"Optimizing AI Model Thinking, Token Usage, and Context Size" matching MCP servers:

Enables coding agents to retrieve token-budgeted, source-hashed code context from explicitly registered local repositories via read-only MCP tools, reducing broad repository crawling while providing structure, symbols, and impact slices.

Enables AI agents to obtain a compressed code context where the active file is fully included and dependency files are reduced to type signatures, interfaces, and exports, cutting token usage while preserving code shape.

Enables analyzing and optimizing context window token usage by measuring, compressing, and eliminating waste from MCP tool definitions.

Provides AI coding agents with ground truth about a Rails app via 45 read-only tools covering schema, models, routes, controllers, views, jobs, and conventions, working over MCP or CLI and functioning even when the app cannot boot.

MCP server that reduces token consumption in AI coding assistants by up to 90% via structural reads, PreToolUse hooks, and tp-\* subagents.

Enables MCP clients to query project-scoped Graphify graphs, search, read, and write Obsidian Markdown notes, and recall or store persistent ICM memories over authenticated private HTTPS. It serializes access per project and supports slim and standard Docker deployments without embeddings or extra memory services.

An MCP server that provides structural codebase indexing and surgical query tools to drastically reduce token usage through symbol-level searches and transitive impact analysis. It supports multiple languages and integrates with git to help AI agents understand code dependencies and the impact of changes in sub-millisecond time.

Exposes compact-context MCP tools that outline large files, cap and summarize repo-wide searches, and run builds or tests while returning only grouped errors and key warnings. Guard hooks block whole-file reads, unbounded recursive greps, and raw build logs so coding sessions keep their context budget intact.

A Python-based server for managing and analyzing code context for AI-assisted development. Provides real-time code analysis, project structure analysis, dependency tracking, and secure API endpoints.

Enables querying Odoo's model registry (fields, inheritance, methods) without a database connection by loading the registry in-memory.

Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.

Token-safe code search for AI agents: queries the language-server index (clangd / Roslyn / tsserver / pyright) instead of grep and returns a token-capped file:line list — ~20x fewer tokens. Symbol-level editing + a grep→index rewrite hook. Local-only, no IDE.

Provides persistent memory and a codebase knowledge graph for AI coding assistants, enabling shared context across multiple tools like Claude, Cursor, and ChatGPT, with significant token reduction.

Graph RAG based context management engine for coding agents

Enables AI coding agents to retrieve compact structural codebase context through repository architecture summaries, AST symbol extraction, cyclomatic complexity scoring, and TODO/FIXME hotspot search.

MCP server that reduces AI agent token usage by up to 90% through intelligent context compression. Enables efficient code exploration, multi-file refactoring, and debugging by providing tools for smart reading, searching, and managing code context.

Enables AI coding assistants to automatically scan, store, and query API endpoints from codebases, providing instant lookup and semantic search to reduce context switching and token consumption.

Enables AI assistants to generate and statically verify IEC 61131-3 Structured Text logic, mutate .csp/.cpj OLE2 project containers, trigger in-GUI Error Check compilation, manage HMI screens, and run deterministic OCS scan-cycle simulations and Modbus telemetry scaling against Cscape 10.2. All physical hardware and firmware-download paths are intercepted fail-closed, so commissioning must be performed manually by authorized field personnel.

Enables AI coding assistants to retrieve a lean, optimized codebase context without lockfile and build bloat, and to analyze which files consume the most tokens.

Bridges VSCode's Language Server Protocol with MCP to give AI assistants instant access to code intelligence, delivering 100-1000x faster responses with 90% fewer tokens than traditional text-based searching. Provides 17 production-ready tools for navigation, refactoring, diagnostics, and code analysis.

A local MCP server that indexes a repository into line-range chunks and serves compact evidence packets to cut token usage when exploring or debugging code.

MCP server that helps AI coding agents understand a repository by providing lightweight tree/map, code search, and token-budgeted context packing tools without dumping the entire monorepo into the prompt.

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

Enables AI coding agents to reduce token usage by condensing source code into AST skeletons, extracting specific symbols, compressing logs, and estimating token budgets.

Enables auditing the AI models and SDKs in a repository, drafting evaluation examples, ranking candidates by cost per passing answer, and generating cheapest-first routing configurations, all through read-only local tools that never make network calls. Also estimates monthly token spend and local weights/KV-cache memory so users can pick and size the cheapest model that passes their own tests.

MCP server that inspects local codebases and generates source-grounded context packs for coding agents, with tools for repository search, symbols, dependencies, Git analysis, and context generation.

Enables efficient code navigation and retrieval through natural language search, BM25 ranking, and fuzzy matching across multiple programming languages. It drastically reduces token usage by allowing Claude to query specific code symbols and logic instead of reading entire files.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

Provides codebase indexing and retrieval tools that give AI agents token-efficient, query-relevant context packages (symbols, imports, and dependencies) instead of scanning entire repositories.