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Mythic MCP

Mythic MCP

by johanrobbenites
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Python
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run_sharphound

Collect Active Directory data by executing SharpHound on Windows agents, enabling BloodHound attack path analysis. Supports stealth and custom collection methods.

Instructions

Run SharpHound (BloodHound collector) on a Windows agent.
SharpHound.exe must already exist on the target system.

Args:
    agent_id: ID of the Mythic agent
    collection: SharpHound collection methods (Default: All)
    output_dir: Directory to store SharpHound output ZIP
    stealth: Use stealth collection (recommended)

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": {
      "agent_id": "<agent_id>"
    },
    "name": "run_sharphound"
  }
}

Input Schema

NameRequiredDescriptionDefault
stealthNo
agent_idYes
collectionNoAll
output_dirNoC:\Users\Temp

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
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. It only mentions the prerequisite and output directory, but does not disclose potential side effects like network activity, file creation on the target, or security/AV risks. This is a significant transparency gap for a security tool.

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 efficient and well-structured with an intro sentence, a prerequisite note, and a list of arguments. Every sentence provides necessary information with no redundancy.

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?

The description covers prerequisites and output location but lacks information on return format, how to retrieve the output ZIP, or behavioral risks. With no annotations and no output schema, it leaves meaningful gaps for a complex tool like SharpHound.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema description coverage, the description compensates by explaining each parameter: agent_id as the Mythic agent ID, collection methods, output_dir for the ZIP, and stealth as recommended. This adds meaningful context beyond the schema's type/defaults, though it could offer more detail on allowed collection values.

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?

The description clearly states the tool runs SharpHound (BloodHound collector) on a Windows agent, which is a specific verb and resource. It doesn't explicitly differentiate from sibling AD tools like ad_recon, but the tool name and description are specific enough to distinguish the core purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear prerequisite (SharpHound.exe must already exist) and recommends stealth mode, which gives context on when to use the tool. It doesn't mention exclusions or alternatives, so it stops short of an explicit 5.

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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