What can you do with this server?

The CtrlTest MCP Server provides automated control system evaluation and regression testing for PID and bio-inspired controllers in flapping-wing aircraft systems, exposing a standardized API (ctrltest.analyze_pid) for comprehensive performance analysis. Core Capabilities: * PID Controller Analysis - Evaluate PID gains against plant dynamics (natural frequency, damping ratio) to measure overshoot, settling time, integral squared error (ISE), and Lyapunov stability margins * High-Fidelity Simulation Integration - Ingest data from diffSPH (differentiable smoothed-particle hydrodynamics) and Foam-Agent CFD simulations via extra_metrics for realistic performance assessment * Multi-Modal Scoring - Fuse analytic models with high-fidelity simulation data when both diffSPH and Foam-Agent metrics are available * Gust Rejection Analysis - Quantify disturbance handling through gust detection latency, bandwidth, and rejection percentage metrics * Energy Efficiency Assessment - Measure CPG (Central Pattern Generator) baseline vs. consumed energy and calculate energy reduction percentages * Mix-of-Experts Evaluation - Compute switching penalties, latency budgets, and energy consumption for adaptive control architectures * Automated Integration - Support continuous performance monitoring, automated gain tuning, and controller optimization through STDIO/HTTP API integration with ToolHive, RL algorithms, and evolutionary systems * Structured Output - Export JSON-formatted results with provenance metadata for dashboards, logging, and downstream analysis

Which integrations are available for this server?

Provides a REST API interface for the control system evaluation service, allowing HTTP-based access to controller benchmarking and scoring functionality. Enables visualization of controller performance metrics and analytics data exported from PID evaluations and comparative controller assessments.

How do I use CtrlTest MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@CtrlTest MCP Server evaluate PID gains kp=2.0, ki=0.5, kd=0.12 for a second-order plant with natural frequency 3.2Hz" 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.

CtrlTest MCP Server

by Three-Little-Birds

Overview Schema Related Servers Score Discussions

Python

Remote

ctrltest-mcp - Flight-control regression lab for MCP agents

TL;DR: Evaluate PID and bio-inspired controllers against analytic or diffSPH/Foam-Agent data through MCP, logging overshoot, energy, and gust metrics automatically.

Related MCP server: shewhart-mcp

What it provides

Scenario	Value
Analytic PID benchmarking	Run closed-form plant models and produce overshoot/settling/energy metrics without manual scripting.
High-fidelity scoring	Ingest logged data from Foam-Agent or diffSPH runs and fuse it into controller evaluations.
MCP integration	Expose the scoring API via STDIO/HTTP so ToolHive or other clients can automate gain tuning and generate continuous performance scorecards.

Quickstart

uv pip install "git+https://github.com/Three-Little-Birds/ctrltest-mcp.git"

Run a PID evaluation:

from ctrltest_mcp import (
    ControlAnalysisInput,
    ControlPlant,
    ControlSimulation,
    PIDGains,
    evaluate_control,
)

request = ControlAnalysisInput(
    plant=ControlPlant(natural_frequency_hz=3.2, damping_ratio=0.35),
    gains=PIDGains(kp=2.0, ki=0.5, kd=0.12),
    simulation=ControlSimulation(duration_s=3.0, sample_points=400),
    setpoint=0.2,
)
response = evaluate_control(request)
print(response.model_dump())

Typical outputs (analytic only):

{
  "overshoot": -0.034024863556091134,
  "ise": 0.008612387509182674,
  "settling_time": 3.0,
  "gust_detection_latency_ms": 0.8,
  "gust_detection_bandwidth_hz": 1200.0,
  "gust_rejection_pct": 0.396,
  "cpg_energy_baseline_j": 12.0,
  "cpg_energy_consumed_j": 7.8,
  "cpg_energy_reduction_pct": 0.35,
  "lyapunov_margin": 0.12,
  "moe_switch_penalty": 0.135,
  "moe_latency_ms": 12.72,
  "moe_energy_j": 3.9,
  "multi_modal_score": null,
  "extra_metrics": null,
  "metadata": {"solver": "analytic"}
}

The analytic plant example above clips settling_time at the requested simulation duration (duration_s=3.0). Increase the horizon if you need the loop to settle fully before computing that metric.

