Related MCP server: matlab-mcp-server-python
matlab://documentation/getting-started URI를 통해 MATLAB 설명서에 액세스하세요.
예제와 사용 지침이 포함된 시작 가이드
execute_matlab_code - MATLAB 코드를 실행하고 결과를 얻습니다.
MATLAB 명령이나 스크립트를 실행합니다.
나중에 참조할 수 있도록 스크립트를 저장하는 옵션
대화에서 직접 출력을 확인하세요
generate_matlab_code - 자연어에서 MATLAB 코드 생성
당신이 성취하고자 하는 바를 평범한 언어로 설명하세요
응답으로 실행 가능한 MATLAB 코드 가져오기
생성된 스크립트를 저장하는 옵션
종속성 설치:
지엑스피1
서버를 빌드하세요:
npm run build자동 재빌드를 사용한 개발의 경우:
npm run watch시스템에 MATLAB 설치
Node.js(v14 이상)
Smithery를 통해 Claude Desktop용 MATLAB MCP 서버를 자동으로 설치하려면:
npx -y @smithery/cli install @WilliamCloudQi/matlab-mcp-server --client claudenpm install -g matlab-mcp-server또는 저장소를 복제하여 직접 빌드하세요.
git clone https://github.com/username/matlab-mcp-server.git
cd matlab-mcp-server
npm install
npm run buildcline과 함께 사용하려면 서버 구성을 추가하세요.
MacOS의 경우: ~/Library/Application Support/Claude/claude_desktop_config.json Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"matlab-server": {
"command": "node",
"args": ["/path/to/matlab-server/build/index.js"],
"env": {
"MATLAB_PATH": "/path/to/matlab/executable"
},
"disabled": false,
"autoApprove": []
}
}
}/path/to/matlab/executable MATLAB 실행 파일의 경로로 바꾸세요.
Windows: 일반적으로 C:\\Program Files\\MATLAB\\R2023b\\bin\\matlab.exe
macOS: 일반적으로 /Applications/MATLAB_R2023b.app/bin/matlab
Linux: 일반적으로 /usr/local/MATLAB/R2023b/bin/matlab
MCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. 패키지 스크립트로 제공되는 MCP Inspector를 사용하는 것이 좋습니다.
npm run inspector검사기는 브라우저에서 디버깅 도구에 액세스할 수 있는 URL을 제공합니다.
Execute MATLAB code and return the results
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | MATLAB code to execute | |
| saveScript | No | Whether to save the MATLAB script for future reference | |
| scriptPath | No | Custom path to save the MATLAB script (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It states the basic action and result but does not disclose execution side effects, persistence between calls, result format, or how saveScript and scriptPath affect behavior. This is thin for an arbitrary code-execution tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one compact sentence with no filler and the primary action is front-loaded. It is concise, though arguably too terse given the missing behavioral context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one required parameter and fully documented optional parameters, the schema plus description are mostly usable. However, with no output schema and no annotations, the vague phrase 'return the results' leaves the return format undefined, and no execution-environment context is given.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so each parameter is already documented in the schema. The description adds no additional meaning for 'code', 'saveScript', or 'scriptPath', so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Execute'), identifies the target ('MATLAB code'), and notes the outcome ('return the results'). It is clear on its face but does not explicitly distinguish this tool from its sibling generate_matlab_code, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to choose execute_matlab_code over generate_matlab_code, and there are no exclusions or prerequisites. The intended use must be inferred from the tool name and basic semantics rather than from explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate MATLAB code from a natural language description
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | Natural language description of what the code should do | |
| saveScript | No | Whether to save the generated MATLAB script | |
| scriptPath | No | Custom path to save the MATLAB script (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says code will be generated; it does not explain whether code is returned, saved, or how the optional saveScript/scriptPath parameters affect behavior. It also does not state that this tool does not execute the generated MATLAB code.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It communicates the primary function efficiently, and every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description leaves important operational details unstated: how the generated code is returned to the agent, what the default saving behavior is, and how it relates to execute_matlab_code. These gaps could cause incorrect invocation or misunderstanding of the tool's response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers all three parameters with descriptions, so the baseline is 3. The description's 'from a natural language description' slightly reinforces the central 'description' parameter, but it adds no meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Generate') and resource ('MATLAB code'), making the core purpose clear. It does not explicitly contrast itself against the sibling execute_matlab_code, so the differentiation is only implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to choose this tool over execute_matlab_code, and no mention of prerequisites or intended workflow. The agent must infer that 'generate' means code creation rather than execution, but the description offers no explicit selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0execute_matlab_codegenerate_matlab_codeScored across 2 tools
The two tools have clearly distinct purposes: one executes existing MATLAB code, while the other generates new code from natural language. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool based on the task.
Both tool names follow a consistent verb_noun pattern (execute_matlab_code and generate_matlab_code), using the same verb style and snake_case formatting. This predictability aids in understanding and usage without any deviations.
With only two tools, the server feels thin for a MATLAB domain, which typically involves more operations like plotting, data analysis, or file management. While the tools cover core execution and generation, the scope is limited and may not support complex agent workflows effectively.
The tool set is severely incomplete for a MATLAB server, lacking essential operations such as loading/saving data, creating plots, debugging code, or managing variables. Agents will face significant gaps when trying to perform common MATLAB tasks beyond basic code execution and generation.
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