better format

graph/graph.py

@@ -16,8 +16,7 @@ class LocalGraph:
     # --- Creation ---
     def add_node(self, node_id: str | dict, **attrs):
         """
-        If a dict is passed instead of a plain string, build the node_id
+        Enhanced add_node that can return the generated node_id
-        as:  <lang>::<type>::<file>::<name>
         """
         if isinstance(node_id, dict):
             lang = node_id.get("lang", "unknown")
@@ -25,7 +24,15 @@ class LocalGraph:
             name = node_id.get("name", "unknown")
             fpath = node_id.get("file", "")
             node_id = f"{lang}::{kind}::{fpath}::{name}"
-        self.graph.add_node(node_id, **attrs)
+
+        # Merge any additional attributes
+        final_attrs = {}
+        if isinstance(node_id, dict):
+            final_attrs.update(node_id)
+        final_attrs.update(attrs)
+
+        self.graph.add_node(node_id, **final_attrs)
+        return node_id  # Return the ID for reference
 
     def _add_import_edges(self, src: str, imports: list[str]):
         for imp in imports:
@@ -213,3 +220,67 @@ class LocalGraph:
             except Exception:
                 # swallow; graph should remain usable
                 pass
+
+    def get_node_relationships(self, node_id: str) -> dict:
+        """
+        Get comprehensive relationships for a node in a structured format.
+        Returns: {
+            'outgoing': [(neighbor_id, edge_type, neighbor_data)],
+            'incoming': [(predecessor_id, edge_type, predecessor_data)],
+            'cross_file': list of cross-file relationships,
+            'same_file': list of same-file relationships
+        }
+        """
+        if node_id not in self.graph:
+            return {}
+
+        result = {
+            'outgoing': [],
+            'incoming': [],
+            'cross_file': [],
+            'same_file': []
+        }
+
+        node_file = self.graph.nodes[node_id].get('file', '')
+
+        # Outgoing relationships
+        for neighbor_id in self.graph.successors(node_id):
+            edge_data = self.graph.edges[node_id, neighbor_id]
+            neighbor_data = self.graph.nodes[neighbor_id]
+            edge_type = edge_data.get('type', 'related')
+
+            result['outgoing'].append((neighbor_id, edge_type, neighbor_data))
+
+            # Cross-file classification
+            neighbor_file = neighbor_data.get('file', '')
+            rel_info = f"{edge_type}:{neighbor_data.get('type', '')}:{neighbor_data.get('name', '')}"
+
+            if neighbor_file and neighbor_file != node_file:
+                result['cross_file'].append(f"{rel_info}@{neighbor_file}")
+            else:
+                result['same_file'].append(rel_info)
+
+        # Incoming relationships
+        for predecessor_id in self.graph.predecessors(node_id):
+            edge_data = self.graph.edges[predecessor_id, node_id]
+            predecessor_data = self.graph.nodes[predecessor_id]
+            edge_type = edge_data.get('type', 'related')
+
+            result['incoming'].append((predecessor_id, edge_type, predecessor_data))
+
+        return result
+
+    def get_file_entities(self, file_path: str) -> list:
+        """Get all entities in a file in compact format."""
+        file_node_id = f"file::{file_path}"
+        if file_node_id not in self.graph:
+            return []
+
+        entities = []
+        for neighbor_id, neighbor_data in self.neighbors(file_node_id):
+            entity_type = neighbor_data.get('type', '')
+            entity_name = neighbor_data.get('name', '')
+            if entity_type and entity_name:
+                entities.append(f"{entity_type}:{entity_name}")
+
+        return entities

mcp_codebase.py

@@ -5007,93 +5007,174 @@ def _build_enhanced_result(chunk_text: str, meta: dict, score: float, graph: Opt
 
 
 def _get_result_graph_context(meta: dict, graph: Optional[LocalGraph] = None) -> dict:
-    """Get MOST relevant graph context - optimized for LLM tokens."""
+    """Ultra-compact graph context using LocalGraph helper methods."""
-    if graph is None or graph.graph.number_of_nodes() == 0:
+    if graph is None:
         return {}
 