Run as a service

CLI (STDIO transport)

uvx ctrltest-mcp  # runs the MCP over stdio
# or just python -m ctrltest_mcp

Use python -m ctrltest_mcp --describe to print basic metadata without starting the server.

FastAPI (REST)

uv run uvicorn ctrltest_mcp.fastapi_app:create_app --factory --port 8005

python-sdk tool (STDIO / MCP)

from mcp.server.fastmcp import FastMCP
from ctrltest_mcp.tool import build_tool

mcp = FastMCP("ctrltest-mcp", "Flapping-wing control regression")
build_tool(mcp)

if __name__ == "__main__":
    mcp.run()

ToolHive smoke test

Run the integration script from your workspace root:

uvx --with 'mcp==1.20.0' python scripts/integration/run_ctrltest.py

The smoke test runs the analytic path by default. To exercise high-fidelity scoring, stage Foam-Agent archives under logs/foam_agent/ and diffSPH gradients under logs/diffsph/ before launching the script.

Agent playbook

Gust rejection - feed archived diffSPH gradients (diffsph_metrics) and Foam-Agent archives (paths returned by those services) to quantify adaptive CPG improvements.
Controller comparison - log analytics for multiple PID gains, export JSONL evidence, and visualise in Grafana.
Policy evaluation - integrate with RL or evolutionary algorithms; metrics are structured for automated scoring.

Stretch ideas

Extend the adapter for PteraControls (planned once upstream Python bindings are published).
Drive the MCP from scripts/fitness to populate nightly scorecards.
Combine with migration-mcp to explore route-specific disturbance budgets.

Accessibility & upkeep

Run uv run pytest (tests mock diffSPH/Foam-Agent inputs and assert deterministic analytic results).
Keep metric schema changes documented—downstream dashboards rely on them.

Metric schema at a glance

Field	Units	Notes
`overshoot`	radians	peak response minus setpoint
`ise`	rad²·s	integral squared error
`settling_time`	seconds	first time error stays within tolerance
`gust_detection_latency_ms`	milliseconds	detector latency
`gust_detection_bandwidth_hz`	hertz	detector bandwidth
`gust_rejection_pct`	0–1	fraction of disturbance rejected
`cpg_energy_baseline_j`	joules	energy pre-adaptation
`cpg_energy_consumed_j`	joules	energy post-adaptation
`cpg_energy_reduction_pct`	0–1	energy reduction ratio
`lyapunov_margin`	unitless	stability margin
`moe_switch_penalty`	unitless	cost weight × switches
`moe_latency_ms`	milliseconds	latency budget after switching
`moe_energy_j`	joules	mix-of-experts energy draw
`multi_modal_score`	unitless	only when both diffSPH & Foam metrics are present
`extra_metrics`	varies	raw diffSPH/Foam metrics merged in

Example of fused high-fidelity metrics:

{
  "extra_metrics": {
    "force_gradient_norm": 0.87,
    "lift_drag_ratio": 18.4
  },
  "multi_modal_score": 0.047,
  "metadata": {"solver": "analytic"}
}

Contributing

uv pip install --system -e .[dev]
uv run ruff check . and uv run pytest
Share sample metrics in PRs so reviewers can sanity-check improvements quickly.

MIT license - see LICENSE.

Available Tools

1 tool

ctrltest.analyze_pidC

Score PID gains for a flapping-wing plant. Provide plant dynamics and optional gradients/metadata. Returns key control metrics plus provenance. Example input: {"plant":{"natural_frequency_hz":4.2,"damping_ratio":0.45},"gains":{"kp":1.1,"ki":0.2,"kd":0.08},"diffsph_metrics":{"force_gradient_norm":1.9}}

ParametersJSON Schema

Name	Required	Description	Default
`request`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`ise`	Yes
`metadata`	No
`overshoot`	Yes
`moe_energy_j`	Yes
`extra_metrics`	No
`settling_time`	Yes
`moe_latency_ms`	Yes
`lyapunov_margin`	Yes
`multi_modal_score`	No
`gust_rejection_pct`	Yes
`moe_switch_penalty`	Yes
`cpg_energy_baseline_j`	Yes
`cpg_energy_consumed_j`	Yes
`cpg_energy_reduction_pct`	Yes
`gust_detection_latency_ms`	Yes
`gust_detection_bandwidth_hz`	Yes

