Deterministic verification for AI-generated analysis: run a spec, inspect or diff a workbook
Deterministic verification for AI-generated analysis: run a spec, inspect or diff a workbook
andon re-checks the numbers in finished analysis — the report your AI agent just drafted, the workbook a colleague "quickly updated" — against the data they came from and against themselves. It does this with arithmetic, not with another LLM: reconciliation, internal consistency, schema contracts and Excel workbook integrity, written down as a small YAML spec and enforced with exit codes.
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| Tool | What it does |
|---|---|
| name | no dropped orders |
| uses | gulmezeren2-byte/andon@v1 |
| run | pip install andon-verify |
| repo | https://github.com/gulmezeren2-byte/andon |
| reconcile | row_count, sum, aggregate, group_sum, keys |
| internal | total_row, percent_sum, recompute |
| schema | columns, unique, not_null, allowed_values, date_continuity |
| excel | integrity |
| plausibility | bounds, new_categories, mean_shift |
| code | meaning |
{
"mcpServers": {
"andon": {
"command": "uvx",
"args": ["andon-verify"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
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A Model Context Protocol server
This is a [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) server that supports remote MCP connections, with Github OAuth
This server exposes 22 MCP tools. You don't call these directly — once the MCP is connected, your AI client selects the right tool based on your prompt.