cortexcloud

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

No annotations are provided, so the description must carry the full burden. It discloses cost ('for free'), output contents, and workflow prerequisite. However, it does not explicitly state that it is read-only or has no side effects, leaving some ambiguity about whether it executes any optimization or stores data.

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 sentences, front-loaded with the action and output, and includes a clear workflow instruction. Every phrase earns its place, with no unnecessary words.

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 absence of an output schema, the description compensates by listing the return fields (mode/backend, runtime, USDC price, benchmark evidence). It also provides cost and usage context, making it sufficiently complete for an agent to decide when to invoke the tool.

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?

The schema description coverage is 100%, with detailed descriptions of problem, n, data, linear, and quadratic fields. The tool description adds no additional parameter semantics beyond referring to an 'optimization problem', so baseline 3 applies.

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 'Analyze an optimization problem for free' and lists specific outputs (recommended mode/backend, estimated runtime, USDC price, benchmark evidence). It explicitly distinguishes from the sibling cortex_optimize by saying 'Always call this before paying for cortex_optimize'.

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?

Provides explicit usage guidance: 'Always call this before paying for cortex_optimize' establishes a clear workflow and names the alternative. The description implies this tool is for free estimation, while cortex_optimize is the paid execution step.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden. It usefully adds 'Free' and enumerates the return contents (status, solution, objective, error), which is helpful. But it does not disclose typical polling behaviors like repeated calls, rate limits, or what statuses may be returned.

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 short sentences, front-loaded with the action and resource. Every word is informative, and there is no redundancy or unnecessary elaboration.

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 one-parameter polling tool with no output schema, the description covers the essential return fields. It ties to cortex_optimize via the schema's parameter description. It could mention the polling lifecycle (e.g., call until status is complete), but the simplicity warrants a high score.

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?

The schema already provides full coverage for job_id with a clear description ('Job id from cortex_optimize'). The tool description adds no additional meaning beyond the schema, so it meets the baseline but provides no extra 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 tool's verb ('Poll') and resource ('optimization job by id'), and lists the returned content ('status, solution, objective, error'). This distinguishes it from sibling tools like cortex_optimize (which creates jobs) and cortex_estimate_optimization.

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 implies the tool is used after cortex_optimize to check job status, and the schema reinforces this with 'Job id from cortex_optimize'. However, it does not explicitly state when not to use it or mention any alternative polling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the burden. It discloses that the tool is free, returns live availability, and includes a verified flag, which implies a read-only operation. This is richer than a bare listing, though it doesn't discuss auth or return format.

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 short sentences, front-loaded with the action and resource. The second sentence adds actionable usage guidance without fluff. Perfectly sized.

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?

The description covers the tool's purpose, the types of backends, additional output fields, cost, and when to use it. For a simple list tool with no output schema, this is adequate. It could optionally specify the return format, but the description is complete enough.

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?

The tool has zero parameters, so the description need not explain parameter syntax. Baseline 4 applies as there is no parameter ambiguity to resolve.

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 verb 'List' clearly identifies a read-only enumeration, and the resource 'solver backends' is specific. The categories (classical/hybrid/quantum) and the mention of 'live availability' and 'verified flag' add concrete detail that differentiates it from sibling tools like 'optimize' or 'get_job'.

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?

Provides clear context: 'check before choosing a mode' implies use this tool when you need to decide on a backend. While it doesn't name alternative tools, the context is sufficient for an agent to know when to invoke it. No exclusions are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.