VERDICT

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hypothesis_set_state

Update the state of a hypothesis in a DFIR investigation to reflect findings as open, supported, refuted, or inconclusive.

Instructions

Update a hypothesis state: OPEN, SUPPORTED, REFUTED, or INCONCLUSIVE.

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": {
      "hypothesis_id": "<hypothesis_id>",
      "state": "<state>"
    },
    "name": "hypothesis_set_state"
  }
}

Input Schema

Name	Required	Description	Default
`hypothesis_id`	Yes
`state`	Yes
`rationale`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 14, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description must fully disclose behavioral traits. It only states the action and allowed states; it does not describe side effects (e.g., whether previous state is overwritten), required permissions, or error states. For a mutation tool, this is insufficient.

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?

The description is a single concise sentence that front-loads the purpose. It is efficient and has no wasted words. However, it omits important details that could be added without losing conciseness.

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?

Given the tool has 3 parameters (2 required), no annotations, and an output schema (though not shown), the description is incomplete. It does not cover parameter semantics beyond state, nor does it describe the output or any behavioral constraints. An AI agent would need additional information to use this tool correctly.

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

Parameters2/5

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

The input schema has 0% description coverage, so the description must explain parameter meanings. It only partially addresses the 'state' parameter by listing valid values (which are not enumerated in the schema). The 'hypothesis_id' and 'rationale' parameters receive no explanation. This leaves significant ambiguity for an AI agent.

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

Purpose5/5

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

The description uses the specific verb 'Update' and explicitly names the resource 'hypothesis state'. It lists the four possible states, making the tool's function immediately clear and distinguishing it from sibling tools like hypothesis_add (which creates new hypotheses).

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., hypothesis must exist), nor does it suggest appropriate contexts or exclude inappropriate ones. Sibling tool names are available but not referenced.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.