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IDA-MCP

IDA-MCP

by Captain-AI-Hub
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Python
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Make Code

make_code

Convert raw bytes at a specified address into executable code in IDA Pro, enabling reverse engineers to turn data into disassembled instructions for analysis.

Instructions

Convert bytes at an address into code.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "address": "<address>"
    },
    "name": "make_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
addressYesAddress to convert into code
timeoutNoTimeout in seconds
instance_idNoREQUIRED whenever more than one database is open (from open_database's result or list_databases()); optional only while exactly one instance is live.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.0.1
    • changedInput schema / properties / instance_id / description
      Previous value: -"Instance id override"New value: +"REQUIRED whenever more than one database is open (from open_database's result or list_databases()); optional only while exactly one instance is live."
  2. First observedv0.9.7

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations, the description carries the full burden, yet it doesn't say this mutates the database/IDB, whether the change is reversible (undefine_items), whether the target must be undefined bytes first, or whether analysis must have completed. Only the bare transformation is stated.

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

Conciseness4/5

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

One short, front-loaded sentence with no wasted words. The brevity is efficient, though it shades into under-specification rather than redundancy.

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 an unannotated database-mutating tool with no output schema in a large sibling set, the description is too thin: it omits the mutation effect, preconditions (open database, analysis state), and the relation to make_data/undefine_items.

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 coverage is 100% and the schema already documents address, timeout, and the non-obvious instance_id constraint. The description adds nothing beyond repeating 'address', so baseline 3 applies.

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

Purpose3/5

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

"Convert bytes at an address into code" gives a clear verb and object, but 'code' is ambiguous in an IDA context (define instructions? create a function? decode?). It never distinguishes itself from close siblings like make_data, make_string, or undefine_items, which also transform bytes at an address.

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?

No guidance on when to use this instead of make_data, linear_disasm, create_function, or undefine_items (the inverse). The agent must infer the situation from the name alone.

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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