sovereign-exoself-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@sovereign-exoself-mcpReview the authentication flow for security issues"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
sovereign-exoself-mcp
Local MCP server for personal AI Council. Routes tasks through manager, worker, critic, synthesizer, and archivist with fast/review/full paths. Uses SQLite memory with WAL and FTS5. Supports mock, Ollama, and OpenRouter providers.
Architecture
flowchart LR
C[MCP stdio client] --> T[3 tools]
T --> O[Council Router]
O --> M[Manager<br/>granite3.3:2b]
M -->|fast| W[Worker<br/>qwen2.5-coder:7b]
M -->|review| W
M -->|full| W
W -->|review| CR[Critic<br/>qwen2.5-coder:7b]
CR -->|reject| W
CR -->|approve| S[Synthesizer<br/>granite3.3:2b]
S -->|full| A[Archivist<br/>granite3.3:2b]
S --> R[Result]
A --> RRelated MCP server: orchestrator-mcp
Model Configuration (Config B)
Role | Model | Rationale |
Manager | granite3.3:2b | Fast routing decisions |
Worker | qwen2.5-coder:7b | Quality code execution |
Critic | qwen2.5-coder:7b | Reliable code review |
Synthesizer | granite3.3:2b | Fast result merging |
Archivist | granite3.3:2b | Fast memory extraction |
Benchmark: 1504ms avg, 3428ms P95, 100% success rate, 0 timeouts.
Quick Start
Requirements: Ubuntu/Linux, Python 3.14, uv.
cd /home/hat/AionUI/sovereign-exoself-mcp
bash scripts/install.sh
bash scripts/smoke_test.sh --mock--mock mode requires no API key and is useful for offline validation. For real inference, run the server directly with a provider (see below). scripts/generate_client_configs.py auto-generates host snippets (dist/) that enable a real provider: ollama by default, openrouter when OPENROUTER_API_KEY is present.
Ollama Mode
# Pull required models
ollama pull granite3.3:2b
ollama pull qwen2.5-coder:7b
# Run with Ollama
SOVEREIGN_PROVIDER_MODE=ollama \
uv run python -m sovereign_exoself_mcpOpenRouter Mode
# Secrets are environment-only: store the key in the gitignored `.env` file
echo "OPENROUTER_API_KEY=sk-or-v1-..." >> .env
SOVEREIGN_PROVIDER_MODE=openrouter \
uv run python -m sovereign_exoself_mcpCouncil Routes
Fast Path (Default)
Manager → Worker → Result. Used for simple questions, facts, quick analysis.
Review Path
Manager → Worker → Critic → Synthesizer → Result. Used for code changes, architecture decisions.
Full Council
Manager → Worker → Critic → Synthesizer → Archivist → Result. Used for complex tasks requiring memory.
API Tools
council_run
{
"task": "Review and improve the configuration loader.",
"mode": "auto",
"budget": "low",
"worker_profile": null,
"needs_memory": null,
"max_rounds": null,
"route_override": null
}Mode values: auto (manager decides), code, analysis, decision. As a shorthand, mode also accepts fast, review, or full to force a route directly. An explicit route_override (fast/review/full) always wins when provided.
Response includes: run_id, status, route, models, result, metrics, memory_updates, warnings
memory_manage
{
"action": "search",
"query": "design decisions"
}Actions: search, store, list, delete, export, profile
system_status
Returns health, provider mode, model mapping, prompt versions, active runs, Ollama status.
Changing Models
# Environment variables
export SOVEREIGN_OLLAMA_WORKER_MODEL=qwen3:8b
export SOVEREIGN_OLLAMA_MANAGER_MODEL=gemma2:2b
# Or config file
cp config/council.example.yaml config/council.yaml
# Edit config/council.yamlWorker Profiles
Profile | Purpose |
code_engineer | Code implementation, debugging, refactoring |
system_engineer | Infrastructure, DevOps, system design |
researcher | Information gathering, analysis |
technical_writer | Documentation, prose |
planner | Task decomposition, project planning |
general_operator | Default fallback |
Running Benchmark
# Mock benchmark
python benchmarks/benchmark.py --mode mock
# Live benchmark (requires Ollama with models)
OLLAMA_TEST_MODEL=qwen2.5-coder:7b python benchmarks/benchmark.py --mode ollamaSystem Status
# Via MCP tool
system_status({})
# Via CLI
uv run python -c "import asyncio; from sovereign_exoself_mcp.providers import probe_ollama; print(asyncio.run(probe_ollama('http://127.0.0.1:11434', 5)))"Rollback
Set
SOVEREIGN_PROVIDER_MODE=mockRemove new environment variables
Revert code changes
Adding Worker Profiles
Create
src/sovereign_exoself_mcp/prompts/profiles/<name>.txtAdd to
PROFILESlist inprompts.pyUse in requests:
{"worker_profile": "<name>"}
Environment Variables
See .env.example for all available settings.
Documentation
Testing
# Run all tests
python -m pytest tests/ -v
# Run specific test suite
python -m pytest tests/unit/test_prompts.py -v
python -m pytest tests/unit/test_router.py -v
python -m pytest tests/unit/test_schemas.py -vLicense
MIT
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.
Hosted MCP server for live public-data APIs and Skills for AI agents.
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
MCP server for building and testing AI agents with multi-model experimentation and insights.
Related MCP Servers
- AlicenseNot gradedqualityAmaintenanceMCP server that enables AI agents to run a deterministic orchestration loop with decomposition, subagent execution, and review feedback across multiple LLM backends.55MIT
- FlicenseNot gradedqualityCmaintenanceMulti-model agent orchestration MCP server that enables plan-code-review-deliver pipelines with configurable providers and models.
- AlicenseNot gradedqualityAmaintenanceA lightweight, local MCP server for Mac that replicates a 3-stage LLM council workflow using OpenRouter, enabling multi-model deliberation and synthesis directly from AI coding tools.7MIT
- FlicenseNot gradedqualityCmaintenanceAn MCP server that provides an AI LLM orchestrator supporting multiple providers (LM Studio, Ollama, OpenAI, generic) plus SQLite-backed memory, kanban, and todo databases for persistent task and knowledge management.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/HoAnTrieu/sovereign-exoself-MCP'
If you have feedback or need assistance with the MCP directory API, please join our Discord server