proxysql/scripts/mcp/DiscoveryAgent/Rich

Database Discovery Agent (Async CLI Prototype)

This prototype is a single-file Python CLI that runs an LLM-driven database discovery agent against an existing MCP Query endpoint.

It is designed to be:

• Simple to run (no web server, no SSE, no background services)
• Asynchronous (uses asyncio + async HTTP clients)
• Easy to troubleshoot
  • --trace trace.jsonl records every LLM request/response and every MCP tool call/result
  • --debug shows stack traces

The UI is rendered in the terminal using Rich (live dashboard + status).

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What the script does

The CLI (discover_cli.py) implements a minimal but real “multi-expert” agent:

• A Planner (LLM) decides what to do next (bounded list of tasks).
• Multiple Experts (LLM) execute tasks:
  • Structural: table shapes, constraints, relationship candidates
  • Statistical: table/column profiling, sampling
  • Semantic: domain inference, entity meaning, asks questions when needed
  • Query: explain plans and safe read-only validation (optional)

Experts do not talk to the database directly. They only request MCP tools. Discoveries can be stored in the MCP catalog (if your MCP provides catalog tools).

Core loop

1. Bootstrap

   • list_schemas
   • choose schema (--schema or first returned)
   • list_tables(schema)

2. Iterate (up to --max-iterations)

   • Planner LLM produces up to 1–6 tasks (bounded)
   • Orchestrator executes up to --tasks-per-iter tasks:
     • Expert ACT: choose MCP tool calls
     • MCP tool calls executed
     • Expert REFLECT: synthesize insights + catalog writes + optional questions
     • Catalog writes applied via catalog_upsert (if present)

3. Stop

   • when max iterations reached, or
   • when the run shows diminishing returns (simple heuristic)

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Install

Create a venv and install dependencies:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

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Configuration

The script needs two endpoints:

1. MCP Query endpoint (JSON-RPC)
2. LLM endpoint (OpenAI-compatible /v1/chat/completions)

You can configure via environment variables or CLI flags.

MCP configuration

export MCP_ENDPOINT="https://127.0.0.1:6071/mcp/query"
export MCP_AUTH_TOKEN="YOUR_TOKEN"
export MCP_INSECURE_TLS="1"
# export MCP_AUTH_TOKEN="..."     # if your MCP needs auth

CLI flags override env vars:

• --mcp-endpoint
• --mcp-auth-token
• --mcp-insecure-tls

LLM configuration

The LLM client expects an OpenAI‑compatible /chat/completions endpoint.

For OpenAI:

export LLM_BASE_URL="https://api.openai.com/v1" # must include `v1`
export LLM_API_KEY="YOUR_KEY"
export LLM_MODEL="gpt-4o-mini"

For Z.ai:

export LLM_BASE_URL="https://api.z.ai/api/coding/paas/v4"
export LLM_API_KEY="YOUR_KEY"
export LLM_MODEL="GLM-4.7"

For a local OpenAI‑compatible server (vLLM / llama.cpp / etc.):

export LLM_BASE_URL="http://localhost:8001"   # example
export LLM_API_KEY=""                         # often unused locally
export LLM_MODEL="your-model-name"

CLI flags override env vars:

• --llm-base-url
• --llm-api-key
• --llm-model

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Run

Start a discovery run

python discover_cli.py run --max-iterations 6 --tasks-per-iter 3

Focus on a specific schema

python discover_cli.py run --schema public

Debugging mode (stack traces)

python discover_cli.py run --debug

Trace everything to a file (recommended)

python discover_cli.py run --trace trace.jsonl

The trace is JSONL and includes:

• llm.request, llm.raw, and optional llm.repair.*
• mcp.call and mcp.result
• error and error.traceback (when --debug)

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Provide user intent (optional)

Store intent in the MCP catalog so it influences planning:

python discover_cli.py intent --run-id <RUN_ID> --audience support --goals qna documentation
python discover_cli.py intent --run-id <RUN_ID> --constraint max_db_load=low --constraint max_seconds=120

The agent reads intent from:

• kind=intent
• key=intent/<run_id>

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Troubleshooting

If it errors and you don’t know where:

1. re-run with --trace trace.jsonl --debug
2. open the trace and find the last llm.request / mcp.call

Common issues:

• invalid JSON from the LLM (see llm.raw)
• disallowed tool calls (allow-lists)
• MCP tool failure (see last mcp.call)

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Safety notes

The Query expert can call run_sql_readonly if the planner chooses it. To disable SQL execution, remove run_sql_readonly from the Query expert allow-list.

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License

Prototype / internal use.