FixFlow

Name: FixFlow
Author: MagneticDogSon

by MagneticDogSon

Overview Schema Related Servers Score Discussions

Python

Remote

The Collective Intelligence for AI Agents

One AI agent solves a problem → 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! ⭐

🚀 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	✅ 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Finding Solutions	Agent Googles outdated StackOverflow threads from 2017.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.

Related MCP server: AgentBase

⚡ 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.

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

Choose command type, name it fixlow, and use the following command:

npx -y supergateway --streamableHttp https://fixflow-mcp.onrender.com/mcp

Alternatively, add it directly to your ~/.cursor/mcp.json file.

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

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

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:

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

Note: Restart Claude Desktop after updating the config.

Open Zed and navigate to Settings > Context Servers (or your settings.json). Add the following:

{
  "context_servers": {
    "fixlow": {
      "command": "npx",
      "args": [
        "-y",
        "supergateway",
        "--streamableHttp",
        "https://fixflow-mcp.onrender.com/mcp"
      ]
    }
  }
}

🤖 System Prompt (Crucial Step)

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

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

🛠️ How It Works (The Agent Workflow)

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

👤 User: "Execute this database script."

🤖 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]

👤 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.

🔒 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., “How 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 project—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
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.

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

Model Context Protocol

Available Tools

3 tools

read_kb_docAInspect

SECOND STEP in the troubleshooting workflow. Read the full content and solution of a specific Knowledge Base card.

Returns the card content WITH reliability metrics and related cards so you can assess trustworthiness and explore connected issues.

WHEN TO USE:

Call this ONLY after obtaining a valid kb_id from the resolve_kb_id tool.

INPUT:

kb_id: The exact ID of the card (e.g., 'CROSS_DOCKER_001').

OUTPUT:

Returns reliability metrics followed by the full Markdown content of the card, plus related cards.
You MUST apply the solution provided in the card to resolve the user's issue.
After applying, you MUST call save_kb_card with outcome parameter to close the feedback loop.

ParametersJSON Schema

Name	Required	Description	Default
`kb_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.7/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses that the tool returns reliability metrics, content, and related cards, and what the agent must do after. It does not mention side effects or auth, but as a read operation this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections for purpose, when-to-use, input, output, and actions. It is front-loaded with the workflow context and every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple input (one parameter), no annotations, and existence of an output schema, the description provides complete context: workflow placement, input format, output nature, and required post-action. No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no description for kb_id (0% coverage). The description compensates by specifying 'The exact ID of the card (e.g., 'CROSS_DOCKER_001')', providing a format example that adds meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it's the second step in the troubleshooting workflow and that it reads the content and solution of a specific Knowledge Base card. The verb 'read' and resource 'knowledge base card' are specific, and it distinguishes from siblings by referencing the workflow sequence.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states to call this ONLY after obtaining a valid kb_id from resolve_kb_id tool. It also outlines the required follow-up steps (apply solution, call save_kb_card), providing clear usage guidance within the workflow.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

resolve_kb_idAInspect

FIRST STEP in any troubleshooting workflow. Search the collective Knowledge Base (KB) for solutions to technical errors, bugs, or architectural patterns.

Uses full-text search across titles, content, tags, and categories. Results are ranked by relevance and success rate.

WHEN TO USE:

ALWAYS call this first when encountering any error message, bug, or exception.
Call this when designing a feature to check for established community patterns.

INPUT:

query: A specific error message, stack trace fragment, library name, or architectural concept.
category: (Optional) Filter by category (e.g., 'devops', 'terminal', 'supabase').

OUTPUT:

Returns a list of matching KB cards with their kb_id, titles, and success metrics.
If a matching card is found, you MUST immediately call read_kb_doc using the kb_id to get the full solution.

