TOON MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| toon_encodeA | Convert data to TOON (Token-Oriented Object Notation) format to reduce token usage by 50-70%. Supports: JSON, CSV, TSV, XML, HTML tables, YAML, and raw objects. TOON uses a header-based format where field names are defined once:
Use this before sending large datasets to LLMs to save tokens and costs. |
| toon_decodeC | Decode TOON format back to JSON object or array. |
| toon_analyzeC | Analyze data and show potential token savings with TOON encoding. |
| toon_optimize_promptA | Optimize a prompt containing data for token efficiency. Detects data structures within the prompt and converts them to TOON format, adding instructions for the LLM to understand the format. Returns the optimized prompt with estimated token savings. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a distinct and non-overlapping purpose: toon_analyze assesses potential savings, toon_encode converts data to TOON format, toon_decode reverts TOON to original formats, and toon_optimize_prompt specifically handles prompt optimization. There is no ambiguity in tool selection.
All tools follow a consistent 'toon_' prefix with a clear verb_noun pattern (e.g., toon_analyze, toon_encode, toon_decode, toon_optimize_prompt). This uniformity makes the tool set predictable and easy to understand.
With 4 tools, the server is well-scoped for its purpose of TOON encoding and optimization. Each tool serves a specific function in the data processing workflow, and there are no extraneous or missing tools for this focused domain.
The tool set covers the core TOON workflow comprehensively: analyze, encode, decode, and optimize prompts. A minor gap might be the lack of a tool for batch processing or handling specific edge cases, but the existing tools allow agents to perform essential operations without dead ends.