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name: matlab-debug-code description: Diagnose MATLAB errors and unexpected behavior. Breakpoints, workspace inspection, try-catch diagnostics, and common error patterns. Use when debugging functions, tracing errors, inspecting variables, or diagnosing runtime failures. license: https://www.mathworks.com/content/dam/mathworks/license/pmrl/license.md metadata: author: MathWorks version: "3.0"

Investigating and Debugging MATLAB Code with MCP Tools

You have access to a live MATLAB session via MCP tools. Use them to actively investigate code — whether debugging errors, understanding behavior, or answering questions about how MATLAB code works. Don't just guess from code alone.

When to Use

• User encounters a MATLAB error message or unexpected result
• User wants to set breakpoints or inspect variable state
• Tracing why a function produces wrong output
• NaN/Inf values appearing unexpectedly
• A MATLAB MCP tool returns an error or exception — including runtime errors, syntax errors, undefined function/variable errors, or failed test results
• User asks "why is my MATLAB code not working", "help me debug", or shares a MATLAB stack trace

When NOT to Use

• Code quality review without a runtime problem — use matlab-reviewing-code instead
• Performance profiling — use performance optimization workflows
• Writing tests for correctness — use matlab-write-tests instead
• Understanding MATLAB APIs or language features without a specific bug

Static Analysis vs Runtime Debugging

Not every issue needs the live MATLAB session. Choose the right approach:

• Static analysis is enough when: syntax errors, unused variables, obvious logic mistakes, or issues visible from reading the source code alone. Use the Read tool and check_matlab_code.
• Runtime debugging is needed when:
  • The error depends on actual data values, types, or dimensions
  • The output is wrong but the code looks correct
  • The user says "it doesn't work" but the code looks fine — check actual data
  • You need to know what variables contain at a specific point in execution

When in doubt, start with static analysis. Escalate to runtime debugging when you can't determine the root cause from source alone.

Auto-Trigger on MATLAB Errors

When a MATLAB MCP tool returns an error (runtime error, syntax error, undefined function/variable, dimension mismatch, failed assertion, etc.), do not silently move on or guess at a fix. Instead:

1. Recognize the error — Look for patterns like Error using ..., Undefined function or variable, Index exceeds ..., Error in ..., MATLAB stack traces, or failed test results in MCP tool output.
2. Ask the user for permission — Before launching into investigation, offer:

   "I noticed a MATLAB error: <brief error summary>. I can use the matlab-debug-code skill to dig into this — inspect variables, trace the call stack, and identify the root cause. Want me to investigate?"

3. Proceed only after confirmation — Once the user agrees, follow the investigation workflow below.

This applies whether the error came from the user running code, or from you running code on the user's behalf (e.g., verifying a fix, running tests).

Available Tools

| Tool | Use For | |------|---------| | mcp__matlab__run_matlab_file | Run .m scripts — prefer this for executing user scripts and verifying fixes | | mcp__matlab__evaluate_matlab_code | Quick diagnostics: inspect variables, evaluate expressions, test small snippets | | mcp__matlab__check_matlab_code | Static analysis of .m files (warnings, unused vars, potential issues) | | mcp__matlab__run_matlab_test_file | Run a MATLAB test file | | mcp__matlab__detect_matlab_toolboxes | List installed toolboxes — use when "Undefined function" may be a missing toolbox |

Prefer run_matlab_file over evaluate_matlab_code for running scripts. Only use evaluate_matlab_code for short diagnostic commands (checking a variable, testing an expression, etc.) — not for re-running entire scripts inline.

Workflow

1. Understand the Goal

Determine what the user needs:

• Debugging — runtime error, wrong output, unexpected behavior
• Understanding — how does this code work, what does this function do
• Investigating — why does this variable have this value, where does this data come from
• Exploring — what functions are available, how is this codebase structured

2. Gather Information via MATLAB

Use mcp__matlab__evaluate_matlab_code to run diagnostic commands.

Read source code: Use the Read tool for .m files on disk — not MATLAB's type or dbtype. Only use MATLAB's which to locate files you haven't found yet, and which -all to check for shadowing.

Preview large data: Use varName(1:min(5,end),:) or head(T) to preview slices instead of dumping entire variables.

Runtime debugging — check desktop mode first:

Before using breakpoints, check if MATLAB has a desktop:

desktop('-inuse')  % true = desktop mode, false = no-desktop

Desktop mode (desktop('-inuse') returns true):

Use the full breakpoint workflow:

1. Set a breakpoint before running:

   dbstop if error          % Pause on any error
   dbstop if caught error   % Pause on error inside try-catch (silent failures)
   dbstop if warning        % Pause when a warning is issued
   dbstop if naninf         % Pause on NaN or Inf
   dbstop in file at line   % Pause at a specific line
   

2. Run the code via run_matlab_file — MATLAB pauses at the breakpoint.
3. Inspect the call stack:

   dbstack                  % See full call stack with file names and line numbers
   

4. whos to see what's in scope — check variable names, sizes, and types before inspecting any values. For large arrays/tables, preview a slice (varName(1:5,:), head(T)) instead of displaying the whole thing.
5. Navigate frames and inspect variables in each scope:

   dbup                     % Move up one frame (toward caller)
   dbdown                   % Move back down (toward callee)
   

   After dbup/dbdown, variable inspection commands operate in that frame's local scope — use this to check inputs/outputs at each level.
6. Resume or exit:

   dbcont                   % Continue execution to next breakpoint or end
   dbquit                   % Exit debug mode entirely
   dbclear all              % Remove all breakpoints when done
   

Note: Interactive stepping (dbstep) is unreliable via MCP — each evaluate_matlab_code call is a separate command, so step state may not persist.

