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IDA-MCP

IDA-MCP

by Captain-AI-Hub
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Python
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Get Callees

get_callees

Retrieve callees for a function by address or name, grouped by target and call site, to map outbound calls during reverse engineering.

Instructions

Get callees grouped by target function and call sites.

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": {
      "addr": "<addr>"
    },
    "name": "get_callees"
  }
}

Input Schema

NameRequiredDescriptionDefault
addrYesFunction address or name
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

C2.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It hints at output grouping but does not state that this is a read-only operation, whether analysis must be complete, how timeout behaves, or what the return structure looks like.

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

Conciseness5/5

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

The description is a single efficient sentence with no redundant or filler content. It is front-loaded with the core action and groups the key output characterization immediately after.

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?

For a simple three-parameter read tool whose parameters are fully described in the schema, the description is minimally sufficient. However, without annotations or an output schema, it leaves gaps around prerequisites (such as an open database) and the exact shape of the returned callee grouping.

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%, so all three parameters are already documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, which is the baseline expectation when coverage is complete.

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?

The description states a specific verb and resource ("Get callees") and mentions grouping by target function and call sites. However, it does not clarify whether it returns only immediate callees or recursive ones, nor does it explicitly distinguish the tool from siblings like get_callers or callgraph.

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 guidance on when to use this tool versus alternatives such as get_callers, xrefs_from, or callgraph. The agent must infer the use case 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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