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update_result

Modify test run outcomes in QASE test management by providing updated result data for existing test executions.

Instructions

Update an existing test run result

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
idYes
hashYes
resultYes

Implementation Reference

  • MCP tool handler for 'update_result': parses input with schema and delegates to updateResult function.
    .with({ name: 'update_result' }, ({ arguments: args }) => {
      const { code, id, hash, result } = UpdateResultSchema.parse(args);
      return updateResult(code, id, hash, result);
    })
  • Zod schema for validating inputs to the update_result tool: code, id, hash, result.
    export const UpdateResultSchema = z.object({
      code: z.string(),
      id: z.number(),
      hash: z.string(),
      result: z.record(z.any()).transform((v) => v as ResultUpdate),
    });
  • src/index.ts:165-169 (registration)
    Tool registration in ListToolsRequestSchema handler, defining name, description, and input schema.
    {
      name: 'update_result',
      description: 'Update an existing test run result',
      inputSchema: zodToJsonSchema(UpdateResultSchema),
    },
  • Core updateResult function: pipes client.results.updateResult through toResult transformer.
    export const updateResult = pipe(
      client.results.updateResult.bind(client.results),
      toResult,
    );
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. While 'update' implies a mutation operation, the description doesn't specify permissions required, whether the update is reversible, rate limits, or what happens to unspecified fields. This leaves significant gaps in understanding the tool's behavior beyond the basic action.

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 a single, straightforward sentence with no wasted words. It's appropriately sized for a basic tool description and front-loaded with the essential action, making it easy 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 (4 required parameters including a nested object, no output schema, and no annotations), the description is insufficient. It doesn't explain parameter meanings, return values, error conditions, or behavioral nuances, leaving the AI agent with inadequate information to use the tool correctly in context.

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?

Schema description coverage is 0%, meaning none of the 4 parameters (code, id, hash, result) are documented in the schema. The description adds no information about what these parameters mean, their formats, or how they interact (e.g., whether 'id' identifies the result to update, what 'hash' is for, or what 'result' object should contain). This fails to compensate for the lack of schema documentation.

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

Purpose3/5

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

The description 'Update an existing test run result' clearly states the action (update) and target resource (test run result), which is adequate. However, it doesn't distinguish this tool from similar sibling tools like 'update_case', 'update_plan', or 'update_suite', leaving ambiguity about when to use this specific update operation versus others.

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. With multiple 'update_' sibling tools available (e.g., update_case, update_plan, update_suite), there's no indication of context, prerequisites, or exclusions to help an AI agent choose appropriately among them.

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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