Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Tyler-R-Kendrick/agent-skills --skill mcpgit clone --depth 1 https://github.com/Tyler-R-Kendrick/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tyler-r-kendrick/agent-skills/mcp)<a href="https://agentmods.dev/skills/tyler-r-kendrick/agent-skills/mcp"><img src="https://agentmods.dev/badge/skills/tyler-r-kendrick/agent-skills/mcp/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tyler-r-kendrick/agent-skills/mcp"><img src="https://agentmods.dev/badge/skills/tyler-r-kendrick/agent-skills/mcp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00087 | $0.01072 |
| Opus 5 | $0.00044 | $0.00536 |
| Sonnet 5 | $0.00017 | $0.00214 |
| Haiku 4.5 | $0.00009 | $0.00107 |
Grade A, and why
mcp scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCP — Model Context Protocol
Overview
MCP is an open protocol from Anthropic that standardizes how AI agents discover and use tools, access resources, and receive prompts from external servers. It enables a universal interface between AI models and the systems they interact with.
Architecture
Host (Claude, VS Code, IDE)
└── Client (maintains 1:1 connection)
└── Server (exposes tools, resources, prompts)
Core Primitives
Tools
Functions the agent can call:
{
"name": "get_weather",
"description": "Get current weather for a city",
"inputSchema": {
"type": "object",
"properties": {
"city": { "type": "string" }
},
"required": ["city"]
}
}
Resources
Data the agent can read (files, database records, API responses):
{
"uri": "file:///project/config.json",
"name": "Project Configuration",
"mimeType": "application/json"
}
Prompts
Reusable prompt templates the server can offer:
{
"name": "summarize",
"description": "Summarize a document",
"arguments": [
{ "name": "content", "description": "Text to summarize", "required": true }
]
}
Transport Options
| Transport | Use Case |
|---|---|
| stdio | Local servers running as child processes |
| Streamable HTTP | Remote servers over HTTP with SSE streaming |
Building an MCP Server (Python)
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("weather-server")
@mcp.tool()
def get_weather(city: str) -> str:
"""Get current weather for a city."""
return f"Sunny, 72°F in {city}"
@mcp.resource("config://app")
def get_config() -> str:
"""Return application configuration."""
return '{"theme": "dark", "lang": "en"}'
Building an MCP Server (TypeScript)
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
const server = new McpServer({ name: "weather-server", version: "1.0.0" });
server.tool("get_weather", { city: z.string() }, async ({ city }) => ({
content: [{ type: "text", text: `Sunny, 72°F in ${city}` }],
}));
const transport = new StdioServerTransport();
await server.connect(transport);
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- AGENTS.md 3.5 KB
- metadata.json 751 B
- README.md 599 B
- rules/_sections.md 1.5 KB
- rules/_template.md 362 B
- rules/mcp-implement-proper-error-handling.md 323 B
- rules/mcp-keep-tool-descriptions-clear-and-specific.md 352 B
- rules/mcp-return-structured-data-json-from-tools-when-possible-for.md 375 B
- rules/mcp-use-json-schema-for-inputschema-with-required-fields.md 386 B
- rules/mcp-use-resources-for-read-only-data-access-and-tools-for.md 364 B
- rules/mcp-use-stdio-transport-for-local-development-and-streamable.md 397 B
- rules/mcp-version-your-server-capabilities-so-clients-can-adapt-to.md 355 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 143 lines · 87 tokens per session scan A 2bfecc8042d9
mcp is a skill published in the GitHub repository Tyler-R-Kendrick/agent-skills (11 stars, last pushed 3mo ago), licensed MIT. It adds 87 tokens to every session and 1,072 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…