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IDA-MCP

IDA-MCP

by Captain-AI-Hub
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Python
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Convert Number

convert_number

Converts numeric text between decimal, hex, binary, signed, and unsigned forms at a chosen bit width for reverse engineering analysis.

Instructions

Convert number to hex/unsigned/signed representations.

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": {
      "text": "<text>"
    },
    "name": "convert_number"
  }
}

Input Schema

NameRequiredDescriptionDefault
sizeNoBit width: 8, 16, 32, or 64
textYesNumeric text (decimal, 0x, 0b, trailing h)
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

B3.2/5.0
Behavior3/5

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

With no annotations, the description must carry behavioral disclosure. 'Convert' implies a pure, non-destructive computation, but the description never confirms this is side-effect free, never describes what output forms are returned for a given input, and never notes error behavior for malformed numeric text. It gives the essentials for a low-risk utility but leaves real gaps.

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?

A single front-loaded sentence with zero filler and the verb leading. It is efficiently sized, though its brevity is partly what leaves the gaps noted elsewhere.

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

Completeness3/5

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

With no output schema, the description is the only place to learn return shape, and it only gestures at it via 'hex/unsigned/signed representations'. It also omits the notable instance_id dependency (required when multiple databases are open), which the schema carries but which an agent could easily miss when composing calls.

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%, with 'size' enumerating bit widths and 'text' enumerating accepted numeric formats, so the schema already does the heavy lifting. The description adds nothing about the parameters (e.g., how 'text' format interacts with 'size'), making the baseline 3 correct.

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?

States a specific verb (convert) and resource (number) plus the target forms (hex/unsigned/signed), so an agent knows exactly what the tool computes. It does not distinguish itself from the closest sibling, read_scalar, which also surfaces numeric values from a database, so it falls short of a 5.

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?

There is no statement of when to use this versus alternatives, and no mention that it is a pure in-memory conversion needing no open database context (unlike read_scalar, which reads from an IDB). The agent must infer the usage context entirely.

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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