
  1. v1.0.1LatestSep 16, 2026

Score

83% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 daysRequired25 pts
  • Auth declaredRequired10 pts
  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts

    Declare a GitHub repository with a README, or serve resource_documentation in the OAuth protected-resource metadata.

  • LicenseNot applicable

    A hosted service is used under its provider's terms, so no license is expected here. This check is left out of the percentage.