-    context = {}
     file_path = meta.get('file', '')
     entity_name = meta.get('name', '')
     entity_type = meta.get('type', '')
     language = meta.get('language', '')
 
-    # Build node ID
+    # Build node ID using graph's method
-    node_id = None
+    node_attrs = {"lang": language, "type": entity_type, "file": file_path, "name": entity_name}
-    if entity_name and file_path and language:
+    node_id = graph.add_node(node_attrs, return_id=True)
-        if entity_type in ['class', 'struct', 'interface', 'enum', 'impl']:
-            node_id = f"{language}::{entity_type}::{file_path}::{entity_name}"
-        elif entity_type in ['method', 'function', 'constructor']:
-            class_name = meta.get('class')
-            if class_name:
-                node_id = f"{language}::{entity_type}::{file_path}::{class_name}.{entity_name}"
-            else:
-                node_id = f"{language}::{entity_type}::{file_path}::{entity_name}"
-        elif entity_type == 'component' and language == 'svelte':
-            node_id = f"svelte::component::{file_path}::{entity_name}"
 
-    if node_id and node_id in graph.graph:
+    if node_id not in graph.graph:
-        # PRIORITY 1: Cross-file dependencies (highest value for LLM)
+        return {}
-        cross_file_rels = []
-        incoming_cross_file = []
 
-        # Outgoing cross-file relationships
+    # Get all relationships in one call
-        for neighbor_id, neighbor_data in graph.neighbors(node_id):
+    relationships = graph.get_node_relationships(node_id)
-            neighbor_file = neighbor_data.get('file', '')
+    context = {}
-            if neighbor_file and neighbor_file != file_path:
-                edge_data = graph.graph.edges[node_id, neighbor_id]
-                rel_type = edge_data.get('type', 'related')
-                neighbor_name = neighbor_data.get('name', neighbor_id.split('::')[-1])
-                neighbor_type = neighbor_data.get('type', '')
 
-                # Compress representation based on relationship importance
+    if relationships.get('cross_file'):
-                if rel_type in ['extends', 'implements', 'calls']:
+        context['cross_file'] = relationships['cross_file']
-                    # High-value relationships - keep full info
+    if relationships.get('same_file'):
-                    cross_file_rels.append(f"{rel_type}:{neighbor_type}:{neighbor_name}@{neighbor_file}")
+        context['same_file'] = relationships['same_file']
-                else:
-                    # Medium-value - compress type
-                    cross_file_rels.append(f"{rel_type}:{neighbor_name}@{neighbor_file}")
 
-        # PRIORITY 2: Reverse dependencies (what uses this entity)
+    # Build reverse dependencies from incoming relationships
-        for predecessor in graph.graph.predecessors(node_id):
+    if relationships.get('incoming'):
-            pred_data = graph.graph.nodes[predecessor]
+        reverse_deps = []
+        node_file = graph.graph.nodes[node_id].get('file', '')
+        for pred_id, edge_type, pred_data in relationships['incoming']:
             pred_file = pred_data.get('file', '')
             pred_name = pred_data.get('name', '')
+            pred_type = pred_data.get('type', '')
 
-            # Only show cross-file incoming (same-file is less valuable)
+            if pred_file and pred_file != node_file:
-            if pred_file and pred_file != file_path:
+                reverse_deps.append(f"{edge_type}:{pred_type}:{pred_name}@{pred_file}")
-                edge_data = graph.graph.edges[predecessor, node_id]
+            else:
-                rel_type = edge_data.get('type', 'related')
+                reverse_deps.append(f"{edge_type}:{pred_type}:{pred_name}")
 
