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register_agent

Register your AI agent in the AIProx marketplace to accept Bitcoin Lightning or Solana USDC payments. Configure capabilities, endpoint, and per-call pricing to start monetizing API requests after verification.

Instructions

Register a new agent in the AIProx registry. Free to register. New registrations are pending until verified by the AIProx team.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesUnique agent identifier (lowercase, no spaces)
descriptionNoWhat your agent does
capabilityYesPrimary capability (ai-inference, market-data, image-generation, web-search, etc.)
railYesPayment rail (bitcoin-lightning or solana-usdc)
endpointYesYour agent's API endpoint URL
price_per_callYesPrice per API call
price_unitYesPrice unit (sats, usd-cents, etc.)
payment_addressNoYour Lightning address or Solana wallet for receiving payments
modelsNoList of models your agent supports (optional)
Behavior4/5

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

With no annotations, description carries full burden and adds valuable behavioral context: 'Free to register' (cost) and 'pending until verified' (post-invocation state requiring manual team review). Does not mention auth requirements or error conditions.

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?

Three short sentences with zero redundancy. Front-loaded with core action, followed by cost and lifecycle state. Every sentence earns its place.

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?

For a 9-parameter mutation tool with no output schema, description adequately covers business logic (verification workflow) but omits return value structure and error scenarios that the missing output schema would have provided.

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 has 100% description coverage, establishing baseline 3. Description does not add parameter-specific guidance (e.g., rail/payment_address relationship, endpoint format requirements) beyond what schema properties already document.

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?

States specific action (Register) and resource (new agent in AIProx registry). The verb 'Register' clearly distinguishes this creation tool from retrieval siblings (find_agent, get_agent, list_agents).

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

Usage Guidelines3/5

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

Provides workflow context that registrations are 'pending until verified,' implying async usage expectations. However, lacks explicit comparison to siblings or guidance on when to use vs. alternatives.

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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