"Actual Budget" matching MCP servers:

Enables asking natural-language questions about any GitHub repository and receiving answers grounded in the actual code, with read-only enforcement and automatic repository fetching.

Enables AI coding agents to list, read, create, update, delete, and render PlantUML and Mermaid diagrams, and check them against the actual codebase to detect architecture drift.

Compiler introspection for the Hirð language (typed actors and effect rows on the BEAM): type inference, definition lookup, effect-row explanation, IR fragments, actor protocol and supervision graphs, and token-budget-aware symbol summaries.

Serves token-efficient code context for AI coding assistants by exposing Python signatures, call graphs, and budget-packed task context as MCP tools, enabling precise codebase queries without reading entire files.

Static worst-case token-budget analysis for LLM-agent workflows using AST analysis to identify certifiable, default-dependent, non-certifiable, and runaway units, with optional signed budget certificates.

Assesses code change risk with ultra-low token budget (50-100 tokens) and provides structured risk metadata for AI agents in IDEs.

Provides role-aware repository maps for LLM agents by analyzing codebases and ranking files within a token budget.

An MCP server that helps catch context drift in AI coding agents by comparing actual code changes against the original request, allowing users to keep or roll back unintended additions.

Allows AI assistants to access and analyze your Cursor conversation history, enabling personalized coding assistance based on your actual development patterns.

Show an AI coding assistant only the code that matters for a change — callers, callees, and related functions packed into a token budget, with self-grading retrieval.

Enables verification of AI coding agent self-reports against git diff truth and a deterministic gate, producing pass/regenerate/reject directives to ensure claimed work matches actual changes.

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.

Generates and maintains grounded, enterprise-grade documentation for any codebase by analyzing real project artifacts, ensuring every claim is traceable to actual findings.

Routes narrow text-processing tools (classification, extraction, summarization, etc.) to budget models to reduce token consumption on the main model.

Enables coding agents to retrieve only the JavaScript/TypeScript code needed for a given task within a token budget, using TypeScript compiler analysis and a decision model to judge relevance.

Selects the minimum code context an LLM needs to understand a git diff: graph-based fragment selection under a token budget, deterministic output, 30+ tree-sitter languages.

Validates AI-generated code against actual codebases to catch hallucinations, dead code, and API mismatches before runtime.

Enables read-only auditing of repositories by scanning actual files, flagging risky code, secrets, and untrusted platform configs, and generating Chinese-language reports for human decision-making.

An intelligent, agentic MCP server gateway to OpenRouter's 200+ AI models. It provides budget controls, security features, semantic memory, and code search capabilities.

An MCP server that uses AI to analyze code diffs and transform vague commit messages into readable, conventional commit history. It helps maintain project documentation quality by challenging unclear messages and suggesting improvements based on actual code changes.