-                if rel_type in ['calls', 'extends', 'implements']:
+        if reverse_deps:
-                    incoming_cross_file.append(f"{rel_type}:{pred_name}@{pred_file}")
+            context['used_by'] = reverse_deps
-
-        # Apply aggressive limits for token conservation
-        if cross_file_rels:
-            # Sort by relationship importance
-            priority_order = {'extends': 0, 'implements': 1, 'calls': 2}
-            cross_file_rels.sort(key=lambda x: priority_order.get(x.split(':')[0], 99))
-            context['xfile'] = cross_file_rels[:6]  # Only top 6 cross-file deps
-
-        if incoming_cross_file:
-            context['used_by'] = incoming_cross_file[:4]  # Only top 4 reverse deps
-
-        # PRIORITY 3: Critical same-file context (only if cross-file is sparse)
-        if not context.get('xfile') and not context.get('used_by'):
-            file_node_id = f"file::{file_path}"
-            if file_node_id in graph.graph:
-                file_entities = []
-                for neighbor_id, neighbor_data in graph.neighbors(file_node_id):
-                    neighbor_type = neighbor_data.get('type', '')
-                    neighbor_name = neighbor_data.get('name', '')
-                    if (neighbor_type in ['class', 'struct', 'interface', 'component'] and
-                        neighbor_name != entity_name):
-                        file_entities.append(f"{neighbor_type}:{neighbor_name}")
 
+    # Get file entities
+    file_entities = graph.get_file_entities(file_path)
     if file_entities:
-                    context['file'] = file_entities[:3]  # Only top 3 same-file entities
+        context['file_contents'] = file_entities
 
     return context
 
+def _format_enhanced_results(results: List[Dict], query: str, result_type: str) -> str:
+    """Comprehensive format with ALL information preserved - optimized for LLM analysis."""
+    if not results:
+        return f"# {result_type.title()}: {query}\nNo results found.\n"
+
+    lines = [
+        f"# {result_type.title()}: {query}",
+        f"results_count={len(results)}"
+    ]
+
+    for i, result in enumerate(results, 1):
+        # Extract ALL metadata
+        file_path = result.get('file', '')
+        line_num = result.get('line', '')
+        entity_type = result.get('type', 'code')
+        entity_name = result.get('name', '')
+        language = result.get('language', '')
+        score = result.get('score', 0.0)
+        content = result.get('content', '')  # NO TRUNCATION - let LLM handle it
+        docstring = result.get('docstring', '')
+
+        graph_context = result.get('graph_context', {})
+
+        # Build comprehensive result block
+        lines.append(f"\n--- RESULT {i} ---")
+        lines.append(f"file:{file_path}:{line_num}")
+        lines.append(f"entity:{entity_type}:{entity_name}")
+        lines.append(f"language:{language}")
+        lines.append(f"score:{score:.3f}")
+
+        # Content (FULL, no truncation)
+        if content:
+            lines.append(f"content: {content}")
+
+        # Docstring if available
+        if docstring:
+            lines.append(f"doc: {docstring}")
+
+        # Graph context - PRESERVE EVERYTHING
+        if graph_context:
+            lines.append("graph_context:")
+
+            # Cross-file dependencies (highest value)
+            if graph_context.get('cross_file'):
+                lines.append(f"  cross_file: {' | '.join(graph_context['cross_file'])}")
+
+            # Reverse dependencies
+            if graph_context.get('used_by'):
+                lines.append(f"  used_by: {' | '.join(graph_context['used_by'])}")
+
+            # Same-file relationships
+            if graph_context.get('same_file'):
+                lines.append(f"  same_file_rels: {' | '.join(graph_context['same_file'])}")
+
+            # All relationships (comprehensive)
+            if graph_context.get('all_rels'):
+                lines.append(f"  all_relationships: {' | '.join(graph_context['all_rels'][:15])}")  # Reasonable limit
+
+            # File contents
+            if graph_context.get('file_contents'):
+                lines.append(f"  file_entities: {' | '.join(graph_context['file_contents'])}")
+
+    return "\n".join(lines)
+
+
+def _extract_clean_content(chunk_text: str) -> str:
+    """Extract clean content WITHOUT truncation - preserve full context."""
+    lines = chunk_text.split('\n')
+    content_lines = []
+
+    # Skip only the pure metadata header lines
+    skip_metadata = True
+    for line in lines:
+        stripped = line.strip()
+        if skip_metadata:
+            # Only skip actual metadata labels, not content
+            if any(stripped.startswith(prefix) for prefix in ['File:', 'Type:', 'Name:', 'Language:', 'Doc:']):
+                continue
+            # Stop skipping when we hit actual code/content
+            if stripped and not stripped.startswith(('File:', 'Type:', 'Name:', 'Language:', 'Doc:')):
+                skip_metadata = False
+                content_lines.append(stripped)
+        else:
+            content_lines.append(stripped)
+
+    # Join with spaces but preserve ALL content
+    content = ' '.join(content_lines)
+    content = re.sub(r'\s+', ' ', content)  # Only normalize whitespace
+    return content.strip()
+
+
+def _build_enhanced_result(chunk_text: str, meta: dict, score: float, graph: Optional[LocalGraph] = None) -> dict:
+    """Build comprehensive result with ALL context preserved."""
+    # Extract key metadata
+    file_path = meta.get('file', '')
+    line_num = meta.get('line', '')
+    entity_type = meta.get('type', 'code')
+    language = meta.get('language', '')
+    entity_name = meta.get('name', '')
+
+    # Extract FULL content and docstring
+    clean_content = _extract_clean_content(chunk_text)
+    docstring = _extract_docstring(chunk_text)
+
+    # Get COMPREHENSIVE graph context
+    graph_context = _get_result_graph_context(meta, graph)
+
+    return {
+        'file': file_path,
+        'line': line_num,
+        'type': entity_type,
+        'language': language,
+        'name': entity_name,
+        'score': score,
+        'content': clean_content,  # NO TRUNCATION
+        'docstring': docstring,
+        'graph_context': graph_context
+    }
+
 def _extract_docstring(chunk_text: str) -> str:
     """Extract docstring or comments from chunk text."""
     lines = chunk_text.split('\n')
@@ -5132,94 +5213,95 @@ def _extract_docstring(chunk_text: str) -> str:
 
