cortexcloud

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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly states the stateless single-turn nature, indicates a paid endpoint via 'x402-paid', and describes the response as containing model reply plus usage. It does not mention authentication or error cases, but it covers the core behavioral traits.

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, direct sentences with no filler. The core purpose and endpoint are front-loaded, and the return behavior is stated immediately after.

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 low-complexity tool with a single fully documented parameter, the description is nearly complete: purpose, endpoint, paid nature, and return summary are all present. It leaves out details about authentication and the exact meaning of x402-paid, but those are not essential for an agent to invoke the tool 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?

The input schema already fully documents the only parameter, 'messages', including its OpenAI-style structure and role/content fields. The description adds no additional parameter semantics, so baseline 3 is appropriate.

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 explicitly names the action (single-turn chat completion), the specific endpoint (/v1/ai/chat), and the return value (model reply + usage). This is clear enough to distinguish it from the optimization, research, and wallet-related sibling tools.

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 'single-turn' qualifier implies when to use this tool, but there is no explicit guidance about alternatives or when not to use it. The description does not name any sibling tool or exclusion condition.

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 are provided, so the description carries the behavioral burden. It usefully discloses SSRF guarding and the x402-paid cost model, which are important safety and payment traits. It does not mention whether the response is returned, what side effects the remote endpoint may see, or any authentication requirements.

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 a single dense sentence that front-loads the core behavior ('Outbound SSRF-guarded HTTP') and then adds route and payment context. Every phrase carries useful information with no 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?

With no output schema and no annotations, the description should explain response and error behavior, but it does not. It also omits authentication details beyond the cryptic 'x402-paid' label, leaving an agent with incomplete expectations for using the tool 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 description coverage is 100%, with all parameters such as url, method, headers, body, and timeout already documented. The tool description adds no additional parameter-level meaning beyond what the schema already provides, so the 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 identifies the tool as outbound HTTP with SSRF guarding, and names the exact endpoint. It lacks an explicit verb like 'send' or 'make', but the meaning is unambiguous and distinguishes it from sibling automation tools.

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 phrase 'Outbound' implies it should be used when an external HTTP request is needed, and 'automation' gives a category context. However, it does not explicitly state when not to use it or compare it against alternatives such as cortex_automation_transform.

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 are provided, so the description carries the full burden. It explains what the tool does but not key behavioral details: whether the input is mutated, what is returned, error behavior, or the implications of 'x402-paid'.

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 a single, front-loaded sentence with no filler. Every word conveys meaningful information about the tool's purpose and context.

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 tool is relatively simple and the schema covers both parameters, but with no annotations and no output schema, the description could add more about expected return value and payment/automation implications. It is adequate but not complete.

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 description coverage is 100%, so the baseline is 3. The description adds the operation list (pick/omit/rename/set) but does not detail the actual structure of the rules object or how the operations are expressed, leaving the agent to infer rule syntax.

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 states a specific verb ('shape') and resource ('arbitrary JSON') and names the operation types (pick/omit/rename/set). It is clear but does not explicitly distinguish itself from siblings beyond the 'automation' context.

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?

There is no guidance on when to use this tool versus alternatives. The 'automation' tag only weakly implies a context; it does not state exclusions, prerequisites, or how it relates to sibling tools like cortex_automation_http_request.

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 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?

No annotations are provided, so the description carries the full burden. It discloses that the call is 'Free' and enumerates return fields, but it does not explicitly state this is a non-mutating read operation, nor does it cover rate limits, error scenarios, or job lifecycle concerns. Some useful context is present, but gaps remain.

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?

A single sentence with no filler. The key verb and purpose are front-loaded, and each clause adds value: 'by id' specifies input, 'Free' notes cost, and the return list sets expectations. This is exemplary conciseness.

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 simple one-parameter polling tool without an output schema, the description is sufficiently complete. It identifies the source of the ID (via schema), states the return content, and fits well within the sibling workflow. No critical information is missing.

