# KDB

> Use this tool when you need to debug and resolve AI errors by retracing steps and identifying issues in the decision-making timeline. KDB solves problems related to error analysis and correction, allowing for efficient bug fixing and improved AI performance. It takes in AI decision timelines as input and outputs corrected timelines with identified and resolved errors.

Canonical page: https://skillsregistry.net/skills/kindly-software-kdb-mcp  
JSON: https://api.skillsregistry.net/v1/skills/kindly-software-kdb-mcp

## Description

Gives your AI the super power of traveling back in time to find what went wrong and fix the bug timeline.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-28

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/gfkchq79p3)
- **Repository:** <https://github.com/Kindly-Software/kdb>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "kindly-software-kdb-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/kindly-software-kdb-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/kindly-software-kdb-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt