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godzeo
by godzeo

http_post

Send HTTP POST requests with headers, cookies, and body payloads. Log all requests for debugging, security testing, or API integration tasks using MCP HTTP Requests server.

Instructions

HTTP POST request with full support (headers, cookies, body, timeout) - All requests logged

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNo
cookiesNo
headersNo
timeoutNo
urlYes

Implementation Reference

  • The main handler function for the 'http_post' tool. It is decorated with @mcp.tool() which registers it with the MCP server. Performs HTTP POST request via helper function and returns JSON response.
    @mcp.tool()
    def http_post(
        url: str, 
        headers: Optional[Dict[str, str]] = None,
        cookies: Optional[Dict[str, str]] = None,
        body: Optional[str] = None,
        timeout: float = 30.0
    ) -> str:
        """HTTP POST request with full support (headers, cookies, body, timeout) - All requests logged"""
        try:
            result = make_http_request_with_logging("POST", url, headers or {}, cookies or {}, body or "", timeout)
            return json.dumps(result, indent=2)
        except Exception as e:
            return f"Error: {str(e)}"
  • Core helper function that executes the actual HTTP POST request using httpx.Client, handles errors, and logs everything via log_request_response.
    def make_http_request_with_logging(method: str, url: str, headers: dict, cookies: dict, body: str, timeout: float):
        """Universal HTTP request function with logging"""
        try:
            with httpx.Client(timeout=timeout) as client:
                response = client.request(
                    method=method.upper(),
                    url=url,
                    headers=headers,
                    cookies=cookies,
                    content=body.encode('utf-8') if body else None
                )
                
                # Log the request and response
                log_path = log_request_response(
                    method=method.upper(), 
                    url=url, 
                    headers=headers, 
                    cookies=cookies, 
                    body=body,
                    status_code=response.status_code,
                    response_headers=dict(response.headers),
                    response_content=response.text,
                    response_length=len(response.text)
                )
                
                return {
                    "method": method.upper(),
                    "url": url,
                    "status_code": response.status_code,
                    "response_headers": dict(response.headers),
                    "response_content": response.text,
                    "response_length": len(response.text),
                    "request_headers": headers,
                    "request_cookies": cookies,
                    "request_body": body,
                    "logged_to": log_path
                }
        except Exception as e:
            # Log the error
            log_request_response(
                method=method.upper(), url=url, headers=headers, cookies=cookies, body=body,
                status_code=0, response_headers={}, response_content="", response_length=0,
                error=str(e)
            )
            raise e
  • Supporting utility that logs the full request and response details to a timestamped file in ~/mcp_requests_logs/
    def log_request_response(method: str, url: str, headers: dict, cookies: dict, body: str, 
                            status_code: int, response_headers: dict, response_content: str, 
                            response_length: int, error: str = None):
        """Log complete request and response details"""
        log_data = {
            "timestamp": datetime.datetime.now().isoformat(),
            "request": {
                "method": method,
                "url": url,
                "headers": headers,
                "cookies": cookies,
                "body": body,
                "body_length": len(body) if body else 0
            },
            "response": {
                "status_code": status_code if not error else "ERROR",
                "headers": response_headers if not error else {},
                "content_length": response_length if not error else 0,
                "content_preview": response_content[:500] + "..." if response_content and len(response_content) > 500 else response_content
            },
            "error": error
        }
        
        logger.info(f"HTTP_REQUEST: {json.dumps(log_data, indent=2, ensure_ascii=False)}")
        return log_path
Behavior2/5

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

With no annotations, the description carries full burden but only mentions logging ('All requests logged'). It lacks critical behavioral details like authentication requirements, error handling, rate limits, or what 'logged' entails (e.g., where logs are stored). This is insufficient for a mutation tool with zero annotation coverage.

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 a single, efficient sentence with zero waste. It front-loads the core purpose and includes key features without redundancy, making it highly concise and well-structured.

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

Completeness2/5

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

For a mutation tool with 5 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It misses details on response format, error cases, security implications, and practical usage, leaving significant gaps in contextual understanding.

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?

Schema description coverage is 0%, so the description must compensate. It lists parameters (headers, cookies, body, timeout) but doesn't explain their semantics, formats, or interactions beyond naming them. This adds minimal value over the schema, meeting the baseline for low coverage without fully compensating.

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 action ('HTTP POST request') and scope ('with full support (headers, cookies, body, timeout)'), distinguishing it from siblings by specifying POST method. However, it doesn't explicitly contrast with other HTTP methods like GET or PUT, which would make it a 5.

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?

No guidance on when to use this tool versus alternatives like http_put or http_patch is provided. The description mentions 'full support' but doesn't specify scenarios where POST is appropriate over other HTTP methods, leaving usage context implied at best.

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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