Custom Tools

Custom Tools

Expose your own MATLAB functions as first-class MCP tools. AI agents can discover and call them directly, with full parameter validation and help text.

How It Works

1. Write your MATLAB function (.m file)
2. Describe it in custom_tools.yaml
3. The server loads and registers it as an MCP tool at startup
4. Agents see it alongside built-in tools

Configuration

Point your config.yaml to the custom tools file:

custom_tools:
  config_file: "./custom_tools.yaml"

YAML Schema

tools:
  - name: tool_name                     # MCP tool name (what agents call)
    matlab_function: pkg.func           # MATLAB function to call
    description: "What it does"         # Shown to agents
    parameters:                         # Optional list of parameters
      - name: param_name
        type: str                       # Parameter type (see table below)
        required: true                  # or false
        description: "Parameter help"   # Optional description shown to agent
        default: value                  # Default if not required
    returns: "Description of return value"  # Optional description of return value

Parameter Types

YAML Type	Aliases	Python Type	Notes
str	string	str	String parameter
int	integer	int	Integer parameter
float	number	float	Floating-point parameter
double	—	float	Alias for float (MATLAB convention)
bool	boolean	bool	Boolean parameter
logical	—	bool	Alias for bool (MATLAB convention)
list	—	list	List/array parameter
dict	—	dict	Dictionary/struct parameter
any	—	Any	Any type (no validation)

Complete Example

1. MATLAB Function (mylib/analyze_signal.m)

function result = analyze_signal(signal_path, sample_rate, window_size)
    % ANALYZE_SIGNAL  Frequency analysis of a signal file
    %
    %   result = analyze_signal(signal_path, sample_rate, window_size)
    %
    %   Returns struct with: frequencies, magnitudes, snr, peaks

    data = load(signal_path);
    signal = data.signal;

    N = length(signal);
    Y = fft(signal, window_size);
    f = (0:window_size/2-1) * sample_rate / window_size;
    mag = abs(Y(1:window_size/2)) / N;

    [peaks, locs] = findpeaks(mag, 'MinPeakHeight', max(mag)*0.1);

    result.frequencies = f;
    result.magnitudes = mag;
    result.snr = snr(signal);
    result.peaks = struct('frequencies', f(locs), 'amplitudes', peaks);
end

2. Custom Tool Definition (custom_tools.yaml)

tools:
  - name: analyze_signal
    matlab_function: mylib.analyze_signal
    description: >
      Analyze a signal file and return frequency components, SNR,
      and peak detection results.
    parameters:
      - name: signal_path
        type: string
        required: true
        description: "Path to the signal data file (.mat)"
      - name: sample_rate
        type: double
        required: true
        description: "Sample rate in Hz"
      - name: window_size
        type: int
        default: 1024
        description: "FFT window size"
    returns: "Struct with fields: frequencies, magnitudes, snr, peaks"

3. Make Sure MATLAB Can Find It

Add the directory containing your .m files to the workspace paths in config.yaml:

workspace:
  default_paths:
    - "/path/to/mylib"

4. Agent Usage

The agent now sees analyze_signal as a tool and can call it:

"Analyze the signal in data/recording.mat at 44100 Hz sample rate"

The server:

1. Validates parameters against the YAML schema
2. Calls analyze_signal('data/recording.mat', 44100, 1024) in MATLAB
3. Returns the result to the agent

Loading and Registration at Startup

When the MCP server starts:

1. The config.yaml is parsed for the custom_tools.config_file setting
2. The YAML file is loaded using load_custom_tools()
3. Each tool definition is validated against the CustomToolDef schema
4. For each valid tool, make_custom_tool_handler() creates an async handler function with the proper inspect.Signature
5. The handler is registered with FastMCP via server.tool() decorator
6. Invalid tool definitions are logged as warnings and skipped

If the custom tools config file does not exist or contains no tools section, an empty list is returned and no custom tools are registered.

Multiple Tools

tools:
  - name: analyze_signal
    matlab_function: mylib.analyze_signal
    description: "Frequency analysis of signal files"
    parameters:
      - name: signal_path
        type: string
        required: true
        description: "Path to the signal file"
    returns: "Frequency analysis struct"

  - name: train_model
    matlab_function: ml.train_classifier
    description: "Train a classification model"
    parameters:
      - name: dataset_path
        type: string
        required: true
        description: "Path to training data (.mat or .csv)"
      - name: model_type
        type: string
        default: "svm"
        description: "Model type: svm, tree, knn, ensemble"
      - name: validation_split
        type: double
        default: 0.2
        description: "Fraction of data to use for validation (0-1)"
    returns: "Trained model and accuracy metrics"

  - name: process_image
    matlab_function: imgtools.enhance_image
    description: "Image enhancement pipeline"
    parameters:
      - name: image_path
        type: string
        required: true
        description: "Path to the input image"
      - name: denoise_strength
        type: double
        default: 0.5
        description: "Denoising strength (0=none, 1=maximum)"
      - name: sharpen
        type: logical
        default: false
        description: "Apply sharpening filter"
    returns: "Enhanced image saved to temp directory"

  - name: compute_statistics
    matlab_function: stats.compute_summary
    description: "Compute summary statistics for a data file"
    parameters:
      - name: data_path
        type: string
        required: true
        description: "Path to the .mat file containing the data variable"
    returns: "Struct with mean, std, median, min, max, histogram data"

Tips

• Function names with packages: Use pkg.func notation to call functions in MATLAB packages (e.g., +mylib/analyze_signal.m → mylib.analyze_signal)
• Parameter descriptions: Include a description field for each parameter to help agents understand what each parameter does
• MATLAB conventions: Use double and logical type aliases to match MATLAB naming conventions
• MEX files: Custom tools work with .mex files too — just reference the function name without the extension
• Error handling: If the MATLAB function throws an error, the MCP server returns a structured error response to the agent
• Testing: Test your functions in MATLAB first before exposing them as tools
• Type validation: The server validates all parameters against their declared types before calling the MATLAB function