Request confirmation before applying AI-proposed code changes by providing action details and impact level to prevent unintended modifications.
Request confirmation before applying research-based changes
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Description of the action to be performed | |
| impact | No | Impact level of the action | medium |
| details | Yes | Detailed information about the action |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description signals that the tool is interactive—it requests confirmation rather than directly applying changes—which adds behavioral context beyond the annotations. However, it does not clarify what happens after confirmation, whether the caller must apply the change separately, or any side effects beyond the confirmation request itself.
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 with no filler or unnecessary detail. It is front-loaded with the core purpose and communicates the essential context efficiently.
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?
For a relatively simple interactive confirmation tool, the description captures the core purpose and timing. The schema documents all parameters, and the annotations provide safety signals, so the description is sufficiently complete for an agent to understand the tool's role.
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 input schema already provides complete descriptions for all three parameters, including the impact enum and default value. The description adds no additional parameter-level meaning, so the baseline score of 3 is appropriate.
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 states a clear verb and resource: it requests confirmation for an action before applying research-based changes. This distinguishes the tool as an interactive confirmation gate rather than a validation or execution tool, though it does not explicitly name sibling 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 phrase 'before applying research-based changes' provides clear timing context for when this tool should be used. It does not state exclusions or explicitly compare against sibling tools like validate_adr or incorporate_research, but the intended use is reasonably evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.