Source profileQuality 83/100

Jeffallan/claude-skills/skills/mcp-developer/SKILL.md

mcp-developer

Use when building, debugging, or extending MCP servers or clients that connect AI systems with external tools and data sources. Invoke to implement tool handlers, configure resource providers, set up stdio/HTTP/SSE transport layers, validate schemas with Zod or Pydantic, debug protocol compliance issues, or scaffold complete MCP server/client projects using TypeScript or Python SDKs.

Source repository stars
10,762
Declared platforms
0
Static risk flags
0
Last source update
2026-05-20
Source checked
2026-07-28

Decision brief

What it does—and where it fits

Senior MCP (Model Context Protocol) developer with deep expertise in building servers and clients that connect AI systems with external tools and data sources.

Best for

  • Use when building, debugging, or extending MCP servers or clients that connect AI systems with external tools and data sources.

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/Jeffallan/claude-skills --skill "skills/mcp-developer"
Safe inspection promptEditorial

Inspect the Agent Skill "mcp-developer" from https://github.com/Jeffallan/claude-skills/blob/e8be415bc94d8d6ebddc2fb50e5d03c6e27d4319/skills/mcp-developer/SKILL.md at commit e8be415bc94d8d6ebddc2fb50e5d03c6e27d4319. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Core Workflow

    1. Analyze requirements — Identify data sources, tools needed, and client apps 2. Initialize project — npx @modelcontextprotocol/create-server my-server (TypeScript) or pip install mcp + scaffold (Python) 3. Design protocol — Define resource URIs, tool schemas (Zod/Pydantic), an…

    Analyze requirements — Identify data sources, tools needed, and client appsInitialize project — npx @modelcontextprotocol/create-server my-server (TypeScript) or pip install mcp + scaffold (Python)Design protocol — Define resource URIs, tool schemas (Zod/Pydantic), and prompt templates
  2. 02

    Reference Guide

    Load detailed guidance based on context:

    Load detailed guidance based on context:
  3. 03

    Minimal Working Example

    Expected tool call flow:

    Expected tool call flow:
  4. 04

    TypeScript — Tool with Zod Validation

    Review the “TypeScript — Tool with Zod Validation” section in the pinned source before continuing.

    Review and apply the “TypeScript — Tool with Zod Validation” source section.
  5. 05

    Python — Tool with Pydantic Validation

    Expected tool call flow:

    Expected tool call flow:

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score83/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars10,762SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
Jeffallan/claude-skills
Skill path
skills/mcp-developer/SKILL.md
Commit
e8be415bc94d8d6ebddc2fb50e5d03c6e27d4319
License
MIT
Collected
2026-07-28
Default branch
main
View the original SKILL.md

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

  1. Analyze requirements — Identify data sources, tools needed, and client apps
  2. Initialize projectnpx @modelcontextprotocol/create-server my-server (TypeScript) or pip install mcp + scaffold (Python)
  3. Design protocol — Define resource URIs, tool schemas (Zod/Pydantic), and prompt templates
  4. Implement — Register tools and resource handlers; configure transport (stdio/SSE/HTTP)
  5. Test — Run npx @modelcontextprotocol/inspector to 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.
  6. Deploy — Package, add auth/rate-limiting, configure env vars, monitor

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
Protocolreferences/protocol.mdMessage types, lifecycle, JSON-RPC 2.0
TypeScript SDKreferences/typescript-sdk.mdBuilding servers/clients in Node.js
Python SDKreferences/python-sdk.mdBuilding servers/clients in Python
Toolsreferences/tools.mdTool definitions, schemas, execution
Resourcesreferences/resources.mdResource 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:

  1. Server/client implementation file
  2. Schema definitions (tools, resources, prompts)
  3. Configuration file (transport, auth, etc.)
  4. Brief explanation of design decisions

Documentation

Alternatives

Compare before choosing

Computed 9831,966

K-Dense-AI/scientific-agent-skills

dask

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

Computed 9831,966

K-Dense-AI/scientific-agent-skills

medchem

Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.

Computed 9831,966

K-Dense-AI/scientific-agent-skills

neurokit2

Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.

Computed 9637,126

github/awesome-copilot

flowstudio-power-automate-mcp

Foundation skill for Power Automate via FlowStudio MCP — auth setup, the reusable MCP helper (Python + Node.js), tool discovery via `list_skills` / `tool_search`, and oversized-response handling. Load this skill first when connecting an agent to Power Automate. For specialized workflows, load `flowstudio-power-automate-build`, `flowstudio-power-automate-debug`, `flowstudio-power-automate-monitoring` (Pro+), or `flowstudio-power-automate-governance` (Pro+) — each contains the workflow narrative,