Add LLM reasonning and MCP configuration

Signed-off-by: Herklos <herklos@drakkar.software>

Author: Herklos

CHANGELOG.md

@@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 - pydantic to requirements
 - add AbstractAIService
+- MCP server interface
 
 ## [1.7.2] - 2026-01-25
 ### Added
@@ -63,7 +64,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [1.6.21] - 2023-10-21
 ### Added
-[Constant] add CONIG_LLM_CUSTOM_BASE_URL
+[Constant] add CONFIG_LLM_CUSTOM_BASE_URL
 
 ## [1.6.20] - 2023-10-07
 ### Added

full_requirements.txt

@@ -42,6 +42,7 @@ flask-socketio==5.5.1
 openai==2.15.0
 # agents
 pydantic==2.12.5
+mcp==1.26.0
 # Coingecko
 coingecko-openapi-client>=1.3.0
 # Analysis tools

octobot_services/constants.py

@@ -83,10 +83,23 @@
 CONFIG_LLM_CUSTOM_BASE_URL = "llm-custom-base-url"
 CONFIG_LLM_MODEL = "llm-model"
 CONFIG_LLM_DAILY_TOKENS_LIMIT = "llm-daily-tokens-limit"
+CONFIG_LLM_SHOW_REASONING = "llm-show-reasoning"
+CONFIG_LLM_REASONING_EFFORT = "llm-reasoning-effort"
+CONFIG_LLM_MCP_SERVERS = "llm-mcp-servers"
+CONFIG_LLM_AUTO_INJECT_MCP_TOOLS = "llm-auto-inject-mcp-tools"
 ENV_OPENAI_SECRET_KEY = "OPENAI_SECRET_KEY"
 ENV_GPT_MODEL = "GPT_MODEL"
 ENV_GPT_DAILY_TOKENS_LIMIT = "GPT_DAILY_TOKEN_LIMIT"
 
+# MCP
+CONFIG_MCP = "mcp"
+CONFIG_MCP_IP = "ip"
+CONFIG_MCP_PORT = "port"
+ENV_MCP_PORT = "MCP_PORT"
+ENV_MCP_ADDRESS = "MCP_ADDRESS"
+DEFAULT_MCP_IP = '127.0.0.1'
+DEFAULT_MCP_PORT = 3001
+
 # Google
 CONFIG_GOOGLE = "google"
 CONFIG_TREND_TOPICS = "trends"

octobot_services/services/abstract_ai_service.py

@@ -38,7 +38,10 @@ async def get_completion(
         temperature: float = 0.5,
         json_output: bool = False,
         response_schema: typing.Optional[typing.Any] = None,
-    ) -> typing.Optional[str]:
+        tools: typing.Optional[list] = None,
+        tool_choice: typing.Optional[typing.Union[str, dict]] = None,
+        use_octobot_mcp: typing.Optional[bool] = None,
+    ) -> typing.Union[str, dict, None]:
         """
         Get a completion from the LLM.
         
@@ -52,9 +55,21 @@ async def get_completion(
             json_output: Whether to return JSON formatted output.
             response_schema: Optional Pydantic model or JSON schema dict 
                            for structured output validation.
+            tools: Optional list of tool definitions for function calling.
+                  Each tool should be a dict with 'type' and 'function' keys.
+            tool_choice: Optional control for tool usage. Can be "auto", "none", 
+                        or a dict specifying a specific tool.
+            use_octobot_mcp: Optional bool to include OctoBot MCP server tools.
+                            If True, automatically discovers and includes tools from OctoBot MCP interface.
+                            If None, uses default behavior (does not include OctoBot MCP).
+                            If False, explicitly excludes OctoBot MCP tools.
         
         Returns:
-            The completion text, or None if an error occurred.
+            str: The completion text when no tools are used or tool_choice is "none".
+            dict: When tools are used and model makes tool calls, returns dict with:
+                  - "content": str | None (may be None if only tool calls)
+                  - "tool_calls": list of tool call dicts with id, type, function keys
+            None: On error
         
         Raises:
             InvalidRequestError: If the request is malformed.