Serena MCP
SERENA MCP
┌─────────────────────────────────────────────────────┐
│ PROBLEM: AI kod yazarken codebase'i anlamıyor │
│ Token limit, grep-like search, string find │
└─────────────────────────────────────────────────────┘
▼
┌─────────────────────────────────────────────────────┐
│ SERENA SOLUTION (MCP + LSP Integration) │
│ │
│ Code ──> AST Parser ──> LSP ──> Symbols │
│ │ │
│ AI Agent ────────────────┘ │
│ │ │
│ ├─ find_symbol │
│ ├─ find_referencing_symbols │
│ ├─ insert_after_symbol │
│ └─ semantic_code_search │
└─────────────────────────────────────────────────────┘
▼
┌─────────────────────────────────────────────────────┐
│ OUTCOME: IDE gibi kod anlama + düzenleme │
│ Token-efficient, symbol-level navigation │
└─────────────────────────────────────────────────────┘
ALTERNATİFLER (2025)
Context Management (Serena'ya En Yakın):
A. CLAUDE CONTEXT (Zilliz)
┌────────────────────────────────────┐
│ Codebase → Vector DB (Zilliz) │
│ Query → Hybrid Search │
│ (BM25 + Dense Vector) │
│ │
│ ✓ ~40% token reduction │
│ ✓ Incremental indexing (Merkle) │
│ ✓ AST-based chunking │
│ ⚠ Requires Zilliz Cloud + OpenAI │
└────────────────────────────────────┘
B. CHROMA MCP
┌────────────────────────────────────┐
│ Documents → Vector DB │
│ Semantic + Full-text search │
│ │
│ ✓ Persistent storage │
│ ✓ Open source │
│ ⚠ Document-focused (not code) │
└────────────────────────────────────┘
Framework Alternatifleri:
C. LANGCHAIN/LANGGRAPH
Developer Framework → Custom tools
⚠ Complexity, security responsibility
D. SEMANTIC KERNEL (Microsoft)
.NET/Python SDK → Memory management
⚠ Microsoft ecosystem dependency
E. VERTEX AI (Google)
Managed platform → Enterprise features
⚠ Vendor lock-in, Google Cloud only
KARŞILAŞTIRMA
Serena Claude Chroma LangChain
Context
────────────────────────────────────────────────────
Code-focused ✓ ✓ ✗ ✗
Semantic search ✓ ✓ ✓ △
Symbol-level ✓ ✗ ✗ ✗
LSP integration ✓ ✗ ✗ ✗
Free/Open source ✓ ✓ ✓ ✓
Setup complexity △ △ ○ ●
Token efficiency ●●● ●●● ●● ○
30+ languages ✓ ✗ N/A N/A
IDE-like tools ✓ ✗ ✗ ✗
Vector DB req. ✗ ✓ ✓ △
Legend: ✓=Yes ✗=No △=Partial ○=Low ●=Medium ●●=High ●●●=Very High
PATTERN
Serena: LSP → Symbol-level → IDE tools
Claude Context: Vector DB → Semantic search → Hybrid retrieval
Chroma: Vector DB → Document management → Persistent storage
LangChain: Framework → Custom orchestration → 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 Güncellemeler
MCP 1. yıldönümü (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/Previousmy 1st response to TLDR - Prompts don't scale. MCPs don't scale. Hooks do.NextWeb vs macOS - Responsive Problem
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