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evolve

Dry-run architectural changes by applying patches to see structural deltas before implementation.

Instructions

Dry-run architectural changes — apply a patch and see the structural delta.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesEN source code describing the current system
patchYesEN source code patch to apply

Implementation Reference

  • The 'evolve' tool registration and handler logic. It takes 'source' and 'patch' as input and calls the 'evolve' API endpoint.
    server.tool(
      "evolve",
      "Dry-run architectural changes — apply a patch and see the structural delta.",
      {
        source: z.string().describe("EN source code describing the current system"),
        patch: z.string().describe("EN source code patch to apply"),
      },
      async ({ source, patch }) => {
        const result = await callApi("evolve", { source, patch });
        return {
          content: [{ type: "text" as const, text: result.text }],
          isError: result.isError,
        };
      }
    );
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a 'dry-run' operation, implying it's non-destructive and read-only, which is helpful. However, it doesn't describe other behavioral traits such as performance characteristics, error handling, or what 'structural delta' entails in terms of output format. For a tool with no annotations, this is a significant gap in transparency.

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?

The description is highly concise and front-loaded, consisting of a single sentence that efficiently conveys the core functionality: 'Dry-run architectural changes — apply a patch and see the structural delta.' Every word earns its place, with no unnecessary elaboration, making it easy for an agent to parse quickly.

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 complexity of architectural changes and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'structural delta' means in practice, how results are presented, or any limitations (e.g., supported patch formats). For a tool with no structured output and behavioral gaps, this leaves the agent with insufficient context to use it effectively.

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

Parameters3/5

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

The input schema has 100% description coverage, with clear documentation for both parameters ('source' and 'patch'). The description adds minimal value beyond the schema, as it doesn't explain parameter semantics like what 'EN source code' means or how the patch should be formatted. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but the description could have enhanced understanding.

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 clearly states the tool's purpose: 'Dry-run architectural changes — apply a patch and see the structural delta.' It specifies the verb ('dry-run'), resource ('architectural changes'), and outcome ('see the structural delta'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'diff' or 'analyze_system', which might have overlapping functionality.

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 minimal guidance on when to use this tool, only implying it's for previewing changes before applying them. It doesn't specify when to use it versus alternatives like 'diff' (which might compare differences) or 'analyze_system' (which might assess impacts), nor does it mention prerequisites or exclusions. This leaves the agent with little context for tool selection.

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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