Todo AI MCP Agent

Build AI agents that manage todos through natural language while maintaining safety and reliability.

Core Principles

•No hallucinated actions - Only act on data from MCP tool results
•Database-backed memory - Never cache state; always fetch fresh
•Clear tool invocation - Every action maps to explicit MCP tool call
•Stateless server - Session context only; no persistent agent state

Agent Flow

code

User Input
    │
    ▼
┌─────────────────┐
│ Parse Intent    │ ← "Add buy milk" → intent: create, title: "buy milk"
└─────────────────┘
    │
    ▼
┌─────────────────┐
│ Validate/Clarify│ ← Ambiguous? Ask user
└─────────────────┘
    │
    ▼
┌─────────────────┐
│ Invoke MCP Tool │ ← todos.create({title: "buy milk"})
└─────────────────┘
    │
    ▼
┌─────────────────┐
│ Format Response │ ← "Created 'buy milk'"
└─────────────────┘

Intent to Tool Mapping

User Says	Intent	MCP Tool Call
"Add buy groceries"	create	`todos.create({title: "buy groceries"})`
"Show my tasks"	list	`todos.list({})`
"Mark task 3 as done"	complete	`todos.update({id: 3, status: "completed"})`
"Delete the milk task"	delete	`todos.list({search: "milk"})` → `todos.delete({id: X})`
"What's urgent?"	filter	`todos.list({priority: "high"})`

MCP Tools

todos.list

json

{
  "status": "pending|completed|archived",
  "priority": "low|medium|high",
  "search": "text to match",
  "limit": 20
}

todos.create

json

{
  "title": "required string",
  "description": "optional",
  "priority": "low|medium|high",
  "due_date": "ISO 8601 datetime"
}

todos.update

json

{
  "id": "required integer",
  "title": "optional new title",
  "status": "pending|completed|archived",
  "priority": "low|medium|high"
}

todos.delete

json

{
  "id": "required integer"
}

Safety Rules

Never Invent Data

python

# WRONG - Making up todos
"You have: buy milk, call mom"  # Hallucinated!

# RIGHT - Use tool result
result = await mcp.call_tool("todos.list", {})
response = format_todos(result)  # From actual data

Always Verify Before Acting

python

# WRONG - Assuming ID
await mcp.call_tool("todos.delete", {"id": 1})

# RIGHT - Search first
todos = await mcp.call_tool("todos.list", {"search": "milk"})
if len(todos) == 1:
    await mcp.call_tool("todos.delete", {"id": todos[0]["id"]})
elif len(todos) > 1:
    ask_user_to_clarify(todos)
else:
    report_not_found()

Confirm Destructive Actions

python

NEEDS_CONFIRMATION = ["delete", "bulk_delete", "clear_all"]

if action in NEEDS_CONFIRMATION:
    return f"Delete '{todo.title}'? This cannot be undone. (yes/no)"

Handling Ambiguity

Reference Resolution

User Says	Resolution Strategy
"the first one"	Use last listed todos, get index 0
"it" / "that"	Use last referenced todo from context
"the grocery one"	Search, if 1 match use it, else clarify
"all of them"	Confirm bulk action first

Clarification Pattern

python

if matches == 0:
    "I couldn't find a todo matching 'X'. Show your list?"
elif matches > 1:
    "Found {n} todos matching 'X'. Which one?\n{list}"
else:
    proceed_with_action()

Response Patterns

Successful Actions

code

Created: "Created 'buy milk'"
Listed:  "You have 3 todos:\n- buy milk\n- call mom\n- finish report"
Updated: "Marked 'buy milk' as complete"
Deleted: "Deleted 'buy milk'"

Errors

code

Not found:  "I couldn't find that todo. Would you like to see your list?"
Validation: "Title can't be empty. What should I call this todo?"
Conflict:   "That todo was just modified. Current status: completed"

Session Context (Non-Persistent)

python

class SessionContext:
    last_listed_todos: list[Todo]  # For "the first one"
    last_referenced_todo: Todo     # For "it" / "that"
    pending_confirmation: Action   # Awaiting yes/no

# Lives only in session memory
# NOT stored in database
# Expires with conversation

Multi-Action Handling

User: "Add buy milk and complete the groceries task"

python

# Parse multiple intents
intents = [
    {"action": "create", "title": "buy milk"},
    {"action": "complete", "search": "groceries"},
]

# Execute sequentially
results = []
for intent in intents:
    result = await execute_intent(intent)
    results.append(result)

# Compose response
"Created 'buy milk' and completed 'groceries'"

References

•references/intent-mapping.md - Intent classification, entity extraction, disambiguation
•references/mcp-tools.md - Tool schemas, invocation patterns, error handling
•references/safety-patterns.md - Hallucination prevention, confirmation flows, stateless design