MCP: Model Context Protocol Server Development

Build, review, and debug MCP servers that expose tools, resources, and prompts to AI coding assistants. The goal is secure, well-structured servers that follow the protocol spec and don't become yet another server with preventable injection vulnerabilities.

Target versions (May 2026):

MCP specification: 2025-11-25 (current stable; 2026-03-15 in draft)
TypeScript SDK: @modelcontextprotocol/sdk 1.29.0 (1.x stable; 2.0.0-alpha in dev)
Python SDK: mcp 1.27.0 (v1.26.0+)
Protocol transports: stdio, streamable HTTP (SSE deprecated in spec 2025-03-26)

When to use

Building a new MCP server (tools, resources, prompts)
Adding tool handlers to an existing MCP server
Configuring MCP transport (stdio for local, streamable HTTP for remote)
Implementing MCP authentication (OAuth 2.1)
Implementing MCP elicitation (interactive dialogs)
Reviewing MCP server code for injection or tool poisoning vulnerabilities
Debugging MCP connection issues between client and server
Migrating from a custom tool integration to MCP

When NOT to use

General REST API development that doesn't use MCP - just write the API
Claude API / Anthropic SDK usage in an application - use ai-ml
Security auditing existing servers across a codebase - use security-audit (it has an MCP section)
Using MCP browsing tools to browse or scrape web pages - use browse
Writing prompts for LLMs (not MCP prompt resources) - use prompt-generator

AI Self-Check

When generating or reviewing MCP server code, verify each item before presenting the result:

Current source checked: dated versions, CLI flags, API names, and support windows are verified against primary docs before repeating them
Hidden state identified: local config, credentials, caches, contexts, branches, cluster targets, or previous runs are made explicit before acting
Verification is real: final checks exercise the actual runtime, parser, service, or integration point instead of only linting prose or happy paths
Spec version checked: transports, auth, resources, tools, and prompts match current MCP docs and SDK behavior
Tool poisoning considered: tool descriptions, dynamic metadata, and server updates cannot silently expand authority

Performance

Keep tool schemas tight and responses small; large unstructured tool outputs waste model context.
Use resources for reusable context instead of returning the same large payload from every tool call.
Batch read-only lookups where latency matters, but keep side-effecting tools separate and auditable.

Best Practices

Treat MCP servers as security boundaries: authenticate, authorize, and log side effects explicitly.
Make tool names and schemas stable; version breaking changes instead of changing semantics in place.
Require user confirmation for tools that spend money, mutate infrastructure, delete data, or expose secrets.

Workflow

Build vs. Review: Steps 1-6 are for building new servers. When reviewing existing MCP server code: (1) scope using Step 1 questions - what tools, transport, and auth does the server use; (2) audit each tool handler against Step 3 injection vectors and the AI Self-Check; (3) cross-reference the Common Mistakes section for patterns AI models frequently introduce.

Step 1: Determine the server's purpose

Before writing code, clarify:

What tools will it expose? Each tool = one operation the AI can invoke.
What resources will it serve? Resources = read-only data the AI can access.
What transport? stdio for local CLI integration, streamable HTTP for remote/production.
What authentication? None for stdio. OAuth 2.1 recommended for remote servers handling user data.
What language? TypeScript (most mature SDK) or Python (simpler, FastMCP).

Step 2: Scaffold the server

TypeScript (recommended for production):

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({ name: "my-server", version: "1.0.0" });

server.tool(
  "search_docs",
  "Search documentation by keyword",
  { query: z.string().max(200).describe("Search query"), limit: z.number().int().min(1).max(100).default(10) },
  async ({ query, limit }) => {
    // If this tool reads files, apply path validation from Step 3 before any fs access.
    const sanitized = query.replace(/[^\w\s-]/g, "");
    const results = await searchIndex(sanitized, limit);
    return { content: [{ type: "text", text: JSON.stringify(results) }] };
  }
);

server.resource("config", "config://app/settings", async (uri) => ({
  contents: [{ uri: uri.href, mimeType: "application/json", text: JSON.stringify(config) }],
}));

const transport = new StdioServerTransport();
await server.connect(transport);

