Actor Critic Thinking MCP Server Performance Evaluation

Overview

The Actor Critic Thinking MCP Server provides a structured framework for evaluating Model Context Protocol (MCP) servers using an actor–critic style assessment loop. It automates workload generation, telemetry capture, and policy-driven analysis to locate throughput and latency bottlenecks, then recommends resource allocation or configuration changes to improve end-to-end efficiency.

This tool is useful when you need to compare MCP implementations, stress-test a deployment, or perform iterative optimization guided by learned heuristics. It integrates workload drivers, performance collectors, and an actor–critic evaluator so you can run repeatable experiments and produce actionable reports suitable for capacity planning and CI performance gates.

Features

• Workload generation: configurable synthetic workloads that simulate client interactions with MCP endpoints.
• Actor–critic evaluator: reinforcement-style loop that observes performance and recommends actions (e.g., concurrency, batching).
• Telemetry collection: CPU, memory, latency, throughput, error rates, and token/context usage.
• Policy reports: human-readable suggestions and machine-friendly JSON/CSV outputs for automation.
• Integrations: hooks for Prometheus, Grafana, and log exporters.
• Docker and local runtime support for consistent experimentation across environments.
• Scriptable CLI for batch runs and CI integration.

Installation / Configuration

Clone the repository and install dependencies (Python example). Adjust commands if you prefer Docker.

# Clone the repo
git clone https://github.com/aquarius-wing/actor-critic-thinking-mcp.git
cd actor-critic-thinking-mcp

# Create a virtualenv and install
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Run with default settings:

# Run the evaluator against a local MCP server
python -m mcp_eval.cli --target http://localhost:8080 --config config/default.yaml

Docker Compose example:

# docker-compose.yml
version: "3.8"
services:
  evaluator:
    build: .
    volumes:
      - ./config:/app/config
    environment:
      - TARGET=http://mcp-server:8080
    command: ["python", "-m", "mcp_eval.cli", "--config", "config/default.yaml"]

  mcp-server:
    image: your-mcp-server:latest
    ports:
      - "8080:8080"

Configuration snippet (config/default.yaml):

target: "http://localhost:8080"
workload:
  concurrency: 8
  rps: 50
  duration_s: 300
  payload_template: "templates/simple_prompt.json"
metrics:
  enable_prometheus: true
  push_interval_s: 10
actor_critic:
  policy_learning: true
  exploration_epsilon: 0.1
report:
  output_dir: "./reports"
  formats: ["html","json","csv"]

Available Tools / Resources

• mcp_eval.workload: Workload generator with request templates and arrival patterns (poisson, fixed-rps, burst).
• mcp_eval.collectors: Metric collectors for system and application-level telemetry (psutil, Prometheus client).
• mcp_eval.actor_critic: Actor–critic module that proposes configuration actions and scores outcomes.
• mcp_eval.reporter: Report generator producing CSV, JSON, and basic HTML dashboards.
• Example configs: config/ presets for low/medium/high load and CI smoke tests.
• Dashboards: Grafana dashboards templates (dashboards/grafana.json) for visualizing runs.
• Sample workloads: templates/ with different prompt sizes and context window usage.

Use Cases

• Bottleneck identification

  • Run a medium-duration evaluation to identify whether latency is dominated by CPU, I/O, or model-encoding overhead.
  • Example:

    python -m mcp_eval.cli --target http://localhost:8080 \
      --config config/medium_load.yaml
    

  • Output: latency distribution, p50/p95/p99, CPU utilization, and recommended concurrency limit.

• Resource optimization

  • Let the actor–critic policy suggest adjustments (batch sizes, worker counts, memory limits).
  • Example workflow:
    1. Baseline run (record metrics).
    2. Run with policy enabled to suggest a parameter change.
    3. Apply change and re-run to validate improvement.

• Regression testing in CI

  • Automate short smoke runs on each PR to detect performance regressions.
  • Example GitHub Actions step:

    - name: Run MCP perf smoke test
      run: docker-compose run --rm evaluator python -m mcp_eval.cli --config config/ci_smoke.yaml
    

• Comparative evaluation

  • Compare two MCP implementations or two configuration states and produce a diff report (throughput, latency, token usage).

Example Output Metrics

Use the JSON/CSV outputs for automated analysis or funnel the Prometheus metrics into Grafana for continuous monitoring.

Tips

• Start with low concurrency to characterize per-request cost, then scale up to find system limits.
• Use payload templates that reflect real usage (context length, token distribution).
• Enable Prometheus integration for longer experiments and richer visualization.
• Keep experiment environment consistent: same image versions, isolation from other noisy processes.