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions what the tool returns ('key control metrics plus provenance'), it doesn't describe important behavioral aspects like computational requirements, accuracy limitations, whether it's a simulation or real-time analysis, error conditions, or performance characteristics. The description is insufficient for a complex control analysis tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise with two sentences plus an example. The first sentence states the purpose and required inputs, the second describes the output, and the example provides concrete illustration. However, the example could be more focused on illustrating the structure rather than specific values.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complex parameter structure (1 top-level parameter with 10 nested properties across multiple objects), 0% schema description coverage, and no annotations, the description is incomplete. While an output schema exists (which helps), the description doesn't adequately explain the sophisticated control engineering concepts involved or the tool's operational context. The example helps but doesn't compensate for the missing conceptual explanation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description mentions 'plant dynamics and optional gradients/metadata' which aligns with the input schema's structure. However, with 0% schema description coverage, the description doesn't adequately explain the complex nested parameter structure (plant, gains, simulation, setpoint, gust_detector, adaptive_cpg, moe_router, diffsph_metrics, foam_metrics, prefer_high_fidelity). The example input shows some parameters but doesn't cover the full complexity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Score PID gains for a flapping-wing plant' with specific verb ('Score') and resource ('PID gains'). It mentions providing 'plant dynamics and optional gradients/metadata' and returning 'key control metrics plus provenance'. However, without sibling tools, we cannot assess differentiation from alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions the tool's function but offers no context about prerequisites, typical use cases, or limitations. The example input shows what data to provide, but doesn't explain when this analysis would be appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