Python (FastMCP for quick prototyping):

import json, re
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("my-server")

@mcp.tool()
def search_docs(query: str, limit: int = 10) -> str:
    """Search documentation by keyword."""
    sanitized = re.sub(r"[^\w\s-]", "", query[:200])
    return str(search_index(sanitized, min(limit, 100)))

@mcp.resource("config://app/settings")
def get_config() -> str:
    """Application configuration."""
    return json.dumps(config)

if __name__ == "__main__":
    mcp.run()

Step 3: Implement tools securely

Injection is the top MCP vulnerability class. Every tool handler is an attack surface.

The #1 rule: never interpolate user input into commands, queries, or paths.

Common injection vectors in MCP tools:

Vector	Bad pattern	Safe pattern
Shell	Interpolated command strings	`execFile` with argument arrays + path validation
SQL	String concatenation in queries	Parameterized queries with `$1` placeholders
File paths	Direct `readFile(userPath)`	Resolve path, validate prefix against allowlist
URLs	Direct `fetch(userUrl)`	Parse URL, validate scheme + host against allowlist
Templates	Dynamic code evaluation	Sandboxed template engine with no code execution

Path traversal prevention:

import path from "node:path";

function safePath(base: string, userInput: string): string {
  const resolved = path.resolve(base, userInput);
  if (!resolved.startsWith(path.resolve(base) + path.sep)) {
    throw new Error("Path traversal detected");
  }
  return resolved;
}

Before/after - applying safePath() to a vulnerable tool handler:

// BEFORE (vulnerable - user controls path directly)
server.tool("read_file", "Read a project file",
  { path: z.string() },
  async ({ path: filePath }) => {
    const data = await readFile(filePath, "utf-8"); // path traversal
    return { content: [{ type: "text", text: data }] };
  }
);

// AFTER (safe - resolved path validated against allowed base)
server.tool("read_file", "Read a project file",
  { path: z.string().max(500) },
  async ({ path: filePath }) => {
    const safe = safePath("/srv/project", filePath);
    const data = await readFile(safe, "utf-8");
    return { content: [{ type: "text", text: data }] };
  }
);

SSRF prevention (when tools fetch URLs from user input):

Block private IP ranges: 10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16
Block link-local: 169.254.0.0/16 (includes cloud metadata at 169.254.169.254)
Block loopback: 127.0.0.0/8
Require HTTPS in production
Pin DNS resolution between check and use (TOCTOU defense)

Step 4: Configure transport

Transport	Use case	Auth needed	Notes
stdio	Local tools, CLI integration	No	Runs as user's process. Most secure.
Streamable HTTP	Remote/multi-client servers	Recommended	Single endpoint, POST for messages, optional SSE streaming.

SSE transport was deprecated in spec 2025-03-26. Use streamable HTTP for all remote servers. Auth is optional per spec but strongly recommended for servers handling user data. When implementing auth, use OAuth 2.1 with PKCE. Prefer Client ID Metadata Documents over Dynamic Client Registration (DCR is a fallback, not a requirement).

Streamable HTTP security:

Bind to 127.0.0.1 for local servers (never 0.0.0.0)
Validate Origin header on all requests (DNS rebinding prevention)
If using stateful sessions: MCP-Session-Id must be cryptographically random (UUID v4+)
Client sends MCP-Protocol-Version header (e.g., 2025-11-25)
Consider using createMcpExpressApp() / createMcpHonoApp() from the TS SDK for built-in DNS rebinding protection

Step 5: Handle elicitation safely

MCP elicitation lets servers request structured input from users mid-task.

Schema restrictions - elicitation schemas are limited to flat objects with primitive fields:

string (with optional format: email, uri, date, date-time)
number / integer (with minimum, maximum)
boolean
enum (string with enum; use anyOf with title for labeled choices)
array of enum strings (for multi-select)

No nested objects. Keep schemas simple for broad client support.

Security: never request credentials via elicitation. Clients should show which server is requesting input and allow decline/cancel at any time. Handle all three responses: accept (with data), decline, and cancel.

