Return the chronological validation snapshots for an idea, each capturing the VC score and a summary at that point in time. Use it to compare how validation results changed between runs. Read-only and free; pass an ideaId you own.
| Name | Required | Description | Default |
|---|---|---|---|
| ideaId | Yes | The idea ID |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes | The tool result rendered as human and AI readable text or markdown. |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "result": {
+ "description": "The tool result rendered as human and AI readable text or markdown.",
+ "type": "string"
+ }
+ },
+ "required": [
+ "result"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, so the description's 'Read-only and free' confirms and adds cost semantics. It also describes the return structure (VC score, summary) and chronological ordering, which go beyond annotation fields.
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?
Two sentences, front-loaded with purpose, no wasted words. Every sentence earns its place.
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?
Given the output schema exists (not shown but indicated), the description covers the tool's purpose, usage, parameter, and behavioral traits adequately. It is complete for a read-only tool with one parameter.
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?
Schema coverage is 100% with a basic description ('The idea ID'). The description adds ownership semantics ('pass an ideaId you own'), which is not present in the schema, thus adding meaningful value.
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 uses a specific verb ('Return') and resource ('the chronological validation snapshots for an idea'), and distinguishes the tool from many sibling get_* tools by focusing on validation snapshots with VC score and summary.
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 explicitly states the use case ('compare how validation results changed between runs') and prerequisites ('pass an ideaId you own'). It does not explicitly mention when not to use alternatives, but the context is clear enough.
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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