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AgentWong
by AgentWong

update_entity

Modify existing Infrastructure-as-Code entity properties and append new observations to maintain accurate version tracking and relationship mapping.

Instructions

Update an existing entity's properties and add new observations

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesEntity ID
nameNoNew name
typeNoNew type
observationNoNew observation

Implementation Reference

  • The main MCP tool handler for 'update_entity' that logs the operation, delegates to execute_update_entity, and handles errors by raising McpError.
    async def handle_update_entity(db: Any, arguments: Dict[str, Any], operation_id: str) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
        """Handle update_entity tool."""
        try:
            logger.info(
                "Updating entity",
                extra={
                    "entity_id": arguments.get("entity_id"),
                    "operation_id": operation_id,
                },
            )
    
            # Execute update
            return execute_update_entity(db, arguments)
    
        except Exception as e:
            error_msg = f"Failed to update entity: {str(e)}"
            logger.error(error_msg, extra={"operation_id": operation_id})
            raise McpError(
                types.ErrorData(
                    code=types.INTERNAL_ERROR,
                    message=error_msg,
                    data={
                        "tool": "update_entity",
                        "operation_id": operation_id,
                    },
                )
            )
  • JSON schema definition for the 'update_entity' tool inputs, defining required 'id' and optional properties like name, type, observation.
    "update_entity": {
        "type": "object",
        "description": "Update an existing entity's properties and add new observations",
        "required": ["id"],
        "properties": {
            "id": {"type": "string", "description": "Entity ID"},
            "name": {"type": "string", "description": "New name"},
            "type": {"type": "string", "description": "New type"},
            "observation": {"type": "string", "description": "New observation"},
        },
    },
  • Registration of the 'update_entity' handler in the entity_tool_handlers dictionary used for tool dispatching.
    entity_tool_handlers = {
        "create_entity": handle_create_entity,
        "update_entity": handle_update_entity,
        "delete_entity": handle_delete_entity,
        "view_relationships": handle_view_relationships,
    }
  • Helper function that orchestrates the entity update: prepares updates dict, calls core update_entity, adds observation if present, returns MCP TextContent response.
    def execute_update_entity(
        db: DatabaseManager, arguments: Dict[str, Any]
    ) -> List[TextContent]:
        """Execute update entity operation."""
        logger.info("Updating entity", extra={"args": arguments})
    
        updates = {k: v for k, v in arguments.items() if k != "id"}
        success = update_entity(db, arguments["id"], updates)
        if not success:
            raise DatabaseError(f"Entity not found: {arguments['id']}")
    
        # Add observation if provided
        if "observation" in arguments:
            with db.get_connection() as conn:
                conn.execute(
                    "INSERT INTO observations (entity_id, content) VALUES (?, ?)",
                    (arguments["id"], arguments["observation"]),
                )
    
        return [TextContent(type="text", text=f"Updated entity {arguments['id']}")]
  • Core database update function that dynamically builds and executes SQL UPDATE query on entities table.
    def update_entity(db: DatabaseManager, entity_id: str, updates: Dict) -> bool:
        """Update an existing entity."""
        set_clause = ", ".join(f"{k} = ?" for k in updates.keys())
        values = tuple(updates.values()) + (entity_id,)
        query = f"UPDATE entities SET {set_clause} WHERE id = ?"
        try:
            with db.get_connection() as conn:
                cursor = conn.execute(query, values)
                return cursor.rowcount > 0
        except sqlite3.Error as e:
            raise DatabaseError(f"Update failed: {str(e)}")
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states the action ('update' and 'add') without disclosing behavioral traits like permission requirements, whether updates are reversible, how conflicts are handled, or what the response includes. This is inadequate for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action ('update an existing entity's properties and add new observations') with zero wasted words. Every part earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover error conditions, response format, or important behavioral aspects like idempotency or side effects, leaving significant gaps for agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters. The description adds no additional meaning beyond what's in the schema, such as explaining how 'observation' relates to 'properties' or format details. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('update') and resource ('existing entity'), and specifies what gets updated ('properties') and added ('new observations'). However, it doesn't explicitly differentiate from sibling tools like 'create_entity' or 'delete_entity' beyond the 'existing' qualifier.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'create_entity' for new entities or 'view_relationships' for related operations. It mentions 'existing entity' but doesn't clarify prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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