Mcp Integration Reference logo

Mcp Integration Reference

CommunityPopular
FlorianBruniaux
mcp-integration-reference

Template for skills that integrate with an MCP server. Demonstrates the reference file pattern: Claude reads a domain-specific MCP cheatsheet before making any tool calls, reducing query failures caused by server-specific gotchas. Fork this skill and replace the Sentry example with your target MCP.

Overview

PublisherFlorianBruniaux
Repositoryclaude-code-ultimate-guide
Skill namemcp-integration-reference
Stars
6K
Forks
782
Bundled files
1
LicenseCC-BY-SA-4.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by FlorianBruniaux on GitHub. Read the source before you install it.

Installation

Install the Mcp Integration Reference AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/FlorianBruniaux/claude-code-ultimate-guide.git /tmp/claude-code-ultimate-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-ultimate-guide/examples/skills/mcp-integration-reference .claude/skills/mcp-integration-reference
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mcp Integration Reference in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mcp Integration Reference on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mcp Integration Reference is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

MCP Integration Reference Pattern

This is a template skill. It shows how to structure a skill that wraps an MCP server. Replace sentry with your MCP server name and adapt the reference file at references/sentry-mcp.md.

What This Pattern Solves

When a skill calls an MCP server without prior context, Claude guesses at the query syntax. This works for simple calls but breaks on anything with non-obvious behavior: pagination quirks, required parameter combinations, rate limits, or subtle format restrictions.

The fix: a references/<mcp-name>.md file that captures all the gotchas. The skill reads this file before making any MCP call. Zero guessing.

Three types of content go in the reference file:

  1. Parameter semantics that differ from what the tool name implies
  2. Known error patterns and their root causes
  3. Working query examples (copy-paste, no thinking required)

Step 1: Read the MCP Reference File

Before doing anything else, read the full MCP reference:

Read: references/sentry-mcp.md

This file contains query syntax, known gotchas, and working examples for the Sentry MCP. Do not skip this step.


Step 2: Gather Scope from User

Ask the user:

  • Time range: Last 24h? 7 days? Custom range?
  • Environments: production, staging, or both?
  • Projects: All projects or specific ones? (Default: all)

If the user says "just run it with defaults", use:

  • Time range: last 72 hours
  • Environment: production only
  • Projects: all

Step 3: Fetch Error Data

Using the tool knowledge from Step 1, fetch:

  1. Issue list: Active unresolved issues, ordered by frequency
  2. Event details: Full stack traces for the top 5 issues by event count

Cap results at 50 issues. If more exist, note the count and focus on the highest-frequency items.


Step 4: Group and Analyze

Group issues by root cause, not by error message. Two issues with different messages can share the same underlying cause (shared code path, same external dependency, same config).

For each group:

  • Count of issues in the group
  • Earliest first-seen date
  • Affected users count (if available)
  • Most likely root cause (one sentence, evidence-based)
  • Relevant file paths from the stack trace

Step 5: Generate Report

Output a markdown report with this structure:

markdown
# Error Report: [Project or Scope]

**Period**: [start] to [end]
**Environment**: [env]
**Total active issues**: [N]

## Summary

[2-3 sentences: what is the overall health picture?]

## Issue Groups

### Group 1: [Root Cause Label]

| Attribute      | Value                    |
|----------------|--------------------------|
| Issues         | N                        |
| Total events   | N                        |
| Affected users | N                        |
| First seen     | YYYY-MM-DD               |
| Key file       | path/to/file.py:line     |

**Root cause**: [One paragraph. Specific, evidence-based. Point to file and line.]

**Suggested investigation**: [One or two concrete next steps.]

---

[Repeat for each group]

## Out of Scope

[List issues explicitly excluded and why. Example: "404s on /static/ excluded - expected behavior for SPA asset versioning."]

Scope Rules

  • This skill detects and describes issues. It does not modify code or create tickets.
  • If an issue is ambiguous, flag it as "needs investigation" rather than guessing.
  • Do not include informational logs or warnings unless they correlate directly with errors.

Adapting This Template

To fork this skill for a different MCP:

  1. Copy this directory: cp -r examples/skills/mcp-integration-reference examples/skills/<your-skill>/
  2. Rename references/sentry-mcp.md to references/<your-mcp>.md
  3. Replace the reference file content with your MCP's gotchas
  4. Update allowed-tools in the frontmatter to match your MCP tool names
  5. Adjust the analysis steps to match your data domain

The pattern works for any MCP that has non-obvious query behavior: Datadog, PagerDuty, Linear, Jira, Posthog, Mixpanel, etc.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mcp Integration Reference AI skill do?

Template for skills that integrate with an MCP server. Demonstrates the reference file pattern: Claude reads a domain-specific MCP cheatsheet before making any tool calls, reducing query failures caused by server-specific gotchas. Fork this skill and replace the Sentry example with your target MCP.

Why use Mcp Integration Reference on TypingMind?

Because you install it once and use it with any model. Mcp Integration Reference is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mcp Integration Reference in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FlorianBruniaux/claude-code-ultimate-guide/tree/main/examples/skills/mcp-integration-reference. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mcp Integration Reference?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mcp Integration Reference?

As many as you like. As long as a model supports skills, you can use Mcp Integration Reference with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mcp Integration Reference AI skill free?

Yes. It is published on GitHub by FlorianBruniaux under the CC-BY-SA-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