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consult_codex_with_stdin

Process piped content like files or logs with AI prompts for CI/CD workflows. Combines stdin input with custom prompts to generate text, JSON, or code outputs.

Instructions

Consult Codex with stdin content piped to prompt - pipeline-friendly execution.

Similar to 'echo "content" | codex exec "prompt"' - combines stdin with prompt.
Perfect for CI/CD workflows where you pipe file contents to the AI.

Args:
    stdin_content: Content to pipe as stdin (e.g., file contents, diff, logs)
    prompt: The prompt to process the stdin content
    directory: Working directory (required)
    format: Output format - "text", "json", or "code" (default: "json")
    timeout: Optional timeout in seconds (overrides env var, recommended: 60-120)
    
Returns:
    Formatted response based on format parameter

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stdin_contentYes
promptYes
directoryYes
formatNojson
timeoutNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The primary handler function for the 'consult_codex_with_stdin' MCP tool. Decorated with @mcp.tool() for automatic registration. Executes codex CLI by piping combined stdin_content and prompt, handles platform-specific subprocess execution (Windows UTF-8), formats output as JSON/text/code, and includes comprehensive error handling with timeouts.
    @mcp.tool()
    def consult_codex_with_stdin(
        stdin_content: str,
        prompt: str,
        directory: str,
        format: str = "json",
        timeout: Optional[int] = None
    ) -> str:
        """
        Consult Codex with stdin content piped to prompt - pipeline-friendly execution.
        
        Similar to 'echo "content" | codex exec "prompt"' - combines stdin with prompt.
        Perfect for CI/CD workflows where you pipe file contents to the AI.
        
        Args:
            stdin_content: Content to pipe as stdin (e.g., file contents, diff, logs)
            prompt: The prompt to process the stdin content
            directory: Working directory (required)
            format: Output format - "text", "json", or "code" (default: "json")
            timeout: Optional timeout in seconds (overrides env var, recommended: 60-120)
            
        Returns:
            Formatted response based on format parameter
        """
        # Check if codex CLI is available
        if not _get_codex_command():
            error_response = "Error: Codex CLI not found. Install from OpenAI"
            if format == "json":
                return json.dumps({"status": "error", "error": error_response}, indent=2)
            return error_response
        
        # Validate directory
        if not os.path.isdir(directory):
            error_response = f"Error: Directory does not exist: {directory}"
            if format == "json":
                return json.dumps({"status": "error", "error": error_response}, indent=2)
            return error_response
        
        # Validate format
        if format not in ["text", "json", "code"]:
            error_response = f"Error: Invalid format '{format}'. Must be 'text', 'json', or 'code'"
            # Always return JSON for invalid format errors for consistency
            return json.dumps({"status": "error", "error": error_response}, indent=2)
        
        # Combine stdin content with prompt
        combined_input = f"{stdin_content}\n\n{prompt}"
    
        # Prepare query based on format
        if format == "json":
            processed_query = _format_prompt_for_json(combined_input)
        else:
            processed_query = combined_input
    
        # Setup command and timeout
        cmd = _build_codex_exec_command()
        if _should_skip_git_check():
            cmd.append("--skip-git-repo-check")
        timeout_value = timeout or _get_timeout()
        
        # Execute with timing
        start_time = time.time()
        try:
            result = _run_codex_command(cmd, directory, timeout_value, processed_query)
            execution_time = time.time() - start_time
            
            if result.returncode == 0:
                cleaned_output = _clean_codex_output(result.stdout)
                raw_response = cleaned_output if cleaned_output else "No output from Codex CLI"
                return _format_response(raw_response, format, execution_time, directory)
            else:
                error_response = f"Codex CLI Error: {result.stderr.strip()}"
                if format == "json":
                    return json.dumps({
                        "status": "error",
                        "error": error_response,
                        "metadata": {
                            "execution_time": execution_time,
                            "directory": directory,
                            "format": format
                        }
                    }, indent=2)
                return error_response
                
