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by Formative-Sum41
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Python
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make_synthetic_twin

Generates a synthetic Excel twin with identical structure but fake values, allowing safe code testing on sensitive files without exposing real data.

Instructions

Generate a SYNTHETIC TWIN of a real Excel workbook at path, written to out. Schema (sheets, headers, column types, true row counts) is preserved; every data value is freshly FAKED — no real value is copied. Reason and write your analysis code against the twin; then run it on the real file with run_on_real. Returns the schema summary (safe shape).

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": {
      "path": "<path>",
      "out": "<out>"
    },
    "name": "make_synthetic_twin"
  }
}

Input Schema

NameRequiredDescriptionDefault
outYes
pathYes
cap_rowsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well. It discloses key behavioral traits: schema is preserved, every data value is freshly FAKED, no real value is copied, and it returns a safe schema summary. This gives the agent essential safety and data-handling context.

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 three dense sentences, front-loaded with the core action and then efficient supplementary detail. There is no fluff or redundancy; every sentence earns its place.

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

Completeness4/5

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

The description covers the core function, the workflow pairing with run_on_real, and the return value (schema summary). It lacks any explanation of cap_rows and its potential effect on 'true row counts,' which would be a notable gap for a tool of moderate complexity. Overall, it is quite complete but not flawless.

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?

Schema description coverage is 0%, so the description must compensate. It does explain path and out (': path', 'written to out'), but it does not mention cap_rows at all. This leaves one of three parameters semantically opaque, so the compensation is partial.

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

Purpose5/5

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

The description uses a specific verb+resource: 'Generate a SYNTHETIC TWIN of a real Excel workbook at `path`, written to `out`.' It clearly differentiates from siblings by framing this as the twin-creation step and explicitly mentions run_on_real as the subsequent step for real-file execution.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit workflow guidance: 'Reason and write your analysis code against the twin; then run it on the real file with run_on_real.' This tells exactly when to use this tool and names the alternative (run_on_real) for the next phase, satisfying the 'when/when-not/alternatives' criterion.

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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