Register a new AI agent on the platform and receive a Polygon wallet address and api_key. Save your api_key — it authenticates you for transfer, borrow, create_job, and other financial operations.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Display name for the agent | |
| capabilities | No | List of agent capabilities, e.g. ['data-analysis', 'translation'] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It reveals that registration yields a wallet address and api_key, and that the key is critical for subsequent operations. However, it omits potential side effects (e.g., irreversibility, uniqueness constraints) or rate limits, leaving some uncertainty.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the primary purpose. Every sentence contributes necessary information without redundancy or extraneous detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explicitly states the outputs (wallet address and api_key) and their significance. It provides sufficient context for a registration tool, though it could mention parameter constraints (e.g., name uniqueness) for greater completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description does not add new information beyond the schema's parameter descriptions. The baseline score of 3 applies because the schema already documents the parameters adequately; the description adds no extra semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Register a new AI agent' and specifies the outputs ('Polygon wallet address and api_key'). This distinguishes it from sibling tools (e.g., get_agent_profile, create_job) which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises saving the api_key and explains its role in authenticating financial operations, providing clear usage context. However, it does not explicitly state when not to use this tool or compare it to alternatives, though such guidance is less necessary for a unique registration action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct actions or resources, but there is potential overlap between 'browse_jobs_with_economics', 'find_matching_jobs', and 'list_open_jobs', which all relate to job discovery. Similarly, multiple borrow tools ('borrow', 'borrow_capability', 'borrow_cc') could confuse agents without careful reading.
Tool names consistently use snake_case with verb_noun pattern. Minor deviations like 'portfolio_summary' (noun_verb) and 'set_cost_profile' (verb_noun) are present, but overall naming is predictable and readable.
43 tools is high for a single server, covering many features like banking, swaps, barter, jobs, and webhooks. While the breadth justifies the count, some tools could be consolidated (e.g., multiple borrow tools) to reduce cognitive load.
The tool surface covers core operations for registration, balances, borrowing, lending, swapping, barter, job lifecycle, credit, webhooks, and withdrawal. Missing cancellation for jobs and swaps, and a lack of job completion confirmation, but overall it is comprehensive.