        except subprocess.TimeoutExpired:
            error_response = f"Error: Codex CLI command timed out after {timeout_value} seconds"
            if format == "json":
                return json.dumps({
                    "status": "error",
                    "error": error_response,
                    "metadata": {
                        "timeout": timeout_value,
                        "directory": directory,
                        "format": format
                    }
                }, indent=2)
            return error_response
        except FileNotFoundError as e:
            # More specific error for when codex command is not found
            codex_path = _get_codex_command()
            if _is_windows():
                error_response = (
                    f"Error: Codex CLI not found or not executable. "
                    f"Detected path: {codex_path or 'None'}. "
                    f"Please ensure 'codex' is installed and in your PATH. "
                    f"Try running 'codex --version' in Command Prompt to verify."
                )
            else:
                error_response = f"Error: Codex CLI not found: {str(e)}"
            if format == "json":
                return json.dumps({
                    "status": "error",
                    "error": error_response,
                    "metadata": {
                        "directory": directory,
                        "format": format,
                        "platform": platform.system()
                    }
                }, indent=2)
            return error_response
        except Exception as e:
            error_response = f"Error executing Codex CLI: {str(e)}"
            if format == "json":
                return json.dumps({
                    "status": "error",
                    "error": error_response,
                    "metadata": {
                        "directory": directory,
                        "format": format,
                        "platform": platform.system(),
                        "exception_type": type(e).__name__
                    }
                }, indent=2)
            return error_response
  • Key helper function used by the tool to execute the codex subprocess command, with special handling for Windows (UTF-8 encoding, PowerShell support) and Unix, timeout, and input piping.
    def _run_codex_command(cmd: List[str], directory: str, timeout_value: int, input_text: str) -> subprocess.CompletedProcess:
        """Execute codex command with platform-specific handling.
    
        Args:
            cmd: Command list to execute
            directory: Working directory
            timeout_value: Timeout in seconds
            input_text: Input text to pipe to the command
    
        Returns:
            CompletedProcess result with stdout/stderr as strings
        """
        # Windows-specific handling with UTF-8 encoding support
        if _is_windows():
            # On Windows, we need to:
            # 1. Use encoding='utf-8' instead of text=True to avoid code page issues
            # 2. Set PYTHONUTF8=1 and PYTHONIOENCODING=utf-8 for consistent encoding
            # 3. Don't use start_new_session as it's not supported on Windows
            env = os.environ.copy()
            env['PYTHONUTF8'] = '1'
            env['PYTHONIOENCODING'] = 'utf-8'
    
            # Encode input as UTF-8 bytes
            input_bytes = input_text.encode('utf-8') if input_text else None
    
            result = subprocess.run(
                cmd,
                cwd=directory,
                capture_output=True,
                timeout=timeout_value,
                input=input_bytes,
                shell=False,
                env=env
            )
    
            # Decode output as UTF-8 with error handling
            return subprocess.CompletedProcess(
                args=result.args,
                returncode=result.returncode,
                stdout=result.stdout.decode('utf-8', errors='replace') if result.stdout else '',
                stderr=result.stderr.decode('utf-8', errors='replace') if result.stderr else ''
            )
        else:
            # Unix/macOS handling (original behavior)
            return subprocess.run(
                cmd,
                cwd=directory,
                capture_output=True,
                text=True,
                timeout=timeout_value,
                input=input_text,
                start_new_session=True
            )
  • Helper for formatting the tool's output in text, JSON (with extraction), or code modes.
    def _format_response(raw_response: str, format_type: str, execution_time: float, directory: str) -> str:
        """Format response according to specified output format."""
        if format_type == "text":
            return raw_response
        
        elif format_type == "json":
            # Try to extract JSON from response first
            extracted_json = _extract_json_from_response(raw_response)
            
            if extracted_json:
                # Wrap extracted JSON in standard structure
                return json.dumps({
                    "status": "success",
                    "response": extracted_json,
                    "metadata": {
                        "execution_time": execution_time,
                        "directory": directory,
                        "format": "json"
                    }
                }, indent=2)
            else:
                # Fallback: wrap raw response
                return json.dumps({
                    "status": "success",
                    "response": raw_response,
                    "metadata": {
                        "execution_time": execution_time,
                        "directory": directory,
                        "format": "json"
                    }
                }, indent=2)
        
