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Fahad
2025-06-16 07:18:45 +04:00
parent 4c0bd3b86d
commit 0b94dd8cdd
2 changed files with 19 additions and 19 deletions
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26
server.py
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@@ -364,8 +364,8 @@ async def handle_call_tool(name: str, arguments: dict[str, Any]) -> list[TextCon
"""
Handle incoming tool execution requests from MCP clients.
This is the main request dispatcher that routes tool calls to their appropriate handlers.
It supports both AI-powered tools (from TOOLS registry) and utility tools (implemented as
This is the main request dispatcher that routes tool calls to their appropriate handlers.
It supports both AI-powered tools (from TOOLS registry) and utility tools (implemented as
static functions).
CONVERSATION LIFECYCLE MANAGEMENT:
@@ -373,15 +373,15 @@ async def handle_call_tool(name: str, arguments: dict[str, Any]) -> list[TextCon
1. THREAD RESUMPTION: When continuation_id is present, it reconstructs complete conversation
context from Redis including conversation history and file references
2. CROSS-TOOL CONTINUATION: Enables seamless handoffs between different tools (analyze →
2. CROSS-TOOL CONTINUATION: Enables seamless handoffs between different tools (analyze →
codereview → debug) while preserving full conversation context and file references
3. CONTEXT INJECTION: Reconstructed conversation history is embedded into tool prompts
using the dual prioritization strategy:
- Files: Newest-first prioritization (recent file versions take precedence)
- Turns: Newest-first collection for token efficiency, chronological presentation for LLM
4. FOLLOW-UP GENERATION: After tool execution, generates continuation offers for ongoing
AI-to-AI collaboration with natural language instructions
@@ -531,36 +531,36 @@ async def reconstruct_thread_context(arguments: dict[str, Any]) -> dict[str, Any
"""
Reconstruct conversation context for stateless-to-stateful thread continuation.
This is a critical function that transforms the inherently stateless MCP protocol into
This is a critical function that transforms the inherently stateless MCP protocol into
stateful multi-turn conversations. It loads persistent conversation state from Redis
and rebuilds complete conversation context using the sophisticated dual prioritization
strategy implemented in the conversation memory system.
CONTEXT RECONSTRUCTION PROCESS:
1. THREAD RETRIEVAL: Loads complete ThreadContext from Redis using continuation_id
- Includes all conversation turns with tool attribution
- Preserves file references and cross-tool context
- Handles conversation chains across multiple linked threads
2. CONVERSATION HISTORY BUILDING: Uses build_conversation_history() to create
comprehensive context with intelligent prioritization:
FILE PRIORITIZATION (Newest-First Throughout):
- When same file appears in multiple turns, newest reference wins
- File embedding prioritizes recent versions, excludes older duplicates
- Token budget management ensures most relevant files are preserved
CONVERSATION TURN PRIORITIZATION (Dual Strategy):
- Collection Phase: Processes turns newest-to-oldest for token efficiency
- Presentation Phase: Presents turns chronologically for LLM understanding
- Ensures recent context is preserved when token budget is constrained
3. CONTEXT INJECTION: Embeds reconstructed history into tool request arguments
- Conversation history becomes part of the tool's prompt context
- Files referenced in previous turns are accessible to current tool
- Cross-tool knowledge transfer is seamless and comprehensive
4. TOKEN BUDGET MANAGEMENT: Applies model-specific token allocation
- Balances conversation history vs. file content vs. response space
- Gracefully handles token limits with intelligent exclusion strategies

12
utils/conversation_memory.py
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@@ -40,8 +40,8 @@ Key Features:
- Graceful degradation when Redis is unavailable
DUAL PRIORITIZATION STRATEGY (Files & Conversations):
The conversation memory system implements sophisticated prioritization for both files and
conversation turns, using a consistent "newest-first" approach during collection but
The conversation memory system implements sophisticated prioritization for both files and
conversation turns, using a consistent "newest-first" approach during collection but
presenting information in the optimal format for LLM consumption:
FILE PRIORITIZATION (Newest-First Throughout):
@@ -64,7 +64,7 @@ CONVERSATION TURN PRIORITIZATION (Newest-First Collection, Chronological Present
- LLM sees natural conversation flow: "Turn 1 → Turn 2 → Turn 3..."
- Maintains proper sequential understanding while preserving recency prioritization
This dual approach ensures optimal context preservation (newest-first) with natural
This dual approach ensures optimal context preservation (newest-first) with natural
conversation flow (chronological) for maximum LLM comprehension and relevance.
USAGE EXAMPLE:
@@ -568,19 +568,19 @@ def build_conversation_history(context: ThreadContext, model_context=None, read_
CONVERSATION TURN ORDERING STRATEGY:
The function employs a sophisticated two-phase approach for optimal token utilization:
PHASE 1 - COLLECTION (Newest-First for Token Budget):
- Processes conversation turns in REVERSE chronological order (newest to oldest)
- Prioritizes recent turns within token constraints
- If token budget is exceeded, OLDER turns are excluded first
- Ensures the most contextually relevant recent exchanges are preserved
PHASE 2 - PRESENTATION (Chronological for LLM Understanding):
- Reverses the collected turns back to chronological order (oldest to newest)
- Presents conversation flow naturally for LLM comprehension
- Maintains "--- Turn 1, Turn 2, Turn 3..." sequential numbering
- Enables LLM to follow conversation progression logically
This approach balances recency prioritization with natural conversation flow.
TOKEN MANAGEMENT: