Serena MCP

SERENA MCP

  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
  β”‚ PROBLEM: AI kod yazarken codebase'i anlamΔ±yor      β”‚
  β”‚          Token limit, grep-like search, string find β”‚
  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                          β–Ό
  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
  β”‚ SERENA SOLUTION (MCP + LSP Integration)             β”‚
  β”‚                                                     β”‚
  β”‚  Code ──> AST Parser ──> LSP ──> Symbols           β”‚
  β”‚                           β”‚                         β”‚
  β”‚  AI Agent β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜                         β”‚
  β”‚    β”‚                                                β”‚
  β”‚    β”œβ”€ find_symbol                                  β”‚
  β”‚    β”œβ”€ find_referencing_symbols                     β”‚
  β”‚    β”œβ”€ insert_after_symbol                          β”‚
  β”‚    └─ semantic_code_search                         β”‚
  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                          β–Ό
  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
  β”‚ OUTCOME: IDE gibi kod anlama + dΓΌzenleme           β”‚
  β”‚          Token-efficient, symbol-level navigation   β”‚
  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

  ALTERNATΔ°FLER (2025)

  Context Management (Serena'ya En YakΔ±n):

  A. CLAUDE CONTEXT (Zilliz)
  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
  β”‚ Codebase β†’ Vector DB (Zilliz)     β”‚
  β”‚ Query β†’ Hybrid Search              β”‚
  β”‚ (BM25 + Dense Vector)              β”‚
  β”‚                                    β”‚
  β”‚ βœ“ ~40% token reduction             β”‚
  β”‚ βœ“ Incremental indexing (Merkle)   β”‚
  β”‚ βœ“ AST-based chunking               β”‚
  β”‚ ⚠ Requires Zilliz Cloud + OpenAI  β”‚
  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

  B. CHROMA MCP
  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
  β”‚ Documents β†’ Vector DB              β”‚
  β”‚ Semantic + Full-text search        β”‚
  β”‚                                    β”‚
  β”‚ βœ“ Persistent storage               β”‚
  β”‚ βœ“ Open source                      β”‚
  β”‚ ⚠ Document-focused (not code)      β”‚
  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

  Framework Alternatifleri:

  C. LANGCHAIN/LANGGRAPH
     Developer Framework β†’ Custom tools
     ⚠ Complexity, security responsibility

  D. SEMANTIC KERNEL (Microsoft)
     .NET/Python SDK β†’ Memory management
     ⚠ Microsoft ecosystem dependency

  E. VERTEX AI (Google)
     Managed platform β†’ Enterprise features
     ⚠ Vendor lock-in, Google Cloud only

  KARŞILAŞTIRMA

                   Serena  Claude   Chroma  LangChain
                           Context
  ────────────────────────────────────────────────────
  Code-focused       βœ“       βœ“        βœ—       βœ—
  Semantic search    βœ“       βœ“        βœ“       β–³
  Symbol-level       βœ“       βœ—        βœ—       βœ—
  LSP integration    βœ“       βœ—        βœ—       βœ—
  Free/Open source   βœ“       βœ“        βœ“       βœ“
  Setup complexity   β–³       β–³        β—‹       ●
  Token efficiency   ●●●     ●●●      ●●      β—‹
  30+ languages      βœ“       βœ—        N/A     N/A
  IDE-like tools     βœ“       βœ—        βœ—       βœ—
  Vector DB req.     βœ—       βœ“        βœ“       β–³

  Legend: βœ“=Yes βœ—=No β–³=Partial β—‹=Low ●=Medium ●●=High ●●●=Very High

  PATTERN

  Serena: LSP β†’ Symbol-level β†’ IDE tools
  Claude Context: Vector DB β†’ Semantic search β†’ Hybrid retrieval
  Chroma: Vector DB β†’ Document management β†’ Persistent storage
  LangChain: Framework β†’ Custom orchestration β†’ Developer control

  CONSTRAINT

  - Serena: Python 3.8+, LSP servers for each language
  github.com/oraios/serena
  - Claude Context: Node 20-23, Zilliz Cloud token, OpenAI API
  github.com/zilliztech/claude-context
  - Chroma: Python env, client setup docs.trychroma.com
  - LangChain: Framework complexity, security self-managed
  python.langchain.com

  2025 GΓΌncellemeler

  MCP 1. yΔ±ldΓΆnΓΌmΓΌ (2025-11-25):
  - Task-based workflows (long-running operations)
  - URL mode elicitation (secure OAuth)
  - Sampling with tools (agentic servers)
  - 2000+ MCP servers ecosystem

  Sources:
  - https://github.com/oraios/serena
  - https://medium.com/@souradip1000/deconstructing-serenas-mcp-powered-se
  mantic-code-understanding-architecture-75802515d116
  - https://www.merge.dev/blog/model-context-protocol-alternatives
  - https://github.com/zilliztech/claude-context
  - https://cyberpress.org/best-mcp-servers/
  - https://blog.modelcontextprotocol.io/posts/2025-11-25-first-mcp-annive
  rsary/

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