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fovi-llc

Radicle + GitHub MCP Server

by fovi-llc

rad_id

Retrieve the current node's Radicle ID for peer-to-peer code collaboration and repository management across Radicle and GitHub platforms.

Instructions

Get the current node's Radicle ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function for the 'rad_id' tool. It is decorated with @mcp.tool() for registration and executes the 'rad self' command using the shared run_rad_command helper to retrieve and return the current node's Radicle ID.
    @mcp.tool()
    async def rad_id() -> str:
        """
        Get the current node's Radicle ID.
        """
        result = await run_rad_command(["rad", "self"])
        
        if result["success"]:
            return f"🆔 Your Radicle ID:\n{result['stdout']}"
        else:
            return f"❌ Failed to get Radicle ID: {result['stderr']}"
  • Shared utility function used by the rad_id tool (and others) to execute Radicle 'rad' commands asynchronously, capturing stdout, stderr, return code, and handling errors.
    async def run_rad_command(command: List[str], cwd: Optional[str] = None) -> Dict[str, Any]:
        """
        Run a rad command and return the result.
        
        Args:
            command: List of command arguments starting with 'rad'
            cwd: Working directory to run the command in
            
        Returns:
            Dictionary with stdout, stderr, and return_code
        """
        try:
            # Ensure command starts with 'rad'
            if not command or command[0] != "rad":
                command = ["rad"] + command
                
            logger.info(f"Running command: {' '.join(command)}")
            
            process = await asyncio.create_subprocess_exec(
                *command,
                stdout=asyncio.subprocess.PIPE,
                stderr=asyncio.subprocess.PIPE,
                cwd=cwd
            )
            
            stdout, stderr = await process.communicate()
            
            return {
                "stdout": stdout.decode("utf-8").strip(),
                "stderr": stderr.decode("utf-8").strip(),
                "return_code": process.returncode,
                "success": process.returncode == 0
            }
            
        except FileNotFoundError:
            return {
                "stdout": "",
                "stderr": "rad command not found. Please ensure Radicle is installed.",
                "return_code": 127,
                "success": False
            }
        except Exception as e:
            return {
                "stdout": "",
                "stderr": f"Error running command: {str(e)}",
                "return_code": 1,
                "success": False
            }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves the current node's Radicle ID, implying a read-only operation, but doesn't specify if this requires network access, authentication, or has any side effects like caching. This leaves gaps in understanding the tool's behavior beyond its basic function.

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, clear sentence that directly states the tool's function without any wasted words. It is front-loaded and efficiently communicates the essential information, 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.

Completeness3/5

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

Given the tool's simplicity (0 parameters, no annotations, but with an output schema), the description is adequate but minimal. It explains what the tool does but lacks context on usage, behavioral traits, or how it fits with siblings. The output schema likely covers return values, so the description doesn't need to detail those, but more guidance would improve completeness.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose. This meets the baseline for tools with no parameters, as it avoids unnecessary detail.

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 tool's purpose with a specific verb ('Get') and resource ('current node's Radicle ID'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'rad_status' or 'rad_remote_list', which might also provide ID-related information, so it doesn't reach the highest score.

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?

The description provides no guidance on when to use this tool versus alternatives. For example, it doesn't clarify if this is for retrieving a local ID versus a remote one, or if other tools like 'rad_status' might provide similar information in a broader context. This lack of context leaves usage unclear.

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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