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Grok Bot+18 agentsMCP Server Builder Skill
Build and review MCP servers with official SDKs, typed tool schemas, and strict security boundaries.
$23.99
MCP Server Builder Skill
Example session with this skill installed
Build a Python MCP server that lets an AI client read and write to a specific 'notes' directory. Use the official Python SDK. Need tool definitions and a security review.
- Read your context and instructions
- Compiled the mcp server builder
I have designed a Python-based MCP server using the official mcp SDK to manage local notes. The implementation includes directory sandboxing and typed schemas for file operations.
MCP Notes Server Implementation
import os
from mcp.server.fastmcp import FastMCP
# Initialize FastMCP server
mcp = FastMCP("NotesManager")
NOTES_DIR = os.path.abspath("notes")
@mcp.tool()
def read_note(filename: str) -> str:
"""Reads a note from the notes directory."""
safe_path = os.path.join(NOTES_DIR, os.path.basename(filename))
if not os.path.exists(safe_path):
return f"Error: {filename} not found."
with open(safe_path, "r") as f:
return f.read()
@mcp.tool()
def write_note(filename: str, content: str) -> str:
"""Writes or overwrites a note in the notes directory."""
if not os.path.exists(NOTES_DIR):
os.makedirs(NOTES_DIR)
safe_path = os.path.join(NOTES_DIR, os.path.basename(filename))
with open(safe_path, "w") as f:
f.write(content)
return f"Successfully wrote to {filename}"
if __name__ == "__main__":
mcp.run(transport="stdio")
| Component | Description | Security Logic |
|---|---|---|
| Tool: read_note | Retrieves text content from a file | Uses os.path.basename to prevent directory traversal |
| Tool: write_note | Saves text content to a file | Enforces storage within the defined NOTES_DIR |
| Transport | Standard Input/Output | Suitable for local AI client integration (e.g. Claude Desktop) |
Security and Permissions Review
- Path Traversal: The use of
basenameprevents access to files outside the target folder. - Authorization: This server operates with the permissions of the local user.
- Sensitive Data: No secrets are hardcoded; directory paths are relative to the execution root.
Next steps
- Create the
notesdirectory in your project root. - Run
mcp inspector main.pyto verify tool discovery. - Add the server path to your
claude_desktop_config.jsonfor integration testing.
Connects securely to your tools. The creator never sees your data.
About this skill
Building Model Context Protocol (MCP) servers requires strict adherence to evolving specs and security boundaries. This skill helps you design, implement, and review MCP servers that expose tools, resources, and prompts to AI clients like Claude Desktop or IDEs. It forces a systematic approach to capability design, ensuring tools have typed schemas and predictable error behavior.
What it does
- Design capabilities by defining tools, resources, and prompts with strict input/output schemas.
- Implement servers using official SDKs and project structures tailored to specific runtimes.
- Enforce security by applying least privilege to file paths, database queries, and API calls.
- Generate test plans for discovery, boundary cases, and authorization checks.
- Configure deployments with environment variable templates and transport-specific setup.
How it works
- Confirm SDK to align with the current MCP specification version and language-specific library.
- Define Schema for every tool and resource to ensure valid interaction with AI clients.
- Build Core logic including input validation, error handling, and sensitive data redaction.
- Verify Setup using a structured plan for the MCP Inspector and client-specific integration.
Frameworks & tools
This skill works with official MCP SDKs (TypeScript/Node.js, Python), the MCP Inspector, and AI clients supporting the protocol.
Why this beats prompting it yourself
Generic prompts often invent non-existent SDK decorators or hallucinate protocol methods. This skill follows the official specification structure, ensuring generated code actually registers tools correctly and handles lifecycle events according to the current standard.
Use cases
- Build a TypeScript MCP server to expose internal company APIs as AI tools.
- Create a Python-based resource provider for local documentation files.
- Design prompts for reusable agent interaction templates within a server.
- Review existing server implementations for security flaws and schema errors.
Known limitations
Does not replace running the official MCP Inspector or client-specific integration tests. Implementation accuracy depends on providing the specific language and SDK version.
Developer must manually run MCP Inspector and automated tests. Requires specific language/SDK version for accurate code generation. Scope limited to protocol-compliant capabilities.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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