No-desktop mode (desktop('-inuse') returns false):

Do NOT use dbstop if error, dbstop if naninf, dbstop if warning, or any breakpoint that pauses execution. In no-desktop mode, pausing breakpoints cause the MCP eval to hang indefinitely.

Use these strategies instead:

1. try-catch wrappers — Wrap suspect code to capture the error and inspect state after failure:

   try
       result = suspectFunction(data);
   catch ME
       fprintf('Error: %s\n', ME.message);
       fprintf('In: %s line %d\n', ME.stack(1).name, ME.stack(1).line);
       whos  % Show variables in scope at failure
   end
   

2. Tracer conditional breakpoints — Use dbstop with a condition that always returns false so MATLAB never actually pauses, but prints the value as a side effect. Define a tracer function:

   function out = tracer(val)
       disp(val);
       out = false;
   end
   

   Then set a conditional breakpoint that calls it:

   dbstop in myScript at 42 if tracer(myVar)
   

   When line 42 executes, MATLAB evaluates tracer(myVar), which prints the value and returns false — execution continues without pausing. This probes variables at specific lines without modifying the source file. Remove tracer breakpoints when done: dbclear all

3. Run then inspect — For scripts, run via run_matlab_file, then use evaluate_matlab_code to inspect workspace variables after execution. The base workspace persists across MCP calls within a session.

3. Investigate Iteratively

This is the core of debugging — use your judgment:

• Read the relevant source code to understand intent
• Inspect variables that appear on or near the failing line
• Evaluate sub-expressions to isolate which part fails
• Test hypotheses by running small snippets in MATLAB
• Be selective — don't dump the entire workspace. Only inspect what is relevant to the problem. If there are 7 variables in scope but only 2 are used on the failing line, inspect those 2.

When the error occurs inside a MathWorks built-in function, the bug is almost always in the user's code that called it. Walk up the stack to user code.

4. Question the Diagnosis

The user's description of the problem may not match the actual problem. Before fixing what they say is broken, verify it yourself:

• Test the claimed failure directly — If the user says ismember or strcmp doesn't work, run it yourself with their actual data. Often the operation works fine and the real issue is the logic around it.
• Check data before blaming code — When operations on data "don't work," inspect the data: types (class), actual values (sample a few), whitespace (strtrim), hidden characters (double(str)), and dimensions (size).
• Verify what the code does vs what the user describes — Read the code and confirm which variables and columns are actually being used. The user may describe their intent but the code may do something different.
• Consider whether the approach itself is wrong — Sometimes the code has no bug per se, but the approach is unnecessarily complex or fragile. Suggest idiomatic MATLAB alternatives: table joins instead of manual loops, vectorized operations instead of element-wise comparisons, built-in functions instead of hand-rolled logic.

Common Errors — What to Check First

| Error Pattern | Diagnostic Steps | |---------------|-----------------| | Undefined function or variable 'X' | which X, exist('X','file'), exist('X','var'), check path, use detect_matlab_toolboxes to verify toolbox is installed | | Index exceeds array dimensions / Index exceeds the number of array elements | size(arr) and inspect the index expression — often off-by-one or empty array | | Not enough input arguments | Check how the function is called at the call site, nargin inside the function, compare with function signature | | Too many input arguments | Same as above — caller passing extra args, or calling a script as if it were a function | | Matrix dimensions must agree / Dimension mismatch | size(A), size(B) for both operands — often one is row and the other column | | Subscript indices must either be real positive integers or logicals | Check index variable: class(idx), min(idx), look for 0 or negative values, NaN, or floating-point indices | | Dot indexing is not supported for variables of this type | class(var) — usually accessing a struct field on a non-struct (cell, array, table) | | Unable to perform assignment | Check class and size of both sides of the assignment | | Out of memory | whos to find large variables, check for accidental array growth in loops | | Maximum recursion limit | Check for missing base case or infinite mutual recursion — dbstack at error point | | NaN propagation / wrong branch taken | In desktop mode, use dbstop if naninf to catch where NaN is first created. In no-desktop mode, use dbstop in file at line if tracer(suspect) to probe values without pausing. NaN comparisons (>, <, ==) are always false, so if takes the wrong branch silently. Trace upstream: check for 0/0, Inf-Inf, or NaN in input data. Use `any(isnan(v