"MobX-State-Tree" matching MCP servers:

A Model Context Protocol (MCP) server that enables Claude to use Monte Carlo Tree Search algorithms for deep, explorative analysis of topics, questions, or text inputs.

A systematic reasoning MCP server for Claude Desktop, featuring Beam Search and Monte Carlo Tree Search to facilitate complex problem-solving and decision-making processes.

Enables AI agents to access hybrid vector, reasoning-based tree retrieval, and agent memory through the Model Context Protocol (MCP), supporting Claude Desktop and other MCP-compatible clients.

Enables persistent storage and retrieval of Tree of Thought search states in PostgreSQL, allowing the travel agent to maintain reasoning context across multi-turn itinerary optimization.

A permissioned memory tree for you and your AI — served over MCP.

This advanced memory server facilitates neural memory-based sequence learning and prediction, enhancing code generation and understanding through state maintenance and manifold optimization as inspired by Google Research's framework.

Provides function-level structural retrieval for coding agents using tree-sitter, with zero-LLM indexing and MCP serving, enabling exact function localization and cost savings.

Persistent memory for AI agents — organized by time and space. Important memories get promoted, noise decays naturally, and related knowledge clusters into a browsable topic tree. Fully automatic.

Enables agentic document retrieval over markdown, CSV, and JSONL using BM25 and tree navigation, without vector databases or embeddings, allowing AI agents to search, browse, and retrieve structured document sections.

A smart memory system for AI assistants that retrieves relevant memories using an entity graph, semantic tree, and focus buffer, with context budgeting.

This MCP server replicates how a creative strategist gathers context by expanding queries and performing hybrid search against a Google Drive knowledge base, delivering structured, role-categorized context for ad copy and creative briefs.

Local-first memory for coding agents. Discovers the memory, instructions, and rules Claude Code, Codex, and Cursor already wrote on your machine, combines them into one canonical Markdown tree, and serves grounded, cited recall through tools like ask_memory and canonical_memory.

Local MCP server for semantic code search using Tree-sitter AST parsing, local embeddings, and hybrid search; enables indexing and querying codebases entirely offline.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.

Enables LLMs to chat with long PDFs using a reasoning-based, tree-structured document index that navigates content like a human would, without requiring vector databases or hitting context limits.

Free, unlimited web search MCP server using Gemini CLI's Google Search grounding to provide cited, live web results for any AI agent.

Context+ is a semantic intelligence server that transforms codebases into searchable, hierarchical feature graphs using RAG, Tree-sitter AST, and spectral clustering. It provides tools for deep code discovery, blast radius analysis, and memory graph management for high-accuracy engineering tasks.

Local-first MCP server that gives AI agents token-efficient context from any codebase: project tree, search, and budgeted packs. Secret redaction is enforced by the server, Git scopes and syntax-aware compression keep packs small. The same engine ships as a GUI, TUI, and CLI.

Enables AI coding assistants to perform local semantic code search using AST parsing and embeddings, finding code by meaning with exact file and line locations, plus AST-based usage lookup.