AI Agent Integration Guide & Model Context Protocol (MCP) Server

July 9, 2026 · View on GitHub

This document explains how AI coding assistants, autonomous database agents, and developer workflows can utilize MySQLTuner via the Model Context Protocol (MCP) or direct --agent-json CLI queries to audit and optimize database instances safely.

🚀 Overview of Integration Modes

MySQLTuner supports two native modes for machine integration:

  1. Direct CLI (--agent-json mode): High-speed, zero-dependency JSON output representing actionable recommendations. Excellent for scripts and one-off agent executions.
  2. MCP Server (stdio transport): Standardized protocol interface exposing tools and resources for LLM agents to query state, run audits, and execute rollback-enabled database optimizations.

1. Direct CLI Integration: --agent-json

When running with --agent-json, MySQLTuner suppresses all human-readable ANSI terminal outputs and prints a single, structured JSON payload to stdout containing actionable findings.

CLI Invocation

perl mysqltuner.pl --agent-json --host <db_host> --user <db_user> --pass <db_pass>

Output Schema Definition

Each recommendation returned in the findings list adheres to the following structure:

  • id: Unique deterministic identifier for the recommendation type (e.g., innodb_buffer_pool_size_adjust).
  • topic: Category of the finding (Performance, Security, Reliability, Modeling, Replication).
  • description: Human-readable explanation of the issue.
  • impact_score: Estimated benefit score from 1 (lowest) to 10 (highest).
  • risk_level: Safety assessment associated with applying the recommendation (Low, Medium, High, Critical).
  • risk_description: Potential side effects (e.g. table locks, memory consumption).
  • requires_restart: Boolean (true/false) indicating if the change requires restarting the database service.
  • expected_outcome: Predicted benefit (e.g. "Reduces disk I/O").
  • action: Object containing:
    • type: Either SQL (can be executed live) or Config (requires editing my.cnf).
    • statement: The exact statement to execute or configuration line to write.
    • rollback_statement: The exact command to revert the action to its original baseline state.

Actionable JSON Output Example

{
  "findings": [
    {
      "id": "innodb_buffer_pool_size_adjust",
      "topic": "Performance",
      "description": "InnoDB buffer pool size is under-allocated for the current workload.",
      "impact_score": 9,
      "risk_level": "Medium",
      "risk_description": "Increases memory consumption. Ensure sufficient OS-free RAM to prevent OOM swapping.",
      "requires_restart": false,
      "expected_outcome": "Reduces disk I/O and increases query cache read hits.",
      "action": {
        "type": "SQL",
        "statement": "SET GLOBAL innodb_buffer_pool_size = 1073741824;",
        "rollback_statement": "SET GLOBAL innodb_buffer_pool_size = 134217728;"
      }
    }
  ]
}

2. Model Context Protocol (MCP) Integration

The containerized MCP server integrates directly with client systems (like Cursor, Claude Desktop, Antigravity, or custom LangChain/LlamaIndex pipelines).

Running the MCP Service Container

Deploy the Docker image next to your database server:

docker run -d \
  --name mysqltuner-mcp \
  -e DB_HOST=mysql-server \
  -e DB_USER=root \
  -e DB_PASSWORD=secret_pass \
  -e AUDIT_INTERVAL_HOURS=12 \
  -v /var/cache/mysqltuner:/var/cache/mysqltuner \
  mysqltuner-mcp

Connecting to the MCP Server

Clients communicate with the server over stdio (standard input/output) transport. Configure your agent IDE config (e.g. project_config.json or CLAUDE desktop config) to spawn the server:

{
  "mcpServers": {
    "mysqltuner": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "DB_HOST=127.0.0.1", "mysqltuner-mcp"]
    }
  }
}

🛠️ Exposed MCP Tools & Resources

MCP Resources (Observability)

  • mysqltuner://reports/latest.json: Accesses the latest cached JSON report containing findings and DB variables status.
  • mysqltuner://reports/latest.html: Retreives the complete interactive pgBadger-inspired HTML analytics dashboard.

MCP Tools (Actions)

Agents can invoke these JSON-RPC methods:

  1. get_latest_audit

    • Description: Retrieves cached audit findings instantly without stressing the database.
    • Arguments: None.

  2. run_audit

    • Description: Triggers a fresh run of the Perl engine and returns immediate recommendations.
    • Arguments: None.

  3. apply_recommendation

    • Description: Applies a safe SQL adjustment (e.g. SET GLOBAL).
    • Arguments:
      • statement (string, required): The SQL command to execute.
      • variable_name (string, optional): Variable name being changed to capture and save the rollback state.

  4. rollback_recommendation

    • Description: Reverts a previously executed command using cached transaction state.
    • Arguments:
      • statement_id (string, required): The ID of the transaction to revert.

🧠 Step-by-Step AI Agent Playbook

An AI agent performing database maintenance should follow this operational path:

graph TD
    A[Start: Read Latest Audit] --> B{Are there findings?}
    B -- No --> C[End: Database is Tuned]
    B -- Yes --> D[Filter findings by Risk Level]
    D --> E[Filter: Risk <= Medium]
    E --> F[Present to User: Statement & Rollback]
    F --> G{User Confirms?}
    G -- No --> H[Skip recommendation]
    G -- Yes --> I[Execute apply_recommendation]
    I --> J{Performance OK?}
    J -- Yes --> K[Log Transaction Success]
    J -- No --> L[Execute rollback_recommendation]

Agent Prompt Instructions Template (Copy-Paste for LLM Context)

"You have access to the MySQLTuner MCP server. Your goal is to analyze database performance, propose optimizations, and execute them safely.

  1. Run get_latest_audit to fetch current recommendations.
  2. For each recommendation, analyze the impact_score, risk_level, and requires_restart.
  3. Highlight recommendations with high impact_score (>=7) and safe risk_level (Low or Medium).
  4. Present these recommendations to the user, showing the description, the exact statement to apply, and the rollback_statement.
  5. Wait for user consent.
  6. After confirmation, call apply_recommendation to execute the SQL.
  7. If the user complains of issues or latency spikes, run rollback_recommendation immediately with the returned statement_id to restore original performance levels."