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  3. Owlex
Owlex logo
Health: ActiveRecent health check succeeded.Last checked 9/11/2026, 6:17:52 PM

Owlex

User RatingsBe the first to rate and review this MCP server!
View Repository139 GitHub StarsTotal stargazers on GitHub for the source repository (139 stars).Visit Website
coding-agentsdeveloper-toolsai-councilcode-review

Coordinates parallel AI-agent reviews with optional deliberation, specialist roles, team presets, and asynchronous task management.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet β€” we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself β€” its README, its docs, or a verified owner. We haven’t found those for agentic-mcp-tools/owlex, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Directory Badge Claim listing AlternativesπŸ€– More in Coding Agents

Overview

The agentic-mcp-tools/owlex MCP server runs parallel consultations with Codex, Gemini, OpenCode, ClaudeOR, and AiChat, then can coordinate revision rounds before Claude synthesizes the result. It exposes council deliberation, individual agent sessions, Claude Code skills, and background task controls through MCP. Use it for architecture choices, difficult debugging, code reviews, and other questions where comparing independent model perspectives is useful. External agent subscriptions, API tokens, or OpenRouter credentials may be needed depending on the agents enabled.

Use cases

β€’Compare architecture options across several AI agents
β€’Review difficult code with specialist perspectives
β€’Run a security audit using a predefined council team
β€’Collect asynchronous opinions while continuing other work

Key features

β€’Parallel council queries
β€’Two-round deliberation and critique mode
β€’Specialist roles and team presets
β€’Individual agent session management
β€’Asynchronous task controls
β€’Claude Code slash commands

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Owlex.

Extracted Tool Capabilities
Parallel council queries
Two-round deliberation and critique mode
Specialist roles and team presets
Individual agent session management
Asynchronous task controls
Claude Code slash commands

How Owlex works

What agentic-mcp-tools/owlex MCP server does

The agentic-mcp-tools/owlex MCP server gives an MCP-connected coding workflow access to several command-line AI agents. It can send a prompt to Codex, Gemini, OpenCode, ClaudeOR, and AiChat independently, collect their responses, and return a combined result. Claude can then synthesize the discussion.

The main council_ask operation supports an optional second round. In the first round, agents answer without seeing the other responses. In the next round, they can revise their positions after reviewing the collected answers. A critique mode changes the second-round behavior so agents challenge one another rather than simply revise.

How it works

A council request can include Claude's initial opinion, a timeout, specialist role assignments, or a predefined team. Supported roles include security, performance, skepticism, architecture, maintenance, developer experience, and testing. Team presets include security_audit, code_review, architecture_review, devil_advocate, balanced, and optimal.

The project also provides separate start and resume operations for each supported agent. Resuming uses the session identifier or continuation convention supported by that agent. Council work can run asynchronously, allowing the caller to continue other tasks and later use wait_for_task, get_task_result, list_tasks, or cancel_task.

Setup and configuration

Install the project with uv from its Git repository, then register the owlex-server executable in an MCP configuration file. The README shows a .mcp.json entry using that command. The documented Python requirement is version 3.10 or newer.

Configuration variables can exclude selected agents, choose a default team, share Claude's initial opinion automatically, and set the default timeout. Agent-specific settings control Codex approval bypassing, Gemini automatic approval, and whether OpenCode operates in plan or build mode. OPENROUTER_API_KEY enables the ClaudeOR agent, while CLAUDEOR_MODEL and AICHAT_MODEL select models for the relevant integrations.

Tools and capabilities

The agentic-mcp-tools/owlex MCP server documents these capability groups:

  • Council queries with deliberation, critique, roles, teams, and timeouts.
  • New and resumable sessions for Codex, Gemini, OpenCode, ClaudeOR, and AiChat.
  • Background council task creation, inspection, waiting, and cancellation.
  • Claude Code slash commands: /codex, /gemini, /council, and /critique.

The listed agent profiles have different intended strengths. Codex is described for reasoning and code review, Gemini for large-context and multimodal work, OpenCode for configurable alternative perspectives, ClaudeOR for models accessed through OpenRouter, and AiChat for multiple provider backends.

Limitations and notes

The council is not intended for every prompt; its purpose is to add comparison for decisions that warrant additional review. Codex and Gemini use existing subscriptions, while OpenCode and AiChat consume provider-dependent API tokens. Running multiple agents can therefore increase usage costs. Agents can be excluded with COUNCIL_EXCLUDE_AGENTS when cost or availability is a concern.

The CODEX_BYPASS_APPROVALS and GEMINI_YOLO_MODE settings can approve actions automatically, so they should be enabled only when that behavior is acceptable. OpenCode defaults to the read-oriented plan mode and can be changed to build.

The repository is released under the MIT license and identifies Python 3.10+ and MCP compatibility. The README does not state that every listed agent is available without separately installing or authenticating its underlying CLI or provider.

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
139
Stargazers on the source repository.
Last commit
6mo ago
Most recent push to the default branch.
Directory activity
5 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet β€” be the first to share how this listing worked for you.

Frequently Asked Questions about Owlex

Install it with uv from the project's Git repository using `uv tool install git+https://github.com/agentic-mcp-tools/owlex.git`, then configure the `owlex-server` command in `.mcp.json`.

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Technical Specs & Signals

CategoryπŸ€–Coding Agents
PricingBring your own API key (usage-based cost)
More technical detailsExpand β–Ύ
AuthAPI key
LicenseMIT
ClientsClaude Desktop
Last updatedSep 2, 2026
Views5
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars139
GitHub Star CountTotal stargazers on GitHub representing community popularity (139 stars).
Last commit6mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Mar 15, 2026
37Quality signal: Fair Β· 37/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools13/30
Adoption & activity4/15
Community engagement1/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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