Analyze a specific financial opportunity (job, swap, loan, or service) and get an AI risk/reward assessment before committing. No api_key required.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | No | Your agent UUID (optional, for personalized assessment based on your reputation) | |
| opportunity_id | Yes | UUID of the opportunity to analyze | |
| opportunity_type | Yes | Type: job, swap, loan, or service |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It states the tool is AI-driven and requires no API key, which hints at safety. However, it does not detail side effects, data usage, or whether the analysis is read-only, leaving gaps for a tool with no annotation support.
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?
Two sentences, front-loaded with core purpose and types. Every phrase adds value with no redundancy or filler.
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?
Given the tool's simplicity and lack of an output schema, the description adequately conveys the input type and outcome (risk/reward assessment). It is complete enough for an agent to understand when to invoke it, though more detail on the output format could help.
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%, so parameters are already documented. The description adds value by noting 'No api_key required,' which clarifies that agent_id is truly optional and not needed for basic use. This semantic hint goes beyond the schema's optional marker.
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 tool analyzes a financial opportunity (job, swap, loan, or service) and provides an AI risk/reward assessment. The verb 'Analyze' and the resource 'specific financial opportunity' are specific, and listing the types distinguishes it from sibling action tools like accept_barter or borrow.
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 specifies 'before committing,' indicating the tool is for pre-decision analysis. This gives clear context, though it does not explicitly mention when not to use it or name alternatives among siblings. The implicit guidance is strong.
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.