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deep-agentic-core-mcp

deep-agentic-core-mcp

Official
by DeepAgentLabs
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Python
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List Chaos Faults

chaos.list_faults
Read-only

Discover supported fault types for chaos experiments. Use this list to choose failure scenarios to inject and validate system resilience.

Instructions

List the supported fault types for chaos experiments.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "chaos.list_faults"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, and the description is consistent with a harmless read operation. The description adds a bit of context by indicating it returns the supported fault catalog, but it does not disclose output structure or any other behavioral constraints.

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?

A single sentence with no redundancy. The verb and scope are front-loaded, and every word contributes to the tool's meaning.

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

Completeness4/5

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

For a zero-parameter, read-only enumeration tool, this description is nearly complete: an agent can tell it is called to obtain supported fault types. It loses one point for not specifying the exact return format, especially since there is no output schema.

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?

The tool has zero parameters and the schema coverage is trivially complete. Since there are no parameters, the description does not need to add parameter-level meaning, and the zero-parameter baseline applies.

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 names a specific verb ('List') and resource ('supported fault types for chaos experiments'), making the tool's function immediately clear. It does not explicitly differentiate from sibling tools, but the scope is unambiguous and distinct from the nearby chaos.run_experiment.

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

Usage Guidelines3/5

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

The phrase 'for chaos experiments' implies this is a discovery/read tool used when planning or configuring experiments, but it never states when to prefer it over alternatives or whether it should be called before chaos.run_experiment. Usage context is present but only implied.

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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