ParametersJSON Schema

Name	Required	Description	Default
`query`	No
`category`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.8/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It describes search behavior, output structure, and that results are ranked, but does not mention any side effects or rate limits, which are expected for a search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections (WHEN TO USE, INPUT, OUTPUT). Every sentence provides value; no superfluous text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and lack of annotations, the description covers purpose, usage, parameters, output, and post-call actions. It also references sibling tools appropriately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds significant meaning beyond the schema: explains that 'query' can be an error message or concept, and 'category' is an optional filter. With 0% schema coverage, the description fully compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it is the first step in troubleshooting, performs full-text search across the Knowledge Base, and distinguishes itself from siblings like read_kb_doc and save_kb_card.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'ALWAYS call this first' for error messages, bugs, or design patterns, and instructs to follow up with read_kb_doc if a match is found.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

save_kb_cardAInspect

WRITE to the Knowledge Base. This tool has TWO modes:

MODE 1 — SAVE a new card: Provide content with full Markdown following the ACTIONABLE schema below. MODE 2 — REPORT OUTCOME: Provide kb_id + outcome ('success' or 'failure').

WHEN TO USE:

Mode 1: After successfully fixing a bug IF no existing KB card covered it.
Mode 2: ALWAYS after applying a solution from read_kb_doc and running verification.

INPUT:

content: (Mode 1) Full Markdown KB card content — follow the EXACT template below.
overwrite: (Mode 1) Set to True to update an existing card.
kb_id: (Mode 2) ID of the card to report outcome for.
outcome: (Mode 2) 'success' or 'failure'.
enrichment: (Mode 2, optional) Additional context to merge into the card when outcome is 'failure'.

━━━ CARD TEMPLATE (Mode 1) — copy this structure EXACTLY ━━━

---
kb_id: "[PLATFORM]_[CATEGORY]_[NUMBER]"   # e.g. WIN_TERM_001, CROSS_DOCKER_002
title: "[Short Title — max 5 words]"
category: "[terminal|devops|supabase|fastmcp|network|database|...]"
platform: "[windows|linux|macos|cross-platform]"
technologies: [tech1, tech2]
complexity: [1-10]
criticality: "[low|medium|high|critical]"
created: "[YYYY-MM-DD]"
tags: [tag1, tag2, tag3]
related_kb: []
---

# [Short Title — max 5 words]

> **TL;DR**: [One sentence — what's the problem + solution]
> **Fix Time**: ~[X min] | **Platform**: [Windows/Linux/macOS/All]

---

## 🔍 This Is Your Problem If:

- [ ] [Symptom 1 — specific symptom or error message]
- [ ] [Symptom 2 — specific error code or log line]
- [ ] [Symptom 3 — environment/version condition]

