MCP Tools Development Skill

This skill guides you through creating and modifying MCP tools for the AI Debugger (AIDB). MCP tools are the primary interface between AI agents and the debugging system - they must be fast, accurate, and clear.

Core Philosophy: Agent Optimization

MCP tools are user-facing products. Every response is read by an AI agent with limited context. Your goal:

1. •Accuracy: Responses must contain correct information (line numbers, values, state)
2. •Clarity: Summaries must be clear and actionable, not verbose
3. •Speed: Operations must be fast (agents iterate rapidly)
4. •No Junk: No unnecessary fields, no bloated payloads, no redundant data

Context windows are sacred - every token matters.

Quick Start

When to Use This Skill

Invoke this skill when:

• •Creating a new MCP tool (e.g., aidb.watch for watchpoints)
• •Modifying existing tool behavior
• •Fixing MCP response issues
• •Optimizing tool performance
• •Debugging tool handler errors

Related Skills

When developing MCP tools, you may also need:

• •dap-protocol-guide - MCP tools use DAP types for all debugging operations
• •testing-strategy - MCP tools tested with DebugInterface for content accuracy
• •code-reuse-enforcement - Must use existing constants/enums from core package

MCP Architecture Overview

For complete MCP server architecture, see:

• •docs/developer-guide/overview.md - System architecture showing MCP layer integration
• •src/aidb_mcp/ - MCP server implementation

User Request
    ↓
MCP Server (src/aidb_mcp/server/)
    ↓
Handler Registry (src/aidb_mcp/handlers/registry.py)
    ↓
Tool Handler (src/aidb_mcp/handlers/{category}/)
    ↓
DebugService (src/aidb/service/)
    ↓
Response Builder (src/aidb_mcp/responses/)
    ↓
MCP Response

Key Components:

• •Tool Definitions (src/aidb_mcp/tools/definitions.py): Tool schemas and metadata
• •Handler Registry (src/aidb_mcp/handlers/registry.py): Dispatcher to handlers
• •Tool Handlers (src/aidb_mcp/handlers/{category}/): Business logic
• •Response Builders (src/aidb_mcp/responses/): Standardized response construction
• •Decorators (src/aidb_mcp/core/decorators.py): Common handler functionality

Resource Files

For detailed guidance, see these resources:

• •Tool Architecture - Tool structure, handlers, decorators
• •Handler Decorators - @with_execution_context, @require_initialized_session
• •Composition Patterns - @timed, best practices, performance
• •Testing MCP Tools - E2E and integration testing

Code Reuse: Don't Reinvent

Before writing new code, check these shared modules:

Constants and Enums

File: src/aidb_mcp/core/constants.py

from aidb_mcp.core.constants import (
    ToolName,           # Tool names (INSPECT, STEP, EXECUTE, etc.) - class
    SessionAction,      # Session actions (START, STOP, STATUS, etc.) - Enum
    InspectTarget,      # Inspection targets (LOCALS, GLOBALS, STACK, etc.) - Enum
    StepAction,         # Step actions (INTO, OVER, OUT) - Enum
    ExecutionAction,    # Execution actions (RUN, CONTINUE) - Enum
    ErrorCode,          # Standardized error codes - Enum (in exceptions.py)
    ResponseStatus,     # Response statuses (OK, ERROR, WARNING) - class
    ParamName,          # Common parameter names - class
    ResponseFieldName,  # Response field names - class
    MCPResponseField,   # MCP protocol field names - class
)

Note: Some constants use classes instead of Enums for flexibility. Never use magic strings - always use constants.

Exception Types

File: src/aidb_mcp/core/exceptions.py

from aidb_mcp.core.exceptions import (
    ErrorCode,          # Error codes enum
    ErrorCategory,      # Error categories (VALIDATION, SESSION, etc.)
    ErrorRecovery,      # Recovery strategies
    classify_error,     # Classify exception by type/message
    get_recovery_strategy,     # Get recovery options
    get_suggested_actions,     # Get user-friendly actions
)

Use these for error handling - don't create new exception types unless necessary.

Performance Types

File: src/aidb_mcp/core/performance_types.py

from aidb_mcp.core.performance_types import (
    SpanType,           # Performance span types
    PerformanceSpan,    # Span data class
    TimingFormat,       # Output formats
)

Use for performance tracking - consistent metrics across tools.

Quick Patterns

Creating a New Tool (Step-by-Step)

Note: Examples below use placeholder names (YOUR_TOOL, YourResponse). See actual implementations in src/aidb_mcp/handlers/execution/stepping.py and src/aidb_mcp/responses/execution.py.

1. Define the tool schema (src/aidb_mcp/tools/definitions.py):

Tool(
    name=ToolName.YOUR_TOOL,  # Use constant from core/constants.py
    description="Clear, concise description.\n\nActions:\n- 'action1': What it does",
    inputSchema={
        "type": "object",
        "properties": {
            ParamName.ACTION: {"type": "string", "enum": ["action1", "action2"]},
            ParamName.SESSION_ID: {"type": "string"},
        },
    },
)

2. Create the handler (src/aidb_mcp/handlers/your_category/handler.py):

from aidb_logging import get_mcp_logger as get_logger
from aidb_mcp.core.decorators import with_execution_context
from aidb_mcp.responses.errors import ErrorResponse

