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AI Proof of Us MCP Server

by 0xddneto
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TypeScript
Remote

export_ai_receipts

Read-onlyIdempotent

Retrieve signed receipts stored locally, optionally filtered by a farming wallet address, returning a JSON count and payloads for backup or analysis without creating, validating, or submitting anything.

Instructions

Export signed receipts already stored in this MCP installation, optionally limited to one farming wallet address. Returns JSON with count and receipt payloads, so use it only where local receipt data is appropriate to expose. It does not create a receipt, validate a claim, contact Base, or submit a transaction; use get_aipou_status instead for a compact private summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
walletNoOptional 20-byte farming wallet address filter. Omit it to export every locally stored receipt.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.3
    • addedInput schema / properties / wallet / description
      Added value: +"Optional 20-byte farming wallet address filter. Omit it to export every locally stored receipt."
  2. Addedv0.1.1

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark it read-only, idempotent, and non-destructive, and the description reinforces this by stating it does not create receipts, validate claims, contact Base, or submit transactions. It also discloses that it returns JSON with count and receipt payloads, adding useful behavioral context beyond annotations.

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 two sentences with no filler, front-loads the core purpose, and packs in output format, exclusions, and an alternative tool. Every sentence contributes meaningful guidance.

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

Completeness5/5

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

For a single-optional-parameter, read-only export tool, the description covers what it does, what it returns, when it should be used, and what it deliberately avoids. There is no output schema, so the explicit mention of count and receipt payloads is valuable and sufficient.

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 coverage is 100% and the single parameter's description already explains the wallet filter and omission behavior. The tool description restates this without adding new parameter-level meaning, so it meets the baseline but does not exceed it.

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 states a specific verb ('Export') and resource ('signed receipts already stored in this MCP installation'), and clarifies the optional wallet filter. It distinguishes itself from get_aipou_status by naming the alternative explicitly, so an agent can select it correctly.

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?

It gives explicit when-to-use guidance ('only where local receipt data is appropriate to expose') and lists what the tool does not do, pointing to get_aipou_status as the alternative for a private summary. This is clear exclusion and routing behavior.

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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