Neural Memory — Deep Inspect

Deep-dive into a specific code element — see its full context in the knowledge graph.

What you get

• Full summary: Detailed explanation of purpose, logic, and interface
• Parent: Which module or class contains this
• Callers: Who calls this function (upstream)
• Callees: What this function calls (downstream)
• Siblings: Other functions/methods at the same level
• Children: Contained elements (methods in a class, etc.)
• Call chains: Trace execution paths up and down the graph
• Source code: The actual implementation (optional)

How to call

Via MCP tool (neural-memory configured as MCP server in Claude Code):

Tool: neural_inspect
{ "node_id": "module::ClassName.method_name" }

or by name:

{ "node_name": "method_name", "show_code": true, "trace_calls": true }

Via Python (working directly in the project):

import asyncio
from neural_memory.server import neural_inspect, InspectInput

# By node_id (most precise — get from neural_query results)
asyncio.run(neural_inspect(InspectInput(node_id="module::ClassName.method_name")))

# By name (fuzzy match)
asyncio.run(neural_inspect(InspectInput(node_name="method_name", show_code=True, trace_calls=True)))

Parameters

Provide either node_id or node_name — node_id is preferred when available.