 
 def _format_enhanced_results(results: List[Dict], query: str, result_type: str) -> str:
-    """Ultra-compact formatting optimized for LLM token usage."""
+    """Comprehensive format with ALL information preserved - optimized for LLM analysis."""
     if not results:
         return f"# {result_type.title()}: {query}\nNo results found.\n"
 
     lines = [
         f"# {result_type.title()}: {query}",
-        f"Found {len(results)} results:"
+        f"results_count={len(results)}"
     ]
 
     for i, result in enumerate(results, 1):
-        # Extract core info
+        # Extract ALL metadata
         file_path = result.get('file', '')
         line_num = result.get('line', '')
         entity_type = result.get('type', 'code')
         entity_name = result.get('name', '')
+        language = result.get('language', '')
         score = result.get('score', 0.0)
-        content = result.get('content', '')[:350]  # Strict content limit
+        content = result.get('content', '')  # NO TRUNCATION - let LLM handle it
+        docstring = result.get('docstring', '')
 
         graph_context = result.get('graph_context', {})
 
-        # Build ultra-compact context string
+        # Build comprehensive result block
-        context_parts = []
+        lines.append(f"\n--- RESULT {i} ---")
+        lines.append(f"file:{file_path}:{line_num}")
+        lines.append(f"entity:{entity_type}:{entity_name}")
+        lines.append(f"language:{language}")
+        lines.append(f"score:{score:.3f}")
 
-        # Cross-file deps (highest priority)
+        # Content (FULL, no truncation)
-        if graph_context.get('xfile'):
+        if content:
-            # Compress further: remove file paths, keep only key info
+            lines.append(f"content: {content}")
-            compressed_deps = []
-            for dep in graph_context['xfile'][:4]:  # Only top 4
-                parts = dep.split('@')[0]  # Remove file path
-                compressed_deps.append(parts)
-            context_parts.append(f"deps:{'|'.join(compressed_deps)}")
 
