Trace the causal chain upstream or downstream from a decision to identify contributing factors or resulting impacts. Specify direction and depth to audit decision logic.
Trace the causal chain upstream or downstream from a decision.
| Name | Required | Description | Default |
|---|---|---|---|
| direction | No | Trace direction | |
| max_depth | No | Max chain depth (default 5) | |
| decision_id | Yes | Decision ID to trace |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral details. It only states the action without disclosing output format, side effects, or any constraints. There is no mention of whether it is read-only, what happens if the decision is missing, or how results are structured.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that immediately states the action. It is front-loaded with the core purpose and contains no extraneous words, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, and the description does not describe what the returned causal chain looks like or any behavioral nuances. It also fails to mention default behavior (e.g., max_depth default) beyond what the schema provides. Given the lack of annotations and output schema, the description is incomplete for an agent to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage of parameter descriptions, so the description does not need to add parameter details. It does not offer extra meaning beyond the schema, which aligns with the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Trace the causal chain upstream or downstream from a decision.' It identifies a specific verb and resource, and the phrase 'causal chain' distinguishes it from sibling tools like query_decisions or find_precedents, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 others. It does not mention conditions, exclusions, or alternatives, leaving the agent to infer usage from the tool name and context. This is a notable gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.