simplify MCP interface for Claude Code
- Remove unnecessary search parameters: search_mode, recompute_embeddings, file_types, min_score - Remove leann_clear tool (not needed for Claude Code workflow) - Streamline search to only use: query, index_name, top_k, complexity - Keep core tools: leann_index, leann_search, leann_status, leann_list 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Andy Lee
@@ -111,29 +111,6 @@ This is your first step for any serious codebase work - think of it as giving yo
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"maximum": 128,
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"maximum": 128,
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"description": "Search complexity level. Use 16-32 for fast searches (recommended), 64+ for higher precision when needed.",
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"description": "Search complexity level. Use 16-32 for fast searches (recommended), 64+ for higher precision when needed.",
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},
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},
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"search_mode": {
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"type": "string",
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"enum": ["fast", "balanced", "precise"],
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"default": "balanced",
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"description": "Search strategy: 'fast' (~2-5s), 'balanced' (~5-10s), 'precise' (~10-20s). Choose based on time vs accuracy needs.",
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},
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"recompute_embeddings": {
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"type": "boolean",
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"default": False,
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"description": "Recompute embeddings for maximum accuracy. Enable only when precision is more important than speed.",
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},
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"file_types": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Filter results by file types (e.g., ['py', 'js', 'ts']). Searches all indexed file types if not specified.",
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},
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"min_score": {
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"type": "number",
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"minimum": 0.0,
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"maximum": 1.0,
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"default": 0.0,
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"description": "Minimum relevance score threshold (0.0-1.0). Higher values return more relevant but fewer results.",
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},
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},
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},
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"required": ["index_name", "query"],
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"required": ["index_name", "query"],
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},
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},
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@@ -151,25 +128,6 @@ This is your first step for any serious codebase work - think of it as giving yo
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},
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},
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},
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},
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},
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},
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{
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"name": "leann_clear",
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"description": "🗑️ Safely delete a code index (with confirmation required). Think of it as 'rm -rf' but for your search indexes - be careful!",
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"inputSchema": {
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"type": "object",
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"properties": {
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"index_name": {
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"type": "string",
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"description": "Name of the index to clear/delete.",
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},
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"confirm": {
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"type": "boolean",
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"default": False,
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"description": "Confirmation flag. Must be set to true to actually perform the deletion.",
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},
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},
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"required": ["index_name"],
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},
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},
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{
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{
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"name": "leann_list",
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"name": "leann_list",
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"description": "📋 Show all your indexed codebases - your personal code library! Use this to see what's available for search.",
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"description": "📋 Show all your indexed codebases - your personal code library! Use this to see what's available for search.",
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@@ -263,53 +221,18 @@ This is your first step for any serious codebase work - think of it as giving yo
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},
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},
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}
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}
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# Build command with enhanced parameters
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# Build simplified command
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cmd = [
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cmd = [
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"leann",
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"leann",
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"search",
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"search",
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args["index_name"],
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args["index_name"],
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args["query"],
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args["query"],
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f"--top-k={args.get('top_k', 5)}",
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f"--top-k={args.get('top_k', 5)}",
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f"--complexity={args.get('complexity', 32)}",
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]
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]
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# Handle search mode mapping to set complexity and beam width
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search_mode = args.get("search_mode", "balanced")
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if search_mode == "fast":
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cmd.extend(["--complexity=16", "--beam-width=1"])
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elif search_mode == "precise":
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cmd.extend(["--complexity=64", "--beam-width=2"])
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else: # balanced mode
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complexity = args.get("complexity", 32)
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cmd.append(f"--complexity={complexity}")
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# Handle recompute embeddings
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if args.get("recompute_embeddings", False):
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cmd.append("--recompute-embeddings")
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# Handle file types filtering
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file_types = args.get("file_types")
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if file_types and isinstance(file_types, list):
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# Validate file extensions
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valid_extensions = []
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for ext in file_types:
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if isinstance(ext, str) and ext.strip():
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clean_ext = ext.strip()
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if not clean_ext.startswith("."):
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clean_ext = "." + clean_ext
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valid_extensions.append(clean_ext)
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if valid_extensions:
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cmd.extend(["--filter-extensions", ",".join(valid_extensions)])
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result = subprocess.run(cmd, capture_output=True, text=True)
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result = subprocess.run(cmd, capture_output=True, text=True)
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# Handle min_score filtering in post-processing if needed
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min_score = args.get("min_score", 0.0)
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if min_score > 0.0 and result.returncode == 0:
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# Note: This is a basic implementation. For full support,
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# the CLI would need to return structured data for filtering
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pass
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elif tool_name == "leann_status":
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elif tool_name == "leann_status":
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if args.get("index_name"):
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if args.get("index_name"):
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# Check specific index status - for now, we'll use leann list and filter
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# Check specific index status - for now, we'll use leann list and filter
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@@ -319,39 +242,6 @@ This is your first step for any serious codebase work - think of it as giving yo
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# Show all indexes status
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# Show all indexes status
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result = subprocess.run(["leann", "list"], capture_output=True, text=True)
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result = subprocess.run(["leann", "list"], capture_output=True, text=True)
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elif tool_name == "leann_clear":
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index_name = args["index_name"]
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confirm = args.get("confirm", False)
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if not confirm:
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return {
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"jsonrpc": "2.0",
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"id": request.get("id"),
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"result": {
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"content": [
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{
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"type": "text",
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"text": f"Warning: This will permanently delete index '{index_name}'. To proceed, call this tool again with confirm=true.",
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}
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]
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},
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}
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# For clearing, we need to implement this in the CLI
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# For now, we'll return a message explaining the limitation
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return {
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"jsonrpc": "2.0",
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"id": request.get("id"),
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"result": {
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"content": [
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{
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"type": "text",
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"text": f"Clear functionality for index '{index_name}' is not yet implemented in CLI. You can manually delete the index files in .leann/indexes/{index_name}/",
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}
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]
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},
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}
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elif tool_name == "leann_list":
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elif tool_name == "leann_list":
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result = subprocess.run(["leann", "list"], capture_output=True, text=True)
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result = subprocess.run(["leann", "list"], capture_output=True, text=True)
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