toonify-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@toonify-mcp Compress the verbose JSON output from the API"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Toonify MCP
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Context compression plugin for Claude Code. Automatically trims large tool output—JSON, YAML, stack traces, and logs—before it enters the context window.
Works as a Claude Code plugin (automatic, zero-config) or as an MCP server (on-demand).
Features
Compresses large JSON, YAML, and API responses
Reduces long test failures and stack traces
Runs automatically in the background—no change to your Claude Code workflow required
Related MCP server: token-optimizer-mcp
Limitations
Skipped for short text, very small files, and content where exact original formatting must be preserved.
Installation
git clone https://github.com/PCIRCLE-AI/toonify-mcp.git
cd toonify-mcp
npm install
npm run build
npm install -g .
toonify-mcp setup
toonify-mcp doctortoonify-mcp setup adds the local marketplace and installs, updates, or re-enables the plugin automatically.
Check status
toonify-mcp statusMCP mode (optional)
toonify-mcp setup mcp
claude mcp listclaude mcp list should show toonify: toonify-mcp - ✓ Connected.
Pipe filter (any agent CLI)
toonify-mcp compress reads stdin, compresses what's safe to compress (JSON/YAML → TOON, repetitive logs collapsed; source code, prose, and precision-sensitive numbers pass through untouched), and writes to stdout. It never breaks a pipe — when compression doesn't apply, the input comes out byte-for-byte.
curl -s https://api.example.com/users | toonify-mcp compressThis works with any agent CLI, because output is compressed before it enters the model's context. To have an agent adopt it, add a rule to your project's agent instructions (e.g. AGENTS.md):
When a command is likely to print large JSON/YAML or long logs,
pipe it through `toonify-mcp compress` (e.g. `curl ... | toonify-mcp compress`).
It only rewrites output it can compress losslessly; everything else passes through.OpenAI Codex CLI (on-demand)
toonify-mcp setup codexRegisters toonify as an MCP server in ~/.codex/config.toml (a timestamped backup of the file is made first). Codex can then call the optimize_content tool on demand.
Note: on Codex this is on-demand, not automatic — Codex's hooks cannot yet replace tool output the way Claude Code's updatedToolOutput does, and appending a compressed copy would grow context instead of shrinking it. For automatic compression on Codex, use the pipe filter above.
Docs
Available Tools
5 toolscleanup_expired_cacheB
Clean up expired cache entries and return the number of entries removed
Inputs: no named parameters.
clear_cacheB
Clear the optimization result cache
Inputs: no named parameters.
get_cache_statsA
Get detailed cache statistics (LRU cache + Prompt cache)
Inputs: no named parameters.
get_statsB
Get token optimization statistics
Inputs: no named parameters.
optimize_contentB
Optimize structured data content for token efficiency using TOON format
Inputs: content (required), toolName.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 5 tools
Each tool has a distinct role—optimize, stats, and cache maintenance—but get_stats vs get_cache_stats and clear_cache vs cleanup_expired_cache could be confused without careful reading. The descriptions generally clarify the boundaries.
All tool names follow a consistent verb_noun snake_case pattern: optimize_content, get_stats, clear_cache, get_cache_stats, cleanup_expired_cache. The naming is predictable and readable.
Five tools is a well-scoped set for a token optimization utility: one core operation, one statistics endpoint, and three cache-management tools. Each tool has a clear place and none feel like filler.
The tool surface covers the main optimize operation, general statistics, and comprehensive cache lifecycle management (view, clear, cleanup expired). This is a complete and practical set for the server's stated purpose.
Maintenance
Related MCP Connectors
Compress coding-agent context ~64%. Hosted Neural Keep MCP for Cursor/Claude/Codex.
Free no-LLM token savers: URL→Markdown, outline, JSON trim, log compress, token count; x402 extras
Persistent context for Claude. Your AI always knows your projects and next actions across sessions.
Compress prose for LLM context windows. Per-call USDC billing, signed receipt.
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