Renyi
OfficialRelated Servers
Alternatives to Renyi
No user-submitted related servers found.
Related Servers
- AlicenseCqualityCmaintenanceAgent-first programming language: agents produce JSON AST, the compiler validates, type-checks, effect-checks, verifies contracts via Z3/SMT, and compiles to WASM. 19 MCP tools for the full compile-and-execute loop.22402 npm13MIT
- AlicenseAqualityBmaintenanceLLM-native code tools: 44 MCP tools for AI agents to read, understand and modify code. Rust tree-sitter parse daemon (crash-isolated), SQLite symbol index (repo map 98ms), transactional journal-backed writes, deterministic quality gates (zero LLM calls). 512MB cgroup: 128 concurrent, zero OOM. MIT.131MIT
- AlicenseNot gradedqualityBmaintenanceEnables coding agents to perform deterministic, model-free repository analysis and patch operations—tree-sitter repo maps, symbol/reference lookup, and SEARCH/REPLACE parsing/validation—via a CLI or MCP stdio server, without ever calling a language model.1MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents to query code structure efficiently through 16 MCP tools, including symbol lookup, full-text search, dependency analysis, and refactor planning, powered by tree-sitter parsing and index-backed code intelligence.3MIT
- AlicenseAqualityBmaintenanceLocal code search for AI agents over a tree-sitter index of your repo: exact symbol lookup, scope-aware usages, call graph and impact analysis, AST pattern search, indexed grep and semantic search. 28 MCP tools, 19 languages, runs fully offline.2812MIT
- AlicenseAqualityAmaintenanceAn MCP server that lets AI models run code in 31 languages, evaluate symbolic math and logic problems, and measure complexity—exposed as 48 tools for execution, session management, translation, optimization, and more.5151 PyPI5Apache 2.0
TDQS
Scored across 11 tools
Each tool targets a distinct capability: language reference, stdlib lookup, project overview, single-definition detail, effect graph, static checking, formatting, execution, testing, change diff, and embedding chunks. Overlaps (e.g., project_map vs chunks) are clearly separated by their descriptions and output formats, so an agent can reliably select the right tool.
All names use snake_case, but the set mixes noun-based names (cheat_sheet, project_map, definition, effects, chunks) with verb-based names (check, format, run, diff) and one verb_noun (run_tests). There is no single predictable pattern like verb_noun throughout, though the names remain readable and descriptive.
11 tools is well-scoped for a language toolchain, covering documentation, inspection, checking, formatting, execution, testing, diffing, and embedding without obvious redundancy. Each tool earns its place and the count sits comfortably in the ideal 3–15 range.
The surface covers the core lifecycle: reference, stdlib lookup, project map, definition detail, effect graph, check, format, run, test, diff, and chunks. Minor gaps remain, such as no project-wide search by name (one must scan project_map) and no direct file editing or scaffolding, but agents can work around these.