Step 6: Test the server

# Test with MCP Inspector (official debugging tool)
npx @modelcontextprotocol/inspector your-server-command

# Python alternative
uv run mcp dev server.py

Test each tool handler with: valid inputs (happy path), missing required fields, malicious inputs (injection, path traversal, oversized payloads), concurrent requests.

Read references/security.md for specific injection test payloads.

Tool Poisoning and Rug Pull Defense

These attacks target tool metadata, not tool execution.

Tool poisoning: malicious instructions hidden in tool description fields manipulate the AI model into exfiltrating data or calling unintended tools. Descriptions are visible to the model but often hidden from users in the UI.

Rug pull attacks: server changes tool definitions after initial approval - clean version during onboarding, malicious version later.

Server-side defenses:

Write clear, honest tool descriptions - no hidden instructions
Do not include executable logic or injection payloads in descriptions
Keep descriptions minimal and factual
Treat annotations as advisory (untrusted on the client side)

Client-side defenses (document for consumers of your server):

Display tool descriptions to users before granting access
Hash tool schemas at approval time; alert on changes between sessions
Limit cross-server tool access

Common Mistakes

AI models consistently make these errors when generating MCP server code:

Shell commands via string interpolation - the #1 vulnerability. Always use argument arrays for system commands.
Missing server-side validation - generating inputSchema but never validating against it in the handler. The client may skip validation.
Bare "type": "string" in schemas - no maxLength, no pattern, no constraints. Accepts any string of any length.
Binding HTTP to 0.0.0.0 - exposes local servers to the network. Use 127.0.0.1.
No Origin header validation - enables DNS rebinding against local servers.
Leaking error details - stack traces, file paths, or DB errors in tool responses.
Token passthrough - accepting OAuth tokens meant for other services without audience validation.
Hallucinating SDK methods - inventing API calls that don't exist. Verify every method against the actual SDK docs.
Ignoring elicitation actions - handling accept but crashing on decline/cancel.
No graceful shutdown - missing SIGINT/SIGTERM handlers on stdio servers.

Reference Files

references/security.md - OAuth 2.1 details, known CVEs, injection test payloads, SSRF prevention, session management, and tool poisoning defense

Output Contract

See skills/_shared/output-contract.md for the full contract.

Skill name: MCP
Deliverable bucket: audits
Mode: conditional. When invoked to analyze, review, audit, or improve existing repo content, emit the full contract -- boxed inline header, body summary inline plus per-finding detail in the deliverable file, boxed conclusion, conclusion table -- and write the deliverable to docs/local/audits/mcp/<YYYY-MM-DD>-<slug>.md. When invoked to answer a question, teach a concept, build a new artifact, or generate content, respond freely without the contract.
Severity scale: P0 | P1 | P2 | P3 | info (see shared contract; only used in audit/review mode).

Related Skills

security-audit - for auditing MCP servers as part of a broader security review. The security-audit skill's ASI and MCP sections cover vulnerability patterns; this skill covers building servers correctly from the start.
code-review - for reviewing MCP server code for correctness beyond security.
docker - for containerizing MCP servers with minimal capabilities.

Rules

Validate all tool inputs server-side. Never trust the client or model. Use schema validation (Zod, Pydantic) with explicit types, ranges, and constraints.
No shell execution with string interpolation. Use argument arrays for system commands.
Keep descriptions concise. Some clients truncate long descriptions. A few sentences covering what the tool does and its parameters - not implementation details.
Authenticate when handling user data. Use OAuth 2.1 with PKCE for remote servers that access user data. Auth is optional per spec but strongly recommended.
Return structured errors. MCP error codes + human-readable messages. No stack traces.
Test with malicious inputs. Injection payloads, path traversal, oversized inputs.
Bind local servers to 127.0.0.1. Never 0.0.0.0 for local-only servers.
Validate Origin headers on all streamable HTTP requests.
Handle shutdown gracefully. Register signal handlers. Clean up resources.
Run the AI Self-Check. Every generated MCP server gets verified against the checklist.

mcp