diff --git a/cognee-mcp/src/codingagents/__init__.py b/cognee-mcp/src/codingagents/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/cognee-mcp/src/codingagents/coding_rule_associations.py b/cognee-mcp/src/codingagents/coding_rule_associations.py
deleted file mode 100644
index c860cb9f1..000000000
--- a/cognee-mcp/src/codingagents/coding_rule_associations.py
+++ /dev/null
@@ -1,120 +0,0 @@
-from uuid import NAMESPACE_OID, uuid5
-
-from cognee.infrastructure.databases.graph import get_graph_engine
-from cognee.infrastructure.databases.vector import get_vector_engine
-
-from cognee.low_level import DataPoint
-from cognee.infrastructure.llm import LLMGateway
-from cognee.shared.logging_utils import get_logger
-from cognee.modules.engine.models import NodeSet
-from cognee.tasks.storage import add_data_points, index_graph_edges
-from typing import Optional, List, Any
-from pydantic import Field
-
-logger = get_logger("coding_rule_association")
-
-
-class Rule(DataPoint):
-    """A single developer rule extracted from text."""
-
-    text: str = Field(..., description="The coding rule associated with the conversation")
-    belongs_to_set: Optional[NodeSet] = None
-    metadata: dict = {"index_fields": ["rule"]}
-
-
-class RuleSet(DataPoint):
-    """Collection of parsed rules."""
-
-    rules: List[Rule] = Field(
-        ...,
-        description="List of developer rules extracted from the input text. Each rule represents a coding best practice or guideline.",
-    )
-
-
-async def get_existing_rules(rules_nodeset_name: str) -> str:
-    graph_engine = await get_graph_engine()
-    nodes_data, _ = await graph_engine.get_nodeset_subgraph(
-        node_type=NodeSet, node_name=[rules_nodeset_name]
-    )
-
-    existing_rules = [
-        item[1]["text"]
-        for item in nodes_data
-        if isinstance(item, tuple)
-        and len(item) == 2
-        and isinstance(item[1], dict)
-        and "text" in item[1]
-    ]
-
-    existing_rules = "\n".join(f"- {rule}" for rule in existing_rules)
-
-    return existing_rules
-
-
-async def get_origin_edges(data: str, rules: List[Rule]) -> list[Any]:
-    vector_engine = get_vector_engine()
-
-    origin_chunk = await vector_engine.search("DocumentChunk_text", data, limit=1)
-
-    try:
-        origin_id = origin_chunk[0].id
-    except (AttributeError, KeyError, TypeError, IndexError):
-        origin_id = None
-
-    relationships = []
-
-    if origin_id and isinstance(rules, (list, tuple)) and len(rules) > 0:
-        for rule in rules:
-            try:
-                rule_id = getattr(rule, "id", None)
-                if rule_id is not None:
-                    rel_name = "rule_associated_from"
-                    relationships.append(
-                        (
-                            rule_id,
-                            origin_id,
-                            rel_name,
-                            {
-                                "relationship_name": rel_name,
-                                "source_node_id": rule_id,
-                                "target_node_id": origin_id,
-                                "ontology_valid": False,
-                            },
-                        )
-                    )
-            except Exception as e:
-                logger.info(f"Warning: Skipping invalid rule due to error: {e}")
-    else:
-        logger.info("No valid origin_id or rules provided.")
-
-    return relationships
-
-
-async def add_rule_associations(data: str, rules_nodeset_name: str):
-    graph_engine = await get_graph_engine()
-    existing_rules = await get_existing_rules(rules_nodeset_name=rules_nodeset_name)
-
-    user_context = {"chat": data, "rules": existing_rules}
-
-    user_prompt = LLMGateway.render_prompt(
-        "coding_rule_association_agent_user.txt", context=user_context
-    )
-    system_prompt = LLMGateway.render_prompt("coding_rule_association_agent_system.txt", context={})
-
-    rule_list = await LLMGateway.acreate_structured_output(
-        text_input=user_prompt, system_prompt=system_prompt, response_model=RuleSet
-    )
-
-    rules_nodeset = NodeSet(
-        id=uuid5(NAMESPACE_OID, name=rules_nodeset_name), name=rules_nodeset_name
-    )
-    for rule in rule_list.rules:
-        rule.belongs_to_set = rules_nodeset
-
-    edges_to_save = await get_origin_edges(data=data, rules=rule_list.rules)
-
-    await add_data_points(data_points=rule_list.rules)
-
-    if len(edges_to_save) > 0:
-        await graph_engine.add_edges(edges_to_save)
-        await index_graph_edges(edges_to_save)