        elif format_type == "code":
            # Extract code blocks
            code_blocks = re.findall(r'```(\w+)?\n(.*?)\n```', raw_response, re.DOTALL)
            
            return json.dumps({
                "status": "success",
                "response": raw_response,
                "code_blocks": [{"language": lang or "text", "code": code.strip()} for lang, code in code_blocks],
                "metadata": {
                    "execution_time": execution_time,
                    "directory": directory,
                    "format": "code"
                }
            }, indent=2)
        
        else:
            return raw_response
  • Helper to parse JSON from Codex CLI output which may contain extra text.
    def _extract_json_from_response(response: str) -> Optional[Dict]:
        """Extract JSON from mixed text response using regex."""
        # Clean the response to remove CLI noise
        lines = response.split('\n')
        clean_lines = []
        json_started = False
        
        for line in lines:
            # Skip CLI headers and metadata
            if (line.startswith('[') and ']' in line and ('OpenAI' in line or 'workdir:' in line or 'model:' in line)):
                continue
            if line.startswith('--------'):
                continue
            if 'tokens used:' in line:
                continue
            if 'thinking' in line and line.startswith('['):
                continue
            if 'codex' in line and line.startswith('['):
                continue
                
            # Look for JSON content
            if '{' in line:
                json_started = True
            if json_started:
                clean_lines.append(line)
        
        clean_response = '\n'.join(clean_lines)
        
        # Try to find complete JSON objects
        json_pattern = r'\{(?:[^{}]|{[^{}]*})*\}'
        matches = re.findall(json_pattern, clean_response, re.DOTALL)
        
        for match in matches:
            try:
                parsed = json.loads(match.strip())
                # Validate it has expected structure
                if isinstance(parsed, dict) and any(key in parsed for key in ['result', 'response', 'answer']):
                    return parsed
            except json.JSONDecodeError:
                continue
        
        return None
  • The @mcp.tool() decorator registers the function as an MCP tool named 'consult_codex_with_stdin' (function name). No separate registration needed.
    @mcp.tool()
Behavior4/5

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

With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: it's a pipeline-friendly execution tool for AI processing, suitable for CI/CD, and returns formatted responses. It mentions timeout recommendations ('recommended: 60-120') and default format ('default: "json"'), adding practical context. However, it doesn't cover potential errors, rate limits, or authentication needs.

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

Conciseness5/5

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

The description is well-structured and front-loaded: it starts with the core purpose, provides analogy and use case, then lists parameters with clear explanations. Every sentence earns its place, with no redundant information, making it efficient and easy to parse.

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

Completeness5/5

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

Given the tool's complexity (5 parameters, pipeline execution), no annotations, and an output schema present, the description is complete enough. It covers purpose, usage, parameters, and behavioral context like timeout recommendations. The output schema handles return values, so the description doesn't need to explain them further.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It provides detailed semantics for all 5 parameters: explains 'stdin_content' as 'Content to pipe as stdin (e.g., file contents, diff, logs)', 'prompt' as 'The prompt to process the stdin content', 'directory' as 'Working directory (required)', 'format' with options and default, and 'timeout' with usage notes. This adds significant value beyond the bare schema.

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

Purpose5/5

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

The description clearly states the tool's purpose: 'Consult Codex with stdin content piped to prompt - pipeline-friendly execution.' It specifies the verb ('consult'), resource ('Codex'), and mechanism ('stdin content piped to prompt'), distinguishing it from siblings like 'consult_codex' and 'consult_codex_batch' by emphasizing stdin piping and CI/CD workflows.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool: 'Perfect for CI/CD workflows where you pipe file contents to the AI.' It distinguishes from siblings by noting it's 'Similar to 'echo "content" | codex exec "prompt"' - combines stdin with prompt,' implying alternatives might not handle stdin in this way.

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

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