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Repro Capsule MCP

Repro Capsule MCP

by efficjump
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

analyze_case

Form evidence-cited hypotheses to answer a question about a bug reproduction case, using host sampling.

Instructions

Form evidence-cited hypotheses with host sampling, or return an explicit fallback.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "case_id": "<case_id>",
      "objective": "<objective>"
    },
    "name": "analyze_case"
  }
}

Input Schema

NameRequiredDescriptionDefault
case_idYesIdentifier returned by start_case.
objectiveYesQuestion the analysis should answer from current evidence.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=false and openWorldHint=true, implying modification and external calls. The description adds the behavior of returning an explicit fallback, which is useful, but does not detail state changes or other side effects.

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, front-loaded sentence with no filler. Every word contributes to the purpose, making it very concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the return values are covered. However, the description lacks details about the fallback behavior and the sampling process. It is minimally adequate but could be more informative for a complex analysis tool.

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 already provides clear descriptions for both parameters (case_id from start_case, objective as question). The description adds no additional parameter information, so it meets the baseline for high schema coverage.

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 states a specific action: forming evidence-cited hypotheses with host sampling, or returning a fallback. This clearly distinguishes it from sibling tools like start_case or execute_plan, though the term 'host sampling' is somewhat ambiguous.

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, what prerequisites exist (e.g., case must be started), or when alternatives are preferred. It merely states the action without context.

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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