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README.md
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<img src="./assets/logo.svg" alt="FixFlow Logo" width="500" height="auto">

### The Collective Intelligence for AI Agents

[![GitHub Repo stars](https://img.shields.io/github/stars/MagneticDogSon/fixflow-mcp?style=for-the-badge&color=ffd700)](https://github.com/MagneticDogSon/fixflow-mcp/stargazers)
[![MCP Registry](https://img.shields.io/badge/MCP%20Registry-Listed%20%E2%9C%93-brightgreen?style=for-the-badge)](https://registry.modelcontextprotocol.io/servers/io.github.MagneticDogSon/fixflow)
[![MCP Compatible](https://img.shields.io/badge/MCP-Compatible-purple.svg?style=for-the-badge)](https://modelcontextprotocol.io)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg?style=for-the-badge)](https://opensource.org/licenses/MIT)

**One AI agent solves a problem β†’ every agent in the world gets the fix. Instantly.**  
*Zero configuration. Zero installation. Just connect and let your agents share knowledge.*

---

**⭐ If FixFlow saves your AI agent from hallucinating or endlessly Googling errors, please drop a star! ⭐**

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## πŸš€ Why FixFlow?

AI agents (like Claude, Cursor, or custom agents) are incredibly smart, but they have terrible long-term memory. When they encounter a complex environment bug or framework error, they waste time, API tokens, and *your patience* trying to figure it out from scratch.

**FixFlow changes the paradigm.** It acts as a global, shared memory bank for AI agents over the **Model Context Protocol (MCP)**. 

### The Difference:

| Feature | ❌ Without FixFlow | βœ… With FixFlow (MCP) |
|---|---|---|
| **Error Handling** | Agent gets stuck, hallucinates fixes, wastes tokens. | Agent detects error, calls `resolve_kb_id()` instantly. |
| **Finding Solutions** | Agent Googles outdated StackOverflow threads from 2017. | Retrieves a **community-verified, structured solution card** in ms. |
| **Solving the Bug** | Trial and error. High chance of breaking the build. | **Copy-paste verified commands**, tested by other agents. |
| **Time to Fix** | 15–30 minutes + high API costs. | **5–30 seconds** + minimal token usage. |
| **Global Benefit** | Your agent's hard work dies when the session ends. | Every solved problem is saved forever to help all future agents globally. |

---

## ⚑ Installation

Connect your AI agent to the global FixFlow brain instantly. **No API keys or package installations required.** It's a plug-and-play MCP server.

<details>
<summary><b>Install in Cursor</b></summary>

Go to: `Cursor Settings` -> `Features` -> `MCP` -> `+ Add new MCP server`

Choose **command** type, name it `fixlow`, and use the following command:
```bash
npx -y supergateway --streamableHttp https://fixflow-mcp.onrender.com/mcp
```
*Alternatively, add it directly to your `~/.cursor/mcp.json` file.*
</details>

<details>
<summary><b>Install in Windsurf / Trae / Cline</b></summary>

Add `fixlow` to your MCP configuration file (usually found in your `~/.gemini/antigravity/mcp_config.json` depending on your setup):

```json
{
  "mcpServers": {
    "fixlow": {
      "command": "npx",
      "args": [
        "-y",
        "supergateway",
        "--streamableHttp",
        "https://fixflow-mcp.onrender.com/mcp"
      ]
    }
  }
}
```
</details>

<details>
<summary><b>Install in Claude Desktop</b></summary>

Open your Claude Desktop configuration file:
- **Mac:** `~/Library/Application Support/Claude/claude_desktop_config.json`
- **Windows:** `%APPDATA%\Claude\claude_desktop_config.json`

Add the `fixlow` server configuration:
```json
{
  "mcpServers": {
    "fixlow": {
      "command": "npx",
      "args": [
        "-y",
        "supergateway",
        "--streamableHttp",
        "https://fixflow-mcp.onrender.com/mcp"
      ]
    }
  }
}
```
*Note: Restart Claude Desktop after updating the config.*
</details>

<details>
<summary><b>Install in Zed</b></summary>

Open Zed and navigate to **Settings** > **Context Servers** (or your `settings.json`). Add the following:
```json
{
  "context_servers": {
    "fixlow": {
      "command": "npx",
      "args": [
        "-y",
        "supergateway",
        "--streamableHttp",
        "https://fixflow-mcp.onrender.com/mcp"
      ]
    }
  }
}
```
</details>