1 tool updatev1.0.0Jan 3, 2026

Changedctrltest.analyze_pid2 fields changed

addedInput schema / $defs

Added value: +{
+  "AdaptiveCPGConfig": {
+    "properties": {
+      "energy_baseline_j": {
+        "default": 12,
+        "exclusiveMinimum": 0,
+        "title": "Energy Baseline J",
+        "type": "number"
+      },
+      "energy_reduction_pct": {
+        "default": 0.35,
+        "maximum": 0.95,
+        "minimum": 0,
+        "title": "Energy Reduction Pct",
+        "type": "number"
+      },
+      "lyapunov_margin": {
+        "default": 0.12,
+        "minimum": 0,
+        "title": "Lyapunov Margin",
+        "type": "number"
+      },
+      "target_rejection_pct": {
+        "default": 0.45,
+        "maximum": 0.95,
+        "minimum": 0,
+        "title": "Target Rejection Pct",
+        "type": "number"
+      }
+    },
+    "title": "AdaptiveCPGConfig",
+    "type": "object"
+  },
+  "ControlAnalysisInput": {
+    "properties": {
+      "adaptive_cpg": {
+        "$ref": "#/$defs/AdaptiveCPGConfig"
+      },
+      "diffsph_metrics": {
+        "anyOf": [
+          {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "title": "Diffsph Metrics"
+      },
+      "foam_metrics": {
+        "anyOf": [
+          {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "title": "Foam Metrics"
+      },
+      "gains": {
+        "$ref": "#/$defs/PIDGains"
+      },
+      "gust_detector": {
+        "$ref": "#/$defs/GustDetectorConfig"
+      },
+      "moe_router": {
+        "$ref": "#/$defs/MoERouterConfig"
+      },
+      "plant": {
+        "$ref": "#/$defs/ControlPlant"
+      },
+      "prefer_high_fidelity": {
+        "default": true,
+        "description": "Attempt to use PteraControls when available before falling back to the analytic surrogate.",
+        "title": "Prefer High Fidelity",
+        "type": "boolean"
+      },
+      "setpoint": {
+        "default": 0,
+        "title": "Setpoint",
+        "type": "number"
+      },
+      "simulation": {
+        "$ref": "#/$defs/ControlSimulation"
+      }
+    },
+    "required": [
+      "plant",
+      "gains"
+    ],
+    "title": "ControlAnalysisInput",
+    "type": "object"
+  },
+  "ControlPlant": {
+    "properties": {
+      "damping_ratio": {
+        "minimum": 0,
+        "title": "Damping Ratio",
+        "type": "number"
+      },
+      "natural_frequency_hz": {
+        "exclusiveMinimum": 0,
+        "title": "Natural Frequency Hz",
+        "type": "number"
+      },
+      "settling_tolerance_rad": {
+        "default": 0.02,
+        "exclusiveMinimum": 0,
+        "title": "Settling Tolerance Rad",
+        "type": "number"
+      },
+      "trim_setpoint": {
+        "default": 0,
+        "title": "Trim Setpoint",
+        "type": "number"
+      }
+    },
+    "required": [
+      "natural_frequency_hz",
+      "damping_ratio"
+    ],
+    "title": "ControlPlant",
+    "type": "object"
+  },
+  "ControlSimulation": {
+    "properties": {
+      "duration_s": {
+        "default": 5,
+        "exclusiveMinimum": 0,
+        "title": "Duration S",
+        "type": "number"
+      },
+      "sample_points": {
+        "default": 500,
+        "maximum": 5000,
+        "minimum": 50,
+        "title": "Sample Points",
+        "type": "integer"
+      }
+    },
+    "title": "ControlSimulation",
+    "type": "object"
+  },
+  "GustDetectorConfig": {
+    "properties": {
+      "bandwidth_hz": {
+        "default": 1200,
+        "minimum": 10,
+        "title": "Bandwidth Hz",
+        "type": "number"
+      },
+      "latency_ms": {
+        "default": 0.8,
+        "minimum": 0.1,
+        "title": "Latency Ms",
+        "type": "number"
+      },
+      "sensitivity": {
+        "default": 0.88,
+        "maximum": 1,
+        "minimum": 0,
+        "title": "Sensitivity",
+        "type": "number"
+      }
+    },
+    "title": "GustDetectorConfig",
+    "type": "object"
+  },
+  "MoERouterConfig": {
+    "properties": {
+      "energy_budget_j": {
+        "default": 6,
+        "exclusiveMinimum": 0,
+        "title": "Energy Budget J",
+        "type": "number"
+      },
+      "latency_budget_ms": {
+        "default": 12,
+        "exclusiveMinimum": 0,
+        "title": "Latency Budget Ms",
+        "type": "number"
+      },
+      "switch_cost_weight": {
+        "default": 0.045,
+        "minimum": 0,
+        "title": "Switch Cost Weight",
+        "type": "number"
+      },
+      "switch_events": {
+        "default": 3,
+        "minimum": 0,
+        "title": "Switch Events",
+        "type": "integer"
+      }
+    },
+    "title": "MoERouterConfig",
+    "type": "object"
+  },
+  "PIDGains": {
+    "properties": {
+      "kd": {
+        "default": 0,
+        "minimum": 0,
+        "title": "Kd",
+        "type": "number"
+      },
+      "ki": {
+        "default": 0,
+        "minimum": 0,
+        "title": "Ki",
+        "type": "number"
+      },
+      "kp": {
+        "minimum": 0,
+        "title": "Kp",
+        "type": "number"
+      }
+    },
+    "required": [
+      "kp"
+    ],
+    "title": "PIDGains",
+    "type": "object"
+  }
+}

addedInput schema / title
```
Added value: +"analyzeArguments"
```

1 tool updateNov 11, 2025
- First observedctrltest.analyze_pid

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.1/5.0

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as analyzing PID gains for a specific type of plant, making it distinct by default.

Naming Consistency5/5

The single tool name follows a consistent pattern with a clear namespace prefix (ctrltest) and descriptive action (analyze_pid). With only one tool, naming consistency is inherently perfect as there are no other tools to compare against.

Tool Count2/5

A single tool is generally too few for most server purposes, as it limits functionality and forces agents to rely on one operation. While it might be appropriate for a highly specialized task, the server's name 'CtrlTest MCP Server' suggests a broader control testing domain where more tools would be expected for completeness.

Completeness2/5

The server appears focused on control system testing, but with only one analysis tool, there are significant gaps. Missing operations likely include tools for setting up tests, running simulations, comparing results, or managing test configurations, making the surface severely incomplete for the implied domain.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

Reddit Discussion

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ctrltest-mcp - Flight-control regression lab for MCP agents

Table of contents

What it provides

Quickstart

Run as a service

CLI (STDIO transport)

FastAPI (REST)

python-sdk tool (STDIO / MCP)

ToolHive smoke test

Agent playbook

Stretch ideas

Accessibility & upkeep

Metric schema at a glance

Contributing

Available Tools

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

AutoDev MCP

shewhart-mcp

kshana-mcp

agent-eval-mcp

Latest Blog Posts

MCP directory API