Feature Description

Introduce semantic search pre-filtering (with reranking) as a complementary step to Code Mode.
This feature reduces large toolsets (1,000+ tools and enumerations) into a smaller, semantically relevant subset before Code Mode operates with LLM, further reducing LLM token use and simultaneously improving accuracy.

Motivation

• LLM tokens are more expensive compared to embedding tokens.
• Even state-of-the-art models with 1M+ context windows suffer from Context Rot when overloaded with too many tools, especially in multi-step tasks.

Proposed Solution

1. Apply semantic search pre-filtering to narrow down thousands of tools to a few dozen and narrow down their enum parameters.
2. Use reranking to refine the subset further, prioritizing the most relevant tools.
3. Extend this approach to enumerations within tools, reducing large option sets.
4. Pass the reduced set into Code Mode, which then optimizes token usage and reasoning clarity with LLM.

This creates a scalable, automated RAG pipeline capable of handling millions of tools without manual intervention.

Alternatives Considered

• Compressing tool definitions with LLMs (suboptimal, loses semantics, requires manual versioning).
• Manual curation of tool subsets (tedious, unscalable) is not suitable for MCP clients.
• Relying solely on Code Mode (keeps inefficiency with very large toolsets, prone to Context Rot).

Use Cases

1. Reducing 1,000+ tools into a manageable subset for complex workflows.
2. Narrowing large enumerations within tools before LLM reasoning.
3. Automating tool selection in dynamic environments (e.g., MCP) without manual intervention.

Example Usage

# Command line example
mcp-use semantic-pre-filter --tools large_toolset.json --rerank --codemode

Implementation Details

• Integrate semantic search embeddings for pre-filtering.
• Use reranking algorithms to prioritize relevance.
• Treat the pipeline as a standard RAG workflow for scalability.
• Reference implementation ideas: Stanford A1 ReduceAndGenerate() in extra strategies
• Improve existing MCP-Use search_tools functionality to pre-filter before Code Mode, and also reduce the large list of enums in the tool parameter.

Suggested Models:

• Batch Qwen3-Embedding
• Qwen3-Reranker

Breaking Changes

[ ] This feature would introduce breaking changes
[x] This feature is backwards compatible

Additional Context

Code Mode already reduces LLM token consumption.
Semantic pre-filtering further improves efficiency and accuracy by shifting work to cheaper embedding tokens.
Helps avoid Context Rot in multi-step tasks with large toolsets.
Complements Code Mode rather than replacing it. Suitable for MCP clients.