@with_execution_context(track_variables=True)
async def handle_your_tool(args: dict[str, Any]) -> dict[str, Any]:
    """Handle your tool operation."""
    action = args.get(ParamName.ACTION)
    api = args.get("_api")

    try:
        result = await api.your_operation()
        return YourResponse(summary=f"Performed {action}", data={"result": result}).to_mcp_response()
    except Exception as e:
        return ErrorResponse(summary="Operation failed", error_code=ErrorCode.AIDB_ERROR.value, error_message=str(e)).to_mcp_response()

3. Register the handler (src/aidb_mcp/handlers/your_category/__init__.py):

from .handler import handle_your_tool

HANDLERS = {ToolName.YOUR_TOOL: handle_your_tool}

4. Add to registry (src/aidb_mcp/handlers/registry.py):

from .your_category import HANDLERS as YOUR_HANDLERS

TOOL_HANDLERS = {**SESSION_HANDLERS, **YOUR_HANDLERS}

5. Create response class (src/aidb_mcp/responses/your_response.py):

from dataclasses import dataclass
from aidb_mcp.core.constants import MCPResponseField

@dataclass
class YourResponse:
    summary: str
    data: dict[str, Any]

    def to_mcp_response(self) -> dict[str, Any]:
        return {
            MCPResponseField.SUCCESS: True,
            MCPResponseField.SUMMARY: self.summary,
            MCPResponseField.DATA: self.data,
        }

Modifying an Existing Tool

1. Locate: Tool definitions in src/aidb_mcp/tools/definitions.py, handlers in src/aidb_mcp/handlers/{category}/

2. Update schema (if adding parameters) in definitions.py

3. Update handler to extract new parameter and validate

4. Update response to include new fields if needed

Common Decorators

@with_execution_context

Captures debugging context and adds to response. Use when tool modifies execution state.

@with_execution_context(include_after=True, track_variables=False)
async def handle_your_tool(args: dict[str, Any]) -> dict[str, Any]:
    pass  # Context automatically added

@with_thread_safety

Ensures thread-safe access to shared resources. From aidb_mcp.core.decorator_primitives.

@timed

Tracks operation performance. From aidb_mcp.core.performance.

Response Construction

Response Builders

Use ExecutionStateBuilder and CodeSnapshotBuilder from aidb_mcp.responses.builders to avoid duplication.

Response Deduplication

Use ResponseDeduplicator.deduplicate() from aidb_mcp.responses.deduplicator to remove duplicate fields.

Response Size Limiting

Use ResponseLimiter from aidb_mcp.core.response_limiter:

• •limit_stack_frames(frames, max_frames=10)
• •limit_variables(variables, max_vars=50)
• •limit_code_context(lines, current_line, context_lines=5)

Compact Mode (Agent-Optimized Responses)

By default (AIDB_MCP_VERBOSE=0), responses are optimized for AI agents:

• •init: Returns minimal {language, framework, ready} instead of full examples/tips
• •session_start: Excludes next_steps guidance (agents learn from schema descriptions)
• •inspect (locals/globals): Variables use compact format {"varName": {"v": "value", "t": "type", "varRef": N}}

Set AIDB_MCP_VERBOSE=1 for human-friendly responses with full details and guidance.

Implementation pattern:

from aidb_common.config.runtime import ConfigManager

if ConfigManager().is_mcp_verbose():
    # Return full format
else:
    # Return compact format

Validation

Parameter Validation

Use ErrorResponse for missing/invalid parameters. Use validate_action(action, EnumClass) from aidb_mcp.tools.actions for action validation.

Session Validation

Use @require_initialized_session decorator from aidb_mcp.core.decorator_primitives to auto-validate session and inject _api, _context, _session_id.

Error Handling

Use classify_error() and get_suggested_actions() from aidb_mcp.core.exceptions to provide user-friendly error messages with recovery suggestions. Always include suggested_actions in error responses.

Testing Your Tool

Use DebugInterface from tests._helpers.debug_interface for E2E tests. Validate three dimensions:

1. •Structure: Response matches MCP protocol
2. •Content accuracy: Data reflects actual debug state
3. •Efficiency: No bloat (essential fields only)

See Testing MCP Tools for comprehensive testing guidance.

Best Practices

DO

• •Use constants from core/constants.py instead of magic strings
• •Use existing exception types from core/exceptions.py
• •Use response builders to avoid duplication
• •Limit response size with ResponseLimiter
• •Provide clear, actionable error messages
• •Track performance with @timed decorator
• •Write E2E tests that validate content accuracy
• •Set breakpoints at session start to avoid race conditions:
  python

  # ✅ CORRECT
  await api.start_session(program=file, breakpoints=[{"line": 10}])
  

DON'T

• •Return verbose summaries (agents need concise guidance)
• •Include unnecessary fields in responses
• •Use magic strings for field names or error codes
• •Create new exception types without checking existing ones
• •Skip input validation
• •Ignore performance implications
• •Test only structure (validate content accuracy too)

Common Pitfalls

1. •Bloated Responses: Include only essential fields the agent needs for next action
2. •Verbose Summaries: Keep concise and actionable (e.g., "Retrieved 5 local variables at line 42")
3. •Poor Error Messages: Provide user-friendly guidance with suggested actions, not technical errors

Summary

Internal Documentation: For MCP server implementation details, see src/aidb_mcp/ and docs/developer-guide/overview.md for system architecture.

MCP tools are the user-facing product of AIDB. Every tool must be:

1. •Accurate - Correct line numbers, values, state
2. •Clear - Concise summaries, actionable next steps
3. •Fast - Optimized operations, limited responses
4. •Clean - No junk fields, no redundant data

Use the resources linked above for deep dives into specific topics. Always prioritize agent experience - context windows are precious.