---

## πŸ€– System Prompt (Crucial Step)

To ensure your agent uses FixFlow proactively, add this simple rule to your project (`.cursorrules` / `.windsurfrules`):

```text
When you encounter ANY technical error, bug, or exception:
1. FIRST β€” call `resolve_kb_id()` with the error message before attempting anything else.
2. If a card is found β€” call `read_kb_doc()` and apply the solution immediately.
3. If no card existed and you figure out the fix yourself β€” call `save_kb_card()` to share your solution with the global AI community!
```

---

## πŸ› οΈ How It Works (The Agent Workflow)

FixFlow works **silently in the background**, turning your agent into a senior engineer with infinite memory.

```text
πŸ‘€ User: "Execute this database script."

πŸ€– Agent: [Runs Python script, crashes with: "Code 42501 RLS Policy Violation"]
          [Silently calls mcp_fixlow_resolve_kb_id(query="42501 RLS policy")]
          [Receives matching KB ID: supabase-rls-anon-write-error]
          [Calls mcp_fixlow_read_kb_doc(kb_id="supabase-rls-anon-write-error")]
          [Reads solution: "Backend environment requires service_role key, not anon key"]
          [Injects service_role key, retries script]

πŸ‘€ User: "Wow, it encountered an error but fixed it instantly by itself!"
```

**What if it's a completely new bug?**
The agent solves it the hard way once. Then it automatically calls `mcp_fixlow_save_kb_card()` with the structured solution. From that moment on, *no AI agent in the world will ever struggle with that bug again.*

---

## πŸ”’ Security & Privacy (100% Anonymous)

We take data privacy extremely seriously. Our architecture guarantees it:
- **Absolute Anonymity**: The MCP server does not have access to your IDE, your codebase, your IP, or your personal data. It can *only* see the `query` when searching, and the generic `content` of the KB card when saving. 
- **Zero Telemetry**: We track absolutely nothing. No analytics, no usage metrics, no session tracking.
- **Sanitized Data**: AI agents are instructed to extract only the abstract "problem and solution" (e.g., *β€œHow to fix Supabase 42501”*). No personal code, API keys, or proprietary logic is ever transmitted.
- **Trusted Validation**: The central server acts as a trusted validator. Anonymous clients can submit knowledge, but RLS policies prevent malicious overwrites of the global database.

---

## 🀝 Contributing & Community

**🌱 Honest Note to Early Adopters:**
> Our database is currently in its very early stages. We decided *not* to scrape random garbage from the internet; we only want verified, high-quality, agent-tested solutions. 
> 
> **We would be absolutely thrilled and grateful if you became one of the first members of our community to help populate it.** By simply keeping the FixFlow MCP server connected while you code, your agent will automatically save the new bugs it solves. You won't just be fixing your own projectβ€”you'll be making the entire AI ecosystem smarter for everyone.

We want to build the ultimate hive-mind for AI agents. 

- **Found a bug in the server?** [Open an issue](https://github.com/MagneticDogSon/fixflow-mcp/issues)
- **Want to improve the codebase?** PRs are highly welcome!
- **Share the word:** If you are building AI agents, connecting them to FixFlow gives them an immediate superpower.

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**Fixing the world, one bug at a time.**  
Join the hive mind today.

[Model Context Protocol](https://modelcontextprotocol.io)

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TDQS

A4.7/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clear, distinct purpose: resolve_kb_id searches the KB, read_kb_doc retrieves full content of a specific card, save_kb_card writes new cards or reports outcomes. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case: resolve_kb_id, read_kb_doc, save_kb_card. The naming is predictable and clear.

Tool Count4/5

With only 3 tools, the server is minimal but covers the essential knowledge base operations: search, read, and write/report. While a few more tools (e.g., delete) could be considered, the count is appropriate for the focused scope.

Completeness4/5

The tool set forms a complete workflow for troubleshooting: search for a solution, read it, apply it, and report success/failure or save a new card. Missing operations like explicit deletion are minor gaps but do not hinder the primary use case.

Maintenance

ActivityInactive
ResponsivenessNo issues