AgentIndex x402

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

With no annotations, the description carries the full disclosure burden. It reveals important behavior: the call produces a synthesized JSON brief, supports free status polling and result retrieval, and is designed to be context-efficient. Mentioning 'Try GET /jobs/sample' also signals an HTTP-style interaction model, though it does not detail lifecycle mechanics or failure modes.

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, opening with the core value proposition of multi-query research synthesis. The hype phrase about twenty calls is mildly non-operational, but the two functional additions—status polling and the sample endpoint—are useful and keep the description efficient.

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 one required string parameter and no output schema, the description gives enough context to invoke it sensibly: it returns a JSON brief, supports polling/retrieval, and points to a sample. It does not explain how the subject maps to the queries or how polling results are delivered, which is a small but not blocking gap.

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 0%, and the description never mentions the only parameter, subject, nor how to format it. The name 'subject' is the only hint that it expects a research topic, which is not enough to fully compensate for the lack of explicit parameter documentation.

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 performs multi-query web research and returns a synthesized, sourced JSON brief. It distinguishes itself from the search and translate siblings by emphasizing aggregation and synthesis across multiple queries rather than a single search or a language operation. It lacks a crisp verb-plus-resource form like 'create a research job', but the intent is understandable.

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 this tool is for substantial research that would otherwise take many searches, and positions it as a high-value consolidation option. It does not explicitly say when to prefer it over the search sibling nor when not to use it, leaving some inference to the agent.

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 behavioral burden. It discloses result limits, de-duplication/merging, no account/key/quota, and even offers a liive sample endpoint. It does not cover failure modes, but the disclosed traits are substantive.

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, each earns its place: result fmt, multi-query capability, and zero-setup access with a sample. Information is front-loaded and no fluff is present.

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 covers return shape and access requirements well. However, it lacks explicit semantics for max_results and leaves-the 'up to 10 results' relationship to the max_results parameter ambiguous.

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 explain parameters. It covers query (up to 5 queries, merged/deduped), extract (cleaned page extract), and summarize (optional short summary), but max_results is not explained and the 'up to 10 results'maximum is not reconciled with the default max_results of 5.

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 (real-time web search), enumerates the result fields and upper bound, and clearly differentiates this tool from its unrelated siblings (jobs, translate). There-is no ambiguity about what the tool does.

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 usage context: real-time web search with an optional summary and support for up th five queries per call. It does not explicitly say when not to use it, but siblings are unrelated, so no strong exclusion guidance is needed.

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 the 200-segment limit, preservation of Markdown/HTML/placeholders, order and count guarantees, source-language auto-detection, automatic model fallback, provider training exclusions, and no-auth/no-key access. This goes well beyond typical tool descriptions.

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 dense and front-loaded, starting with the core capability and then listing important behavioral guarantees. The middle sentence is long and packs many details, but every clause adds value; the closing notes about no account/API key and sample endpoint are useful.

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 no output schema and no annotations, the description covers authentication, input shape, batch limits, formatting preservation, and reliability behavior. It does not explicitly describe the output structure or error behavior, and target_lang syntax is still underspecified, so it is not fully 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 coverage is 0%, so the description must compensate. It adds meaning for text (string or array up to 200 segments), source_lang (auto-detected), and preserve_format (Markdown/HTML/placeholder preservation). However, the target_lang parameter format is not explicitly described, leaving some ambiguity about accepted language codes.

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: 'Translate up to 200 text segments in a single call.' It clearly defines scope (batch translation, single string also works) and differentiates the tool from naive repeated calls by emphasizing the 200-segment batching capability.

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 clear context for when to use the tool: when translating text, especially whole files or multiple segments that would otherwise require 200 separate calls. It does not explicitly name alternatives or state when not to use the tool, but the context is strong enough to guide an agent.

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