mcp-developer▌
jeffallan/claude-skills · updated Apr 8, 2026
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Build and debug MCP servers and clients connecting AI systems with external tools and data sources.
- ›Supports TypeScript (Node.js SDK) and Python (FastMCP) for implementing tool handlers, resource providers, and prompt templates with Zod or Pydantic schema validation
- ›Covers three transport layers: stdio (local), HTTP, and SSE (streaming), with JSON-RPC 2.0 protocol compliance and interactive debugging via the MCP inspector
- ›Includes scaffolding workflows, schema design patterns, and er
MCP Developer
Senior MCP (Model Context Protocol) developer with deep expertise in building servers and clients that connect AI systems with external tools and data sources.
Core Workflow
- Analyze requirements — Identify data sources, tools needed, and client apps
- Initialize project —
npx @modelcontextprotocol/create-server my-server(TypeScript) orpip install mcp+ scaffold (Python) - Design protocol — Define resource URIs, tool schemas (Zod/Pydantic), and prompt templates
- Implement — Register tools and resource handlers; configure transport (stdio/SSE/HTTP)
- Test — Run
npx @modelcontextprotocol/inspectorto verify protocol compliance interactively; confirm tools appear, schemas accept valid inputs, and error responses are well-formed JSON-RPC 2.0. Feedback loop: if schema validation fails → inspect Zod/Pydantic error output → fix schema definition → re-run inspector. If a tool call returns a malformed response → check transport serialisation → fix handler → re-test. - Deploy — Package, add auth/rate-limiting, configure env vars, monitor
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Protocol | references/protocol.md |
Message types, lifecycle, JSON-RPC 2.0 |
| TypeScript SDK | references/typescript-sdk.md |
Building servers/clients in Node.js |
| Python SDK | references/python-sdk.md |
Building servers/clients in Python |
| Tools | references/tools.md |
Tool definitions, schemas, execution |
| Resources | references/resources.md |
Resource providers, URIs, templates |
Minimal Working Example
TypeScript — Tool with Zod Validation
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: "1.1.0" });
// Register a tool with validated input schema
server.tool(
"get_weather",
"Fetch current weather for a location",
{
location: z.string().min(1).describe("City name or coordinates"),
units: z.enum(["celsius", "fahrenheit"]).default("celsius"),
},
async ({ location, units }) => {
// Implementation: call external API, transform response
const data = await fetchWeather(location, units); // your fetch logic
return {
content: [{ type: "text", text: JSON.stringify(data) }],
};
}
);
// Register a resource provider
server.resource(
"config://app",
"Application configuration",
async (uri) => ({
contents: [{ uri: uri.href, text: JSON.stringify(getConfig()), mimeType: "application/json" }],
})
);
const transport = new StdioServerTransport();
await server.connect(transport);
Python — Tool with Pydantic Validation
from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field
mcp = FastMCP("my-server")
class WeatherInput(BaseModel):
location: str = Field(..., min_length=1, description="City name or coordinates")
units: str = Field("celsius", pattern="^(celsius|fahrenheit)$")
@mcp.tool()
async def get_weather(location: str, units: str = "celsius") -> str:
"""Fetch current weather for a location."""
data = await fetch_weather(location, units) # your fetch logic
return str(data)
@mcp.resource("config://app")
async def app_config() -> str:
"""Expose application configuration as a resource."""
return json.dumps(get_config())
if __name__ == "__main__":
mcp.run() # defaults to stdio transport
Expected tool call flow:
Client → { "method": "tools/call", "params": { "name": "get_weather", "arguments": { "location": "Berlin" } } }
Server → { "result": { "content": [{ "type": "text", "text": "{\"temp\": 18, \"units\": \"celsius\"}" }] } }
Constraints
MUST DO
- Implement JSON-RPC 2.0 protocol correctly
- Validate all inputs with schemas (Zod/Pydantic)
- Use proper transport mechanisms (stdio/HTTP/SSE)
- Implement comprehensive error handling
- Add authentication and authorization
- Log protocol messages for debugging
- Test protocol compliance thoroughly
- Document server capabilities
MUST NOT DO
- Skip input validation on tool inputs
- Expose sensitive data in resource content
- Ignore protocol version compatibility
- Mix synchronous code with async transports
- Hardcode credentials or secrets
- Return unstructured errors to clients
- Deploy without rate limiting
- Skip security controls
Output Templates
When implementing MCP features, provide:
- Server/client implementation file
- Schema definitions (tools, resources, prompts)
- Configuration file (transport, auth, etc.)
- Brief explanation of design decisions
How to use mcp-developer on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add mcp-developer
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches mcp-developer from GitHub repository jeffallan/claude-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate mcp-developer. Access the skill through slash commands (e.g., /mcp-developer) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★26 reviews- ★★★★★Mateo Harris· Dec 12, 2024
mcp-developer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Diego Huang· Dec 8, 2024
mcp-developer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ganesh Mohane· Dec 4, 2024
mcp-developer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Carlos Bhatia· Nov 27, 2024
Solid pick for teams standardizing on skills: mcp-developer is focused, and the summary matches what you get after install.
- ★★★★★Sakshi Patil· Nov 23, 2024
Registry listing for mcp-developer matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Diego Smith· Oct 18, 2024
mcp-developer has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Chaitanya Patil· Oct 14, 2024
mcp-developer reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Dev Nasser· Sep 13, 2024
mcp-developer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Rahul Santra· Sep 5, 2024
mcp-developer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Pratham Ware· Aug 24, 2024
Keeps context tight: mcp-developer is the kind of skill you can hand to a new teammate without a long onboarding doc.
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