octave-mcp
by elevanaltd·★ 51·Score 45
OCTAVE MCP server provides structured document infrastructure with canonicalization, schema validation, and mythological compression for AI artifacts.
Overview
The OCTAVE MCP server implements a structured document format with deterministic normalisation, schema validation, and grammar compilation capabilities. It offers token reduction through mythological compression while maintaining document integrity across multiple agents and transformations. The server provides both HTTP and stdio transport options and includes tools for validation, writing, projecting to different views, and compiling grammar constraints.
Try asking AI
After installing, here are 5 things you can ask your AI assistant:
When to choose this
Choose OCTAVE when working with AI documents that need to maintain consistency across multiple agents or compression steps, especially when token efficiency and deterministic output are critical.
When NOT to choose this
Don't choose OCTAVE for single-step prompts, freeform prose documents, or code output where structured formatting isn't needed.
Tools this server exposes
4 tools extracted from the READMEoctave_validateValidate against schema. Field errors, repair suggestions, zone coverage
octave_writeWrite files through the full validation pipeline
octave_ejectProject to different views — canonical, executive summary, developer, template
octave_compile_grammarCompile schema constraints to GBNF grammar for constrained generation
Comparable tools
Installation
Install via pip:
pip install octave-mcpFor Claude Desktop, add to config.json:
{
"mcpServers": {
"octave": {
"command": "octave-mcp-server"
}
}
}For HTTP transport:
octave-mcp-server --transport http --port 8080FAQ
- What is mythological compression?
- Mythological compression uses classical mythology terms as pre-compressed semantic vocabulary that's already in LLM weights, allowing more information to be conveyed in fewer tokens.
- How does OCTAVE ensure document consistency?
- OCTAVE provides canonical normalisation that ensures the same input always produces the same output, with automatic normalization of formatting issues and detailed receipts of all transformations.
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Last updated · Auto-generated from public README + GitHub signals.