# fittok

> Use this tool when you need to efficiently process and transmit large amounts of context to a Large Language Model (LLM), as it filters and compresses data by 80-90% using code knowledge graphs and compression, solving problems of bandwidth and latency. It takes in raw context as input and outputs compressed context, making it ideal for applications where data transfer is limited or costly. Use fittok to optimize LLM performance in resource-constrained environments.

Canonical page: https://skillsregistry.net/skills/likhithreddy-fittok  
JSON: https://api.skillsregistry.net/v1/skills/likhithreddy-fittok

## Description

An MCP server that filters and compresses context by 80-90% before sending to an LLM, using code knowledge graphs and compression.

## Trust

- **Trust score (0–1):** 0.55
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-01

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/gp3xx9kz5l)
- **Repository:** <https://github.com/likhithreddy/fittok>

## 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": "likhithreddy-fittok"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/likhithreddy-fittok` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/likhithreddy-fittok/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