Solve a QUBO/Ising optimization problem (x402-paid, USDC on Base). Returns a job_id to poll with cortex_get_job. Prices per successful run: classical $0.05, hybrid $0.10, quantum $1.503 (exact price quoted by POST /v1/estimate before payment). Use mode=auto unless you specifically need quantum.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Defaults to auto (fastest proven backend) | |
| problem | Yes | Problem to solve |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, but the description discloses key behavioral traits: the call is paid (x402, USDC on Base), prices differ by mode, only successful runs are charged, and the operation is asynchronous. It does not cover failure or refund behavior, but the most important side effects are surfaced.
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?
Three dense sentences, front-loaded with the core purpose, then essential operational and pricing details. Every sentence earns its place and there is no repetition of schema fields.
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?
For a paid, asynchronous, nested-parameter tool with no output schema, the description covers the main lifecycle: solve, get job_id, poll via cortex_get_job, and estimate cost. It slightly under-specifies what happens on failure or how the final result is structured, but overall it is reasonably complete for an agent to use the tool safely and correctly.
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 the baseline is 3. The description adds value beyond the schema by explaining mode semantics ('auto unless quantum'), mapping mode choices to prices, and noting the job_id return flow. It does not add detail to the problem structure, but the schema already covers that well.
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 opens with a specific verb and resource: 'Solve a QUBO/Ising optimization problem.' It also distinguishes itself from siblings by stating it returns a job_id to poll with cortex_get_job and by referencing price estimation, making the tool's role clear.
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?
It gives explicit usage guidance: 'Use mode=auto unless you specifically need quantum,' and it directs the agent to POST /v1/estimate before payment for exact pricing. It also names cortex_get_job as the follow-up polling tool, creating a clear workflow.
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.
Each tool targets a distinct operation: chat, HTTP automation, JSON transform, optimization planning/execution/polling, backend listing, research search, token price, and wallet balances. Related optimization tools are clearly separated into estimate, solve, and poll stages, so an agent can select the right one without ambiguity.
All tools share a snake_case 'cortex_' prefix, but the suffix pattern is inconsistent: some are verb_noun (get_job, list_backends), some are single verbs (optimize), and some are noun phrases (token_price, wallet_balances, ai_chat). The names are readable but do not follow a uniform convention.
Ten tools is a well-scoped size for a multi-purpose cloud API covering AI, automation, optimization, research, and crypto queries. Each tool maps to a distinct paid or free endpoint, and there is no obvious bloat or redundant functionality.
The optimization workflow is well covered with estimate, backend listing, solve, and job polling. Minor gaps exist, such as no job cancellation and read-only wallet tools with no transfer capability, but the main workflows agents would attempt can be completed without dead ends.