-        # Reverse deps
+        # Docstring if available
+        if docstring:
+            lines.append(f"doc: {docstring}")
+
+        # Graph context - PRESERVE EVERYTHING
+        if graph_context:
+            lines.append("graph_context:")
+
+            # Cross-file dependencies (highest value)
+            if graph_context.get('cross_file'):
+                lines.append(f"  cross_file: {' | '.join(graph_context['cross_file'])}")
+
+            # Reverse dependencies
             if graph_context.get('used_by'):
-            compressed_used = []
+                lines.append(f"  used_by: {' | '.join(graph_context['used_by'])}")
-            for used in graph_context['used_by'][:3]:  # Only top 3
-                parts = used.split('@')[0]  # Remove file path
-                compressed_used.append(parts)
-            context_parts.append(f"used:{'|'.join(compressed_used)}")
 
-        # Same-file context (lowest priority)
+            # Same-file relationships
-        if graph_context.get('file') and not context_parts:
+            if graph_context.get('same_file'):
-            context_parts.append(f"file:{'|'.join(graph_context['file'][:2])}")
+                lines.append(f"  same_file_rels: {' | '.join(graph_context['same_file'])}")
 
-        context_str = " ".join(context_parts)
+            # All relationships (comprehensive)
+            if graph_context.get('all_rels'):
+                lines.append(f"  all_relationships: {' | '.join(graph_context['all_rels'][:15])}")  # Reasonable limit
 
-        # Ultra-compact row format
+            # File contents
-        row = f"{i}. {file_path}:{line_num} {entity_type}:{entity_name} (score:{score:.2f})"
+            if graph_context.get('file_contents'):
-        if context_str:
+                lines.append(f"  file_entities: {' | '.join(graph_context['file_contents'])}")
-            row += f" [{context_str}]"
-
-        lines.append(row)
-        lines.append(f"   Content: {content}")
-
-        # Add docstring only if highly relevant and short
-        docstring = result.get('docstring', '')
-        if docstring and len(docstring) < 100 and any(keyword in query.lower() for keyword in ['how', 'what', 'why', 'documentation']):
-            lines.append(f"   Docs: {docstring}")
-
-        lines.append("")  # Empty line between results
 
     return "\n".join(lines)
 
+
 def _extract_clean_content(chunk_text: str) -> str:
-    """Extract clean content from chunk text by removing duplicate metadata."""
+    """Extract clean content WITHOUT truncation - preserve full context."""
     lines = chunk_text.split('\n')
     content_lines = []
 
-    # Skip the first few metadata lines (File:, Type:, Name:, etc.)
+    # Skip only the pure metadata header lines
     skip_metadata = True
     for line in lines:
         stripped = line.strip()
         if skip_metadata:
+            # Only skip actual metadata labels, not content
             if any(stripped.startswith(prefix) for prefix in ['File:', 'Type:', 'Name:', 'Language:', 'Doc:']):
                 continue
-            # Once we hit actual content, stop skipping
+            # Stop skipping when we hit actual code/content
-            if stripped and not any(stripped.startswith(prefix) for prefix in ['File:', 'Type:', 'Name:', 'Language:', 'Doc:', 'SQL:', 'Code:']):
+            if stripped and not stripped.startswith(('File:', 'Type:', 'Name:', 'Language:', 'Doc:')):
                 skip_metadata = False
                 content_lines.append(stripped)
         else:
             content_lines.append(stripped)
 
-    # Join and clean up
+    # Join with spaces but preserve ALL content
     content = ' '.join(content_lines)
-    content = re.sub(r'\s+', ' ', content)  # Normalize whitespace
+    content = re.sub(r'\s+', ' ', content)  # Only normalize whitespace
     return content.strip()
 
-
 @mcp.tool()
 def find_code_references(symbol: str, top_k: int = 20) -> str:
     """What it does - Fast, unranked lookup of all file/line occurrences of a symbol.