KnowledgeSmith
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Optimizes LLM interaction with RBT documents by enabling partial operations, drastically reducing token usage for editing specific content sections.
About
KnowledgeSmith is an innovative MCP server designed to streamline and cost-optimize how AI models interact with RBT documents. It achieves an impressive 80-95% reduction in LLM token consumption by allowing targeted, partial operations on document sections or blocks, eliminating the need to process entire files. Beyond token efficiency, KnowledgeSmith offers structured editing capabilities, intelligent caching, fuzzy search for document retrieval, and template-driven document creation, providing a robust suite of tools for efficient RBT document management and seamless AI integration.
Key Features
- Smart Caching: LRU + TTL cache for frequently accessed documents
- TASK Fuzzy Search: Find TASK files by index (e.g., "001" matches "TASK-001-PathResolver.md")
- Token Optimization: 80-95% reduction in LLM token usage
- Structured Operations: Edit specific sections/blocks without loading entire documents
- Template-based Creation: Auto-fill placeholders (project-id, feature-id, date)
- 0 GitHub stars
Use Cases
- Efficiently updating specific sections within large, structured documents using AI.
- Automating the creation of new documents from pre-defined templates with auto-filled placeholders.
- Reducing LLM operational costs for document editing and management.
Sponsored