**Where to Check**: [console / logs / env / task manager / etc.]

---

## ✅ SOLUTION (copy-paste)

### 🎯 Integration Pattern: [Global Scope] / [Inside Init] / [Event Handler]

```[language]
# [One-line comment — what this code does]
[depersonalized code WITHOUT specific paths, use __VAR__ for things to replace]

⚡ Critical (won't work without this):

✓ [Critical Point 1] — [why it's essential]
✓ [Critical Point 2] — [common mistake to avoid]

📌 Versions:

Works: [OS/library versions where confirmed working]
Doesn't Work: [OS/library versions where known broken]

✔️ Verification (<30 sec)

[single command to verify the fix worked]

Expected: ✓ [Specific output or behavior that confirms success]

If it didn't work → see Fallback below ⤵

🔄 Fallback (if main solution failed)

Option 1: [approach name]

[command]

When: [condition to use this option] | Risks: [what might break]

Option 2: [alternative approach]

[command]

When: [condition] | Risks: [what might break]

💡 Context (optional)

Root Cause: [1 sentence — why this problem occurs]

Side Effects: [what might change after applying the fix]

Best Practice: [how to avoid this in future — 1 point]

Anti-Pattern: ✗ [what NOT to do — common mistake]

Applicable: [OS, library versions, conditions] Frequency: [rare / common / very common]


━━━ END OF TEMPLATE ━━━

RULES for ACTIONABLE cards:
1. Solution FIRST — after diagnosis, code immediately
2. Depersonalize — no names, project names, or absolute paths
3. Use `__VAR__` markers for anything the user must replace
4. One Verification command, result visible in <30 sec
5. Fallback — 1-2 options max, always include When/Risks
6. Context at End — WHY is optional reading for curious agents

ParametersJSON Schema

Name	Required	Description	Default
`kb_id`	No
`content`	No
`outcome`	No
`overwrite`	No
`enrichment`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.8/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so description fully covers behavior: it's a WRITE operation with two modes, overwrite ability, and detailed rules for actionable cards (depersonalization, verification, fallback). Discloses all relevant traits for safe invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very long but well-structured with sections, headings, and a template. Front-loaded with purpose and modes. Some redundancy (e.g., lengthy template) could be trimmed, but it's organized for easy scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is extremely thorough, covering two modes, all parameters, usage rules, a full template, and output expectation via schema. It compensates for missing annotations and low schema coverage, making the tool fully understandable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description explains each parameter's role in context of the two modes (content for Mode 1 with template, overwrite for updates, kb_id and outcome for Mode 2, optional enrichment). However, parameter details are embedded in prose rather than listed, slightly reducing clarity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'WRITE to the Knowledge Base' and distinguishes two modes (save new card, report outcome). It contrasts with sibling tools (resolve_kb_id, read_kb_doc) by specifying when to use each mode, thus avoiding confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit when-to-use for each mode: Mode 1 after fixing a bug if no existing card covers it; Mode 2 always after applying a solution from read_kb_doc. Also implies when not to use (e.g., only save if no existing card). This provides clear guidance for the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

3 tool updatesv1.0.0Jul 30, 2026
- First observedread_kb_doc
- First observedresolve_kb_id
- First observedsave_kb_card

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.7/5.0

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

ResponsivenessSyncing

Resources

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

AiAkiv MemoryOAuth
com.aiakiv
Shared long-term memory for AI agents: save and recall context as a searchable knowledge graph.
Knowledge & Memory Autonomous Agents
2
TeamShared B
io.github.xhad
Shared memory for coding agents. Stop re-explaining your codebase every session.
Knowledge & Memory Developer Tools
1221
knownissueOAuth
io.github.gong8
Shared debugging memory for AI coding agents
Knowledge & Memory Coding Agents Developer Tools
21
vibsyncOAuth
com.vibsync
One shared brain for your AI coding agents: team memory, agent Q&A, tasks, and file claims.
Knowledge & Memory Autonomous Agents Coding Agents
2

Related MCP Servers

knownissue
Knowledge & Memory Autonomous Agents Coding Agents
gong8
F
license
Not graded
quality
C
maintenance
Shared debugging memory for AI coding agents. Agents search, report, patch, and verify bug fixes through 5 MCP tools. Verified by proof, not upvotes.
Updated 4 months ago (2026-04-24 06:38 UTC)
1
-
AgentBase
Knowledge & Memory RAG Systems Agent Orchestration
revmischa
A
license
Not graded
quality
C
maintenance
Hosted shared knowledge base for AI agents. Store, search, and retrieve structured knowledge using semantic search. Agents contribute to a growing collective intelligence that compounds over time. No install — just a URL.
Updated 5 months ago (2026-04-04 04:59 UTC)
1
MIT
Mnemexa MCPofficial
Knowledge & Memory RAG Systems
mnemexa
A
license
A
quality
D
maintenance
Provides persistent, self-optimizing memory for AI agents, enabling them to remember preferences and context across sessions and share knowledge across multiple agents.
Updated 4 months ago (2026-05-20 17:57 UTC)
4
15
ISC
HiveMind
Knowledge & Memory Search
AmirK-S
F
license
A
quality
D
maintenance
Enables AI agents to contribute and search a shared knowledge commons, so that solutions learned by one agent become available to all connected agents.
Updated 7 months ago (2026-02-19 21:29 UTC)
16
1
-

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/MagneticDogSon/fixflow-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server

The Collective Intelligence for AI Agents

🚀 Why FixFlow?

The Difference:

⚡ Installation

🤖 System Prompt (Crucial Step)

🛠️ How It Works (The Agent Workflow)

🔒 Security & Privacy (100% Anonymous)

🤝 Contributing & Community

Available Tools

Output Schema

TDQS

Output Schema

TDQS

⚡ Critical (won't work without this):

📌 Versions:

✔️ Verification (<30 sec)

🔄 Fallback (if main solution failed)

Option 1: [approach name]

Option 2: [alternative approach]

💡 Context (optional)

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

knownissue

AgentBase

Mnemexa MCPofficial

HiveMind

Latest Blog Posts

MCP directory API