AgentIndex x402

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

With no annotations, the description carries the full burden. It discloses that the tool is read/synthesis-oriented, produces a sourced JSON brief, and supports status polling and result retrieval. However, it does not explain whether a job must be created first, how long polling might take, or whether there are authentication or rate-limit considerations.

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 compact and front-loaded with the core value proposition, then gives actionable next steps with the sample endpoint. Every sentence contributes useful information without unnecessary padding.

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 tool with no output schema and no annotations, the description leaves key invocation details unclear: the exact endpoint to create a job, how the 'subject' parameter is sent, what the polled status response looks like, and how the final JSON brief is structured. The sample GET /jobs/sample helps but does not fully compensate for these gaps.

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 only parameter, 'subject', has no schema description and the overall schema description coverage is 0%. The description never explicitly says that 'subject' is the research topic, query, or input text, so the agent must infer this from the tool name and surrounding text. This is a significant gap for a required parameter.

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 that the tool performs multi-query web research and returns a synthesized, sourced JSON brief in one call. This distinguishes it from a plain search tool, though it never explicitly names the 'search' sibling or defines the async job lifecycle.

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 provides clear context: use this for broad research that would otherwise require many individual search calls. It also mentions free status polling and result retrieval, but it does not explicitly state when to prefer 'search' over 'jobs' or list exclusions.

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, and it does so well: no account/API key/quota, real-time behavior, a 10-result cap, query merging/deduplication, and the result fields. An agent can form accurate expectations about authentication, limits, and output content without extra metadata.

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 information-dense sentences with no filler. It front-loads the core action and immediately provides the output shape, limits, and authentication context. Every clause contributes useful 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?

There is no output schema and no annotations, so the description must cover return values and constraints; it does. The only notable gap is that it does not explicitly explain how extract, summarize, and max_results map to the schema booleans/integer, nor the default of 5 results. Still, the description is largely sufficient for correct invocation.

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 0%, so the description must add meaning. It does: 'up to 5 queries' maps to the query array, 'cleaned page extract' maps to extract, 'optional short summary' maps to summarize, and 'up to 10 results' references the max_results cap. However, it never explicitly names the parameters or states defaults/maximum values, leaving some inference work to the agent.

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 'Real-time web search', a specific verb and resource, and then lists exactly what results contain: title, URL, cleaned page extract, publish date, and optional summary. This makes its purpose clear and distinguishes it from sibling tools like jobs and translate.

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 clear context: use this for real-time web search, and use the multi-query feature to replace five separate calls with one. It does not explicitly name when-not-to-use or alternative sibling tools, so it falls short of a full 5, but the usage context is strong.

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 provided, the description carries the full burden and does so impressively. It discloses batching limits, preservation of Markdown/HTML/{x} placeholders, order and count integrity, source auto-detection, multi-model fallback for uptime, exclusion of providers that train on prompts, and no-account/no-API-key access. This goes well beyond a generic 'translate text' statement, though it omits output shape and error behavior.

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 dense, front-loaded paragraph that leads with the core function and batching advantage before listing supporting features. Each clause adds distinct information, so there is little waste. The closing 'Try GET /translate/sample' is a minor appendix but not harmful; the structure could be slightly improved with bullets, though it remains clear.

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 4 parameters, no output schema, and no annotations, the description covers many practical aspects: input flexibility, format preservation, auth requirements, and provider privacy. However, it leaves meaningful gaps: the return shape is not explicitly stated (string vs array), there is no guidance on what happens beyond 200 segments, and target_lang's accepted value format is unspecified. These are relevant for an agent to invoke the tool correctly and interpret results.

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 0%, so the description must compensate, and it does partially. It explains that text can be a single string or array, clarifies source_lang through auto-detection, and illustrates preserve_format via placeholder and markup preservation. However, it does not specify the expected format for target_lang (e.g., language codes like 'es') and only implicitly covers preserve_format's full semantics.

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 ('Translate') and resource ('text segments'), and quantifies the capability ('up to 200 text segments in a single call'). It clearly positions the tool as a translation endpoint and is unmistakable against siblings 'jobs' and 'search', which are unrelated. The added context about replacing 200 separate calls further sharpens the purpose.

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 provides clear contextual guidance: use for batch translation of up to 200 segments, a single string also works, format is preserved, and source language is auto-detected. It does not explicitly mention when not to use the tool or name alternative tools, but given the siblings are unrelated to translation, the guidance is sufficient for an agent to select it appropriately.

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