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 description coverage is 100% because job_id is fully described as 'Job id from cortex_optimize'. The tool description adds no additional meaning about the parameter, so a baseline of 3 is appropriate since the schema carries the semantic load.

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 uses a specific verb 'Poll' with a clear resource ('optimization job by id') and explicitly lists what is returned (status, solution, objective, error). This distinguishes it from siblings like cortex_optimize (which creates jobs) and cortex_list_backends (which lists backends).

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 clearly implies the tool is for checking the status/result of an optimization job, which is unique among siblings. It lacks an explicit 'when not to use' or direct alternative mention, but the polling purpose is self-evident from the description and schema reference to cortex_optimize.

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.

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

With no annotations, the description carries the burden of behavioral disclosure. It usefully notes the 'x402-paid' nature (a cost/authorization signal) and that results are ranked with snippets. However, it does not mention rate limits, error behavior, or confirm read-only semantics, leaving some behavioral aspects undisclosed.

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 tight sentences, front-loaded with the core action and endpoint. Every word earns its place; there is no filler or repetition of schema information.

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 simple 2-parameter tool with no output schema, the description gives enough context to call it correctly and interpret the broad result type. It could be more complete about the exact response shape, but 'ranked results with snippets' plus the schema's parameter docs suffice for basic usage.

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 description coverage is 100%—both query and limit have descriptions. The tool description adds no parameter-specific meaning beyond what the schema already provides, making this baseline appropriate.

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 states a specific verb and resource: 'Web search via /v1/research/search' and clarifies the output ('ranked results with snippets'). This clearly distinguishes it from the listed sibling tools, none of which are web search operations.

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 usage—when you need web search results—but provides no explicit when-to-use guidance, exclusions, or alternatives. Since no sibling performs web search, the lack of alternatives is less critical, but the guidance is still only implied rather than stated.

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 of behavioral disclosure. It states that the operation returns a spot price and is free, which gives some context, but it does not mention response format, rate limits, authentication, or what happens when no match is found. These gaps prevent a higher score.

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 with no filler. The core purpose is front-loaded, and 'Free' earns its place as relevant cost information.

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 simple price lookup, the description covers the main input types and the output currency, but there is no output schema and no annotations. It omits details like whether one of the two parameters is required, expected response shape, or behavior in error cases, making it minimally adequate rather than complete.

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 description coverage is 100%, so the schema already documents both parameters. The description paraphrases the parameter roles by mentioning 'contract address or CoinGecko id' but does not add meaningful semantics beyond that, such as whether exactly one is required or what happens if both are supplied.

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 identifies the resource (ERC-20 token) and the output (spot price in USD), and it names the two input identifiers (contract address or CoinGecko id). It does not explicitly contrast itself with sibling tools, but its purpose is unambiguous and distinguishable from the provided siblings.

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 gives no guidance on when to use this tool instead of alternatives, no exclusions, and no prerequisites. The word 'Free' hints at a cost advantage but does not reference any sibling tool or decision rule.

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 are present, so the description carries the full burden of behavioral disclosure. It reveals that results are USD-valued ERC-20 balances, but it does not mention auth requirements, rate limits, error behavior, or how it handles invalid addresses, leaving significant behavioral context undisclosed.

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 extremely compact, front-loading the resource and scope in the first sentence, with minimal wasted words. 'Free' adds a distinct cost note without bloating the definition.

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 read tool, the description covers the required address input and indicates the core output (USD-valued balances). It does not detail return structure or edge cases, but the tool's simplicity and full schema coverage make this a minor gap rather than a critical one.

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 100% coverage for the single parameter, describing it as an EOA address, and the description simply restates 'EOA address' without adding format specifics or examples. This matches the baseline of relying on the schema.

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 identifies the resource (ERC-20 token balances) and scope (an EOA address), adding the useful detail that values are in USD. It distinguishes itself from siblings like cortex_token_price by the wallet-balance resource, though it does not explicitly name or contrast those siblings or use an explicit verb.

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?

There is no guidance on when to use this tool versus alternatives. 'Free' is a cost signal, not usage direction, and no exclusions or alternative tool mentions are provided.

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