alpha402x Equity Research

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

Annotations already mark the tool readOnlyHint=true, and the description adds useful behavioral context: it queries EDGAR, orders periods newest-first, and returns payable URLs at no charge. It does not discuss failure modes or rate limits, but that is not required for a simple read-only lookup.

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?

One dense, front-loaded sentence conveys source, scope, outputs, and pricing with no filler. The free-vs-paid distinction is stated at the end 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 two-parameter read-only check with no output schema, the description covers what the call does, what it returns, and the key limitation (U.S. tickers). An agent has enough to select and invoke it appropriately.

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%, and the parameter descriptions already explain ticker and the optional period format. The tool description mainly restates the overall purpose rather than adding parameter-level detail beyond 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 names the exact action (coverage check), the data source (SEC EDGAR), the target (any U.S. ticker), and the outputs (availability, fiscal periods, payable URLs). It clearly distinguishes this pre-purchase check from the paid extraction product.

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 explicitly frames the tool as a no-charge coverage check and contrasts it with the paid product (the extraction itself), telling the agent when to use it and when not to. The U.S.-ticker scope is also stated as an inclusion 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?

Annotations include readOnlyHint=true, which already signals a non-mutating operation. The description adds useful context: the tool mirrors the same constants the x402 gates are built from, and the live response header remains authoritative. This clarifies the data source and its relationship to the live 402 response, which is beyond the annotation. However, it doesn't disclose details like error behavior for invalid paths, or whether terms may change over time.

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 sentences, dense with information: the first defines the output, the second sets expectations about authority. No filler, key details front-loaded.

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 read-only tool, the description is nearly complete. It explains what the tool returns, the scope (one payable URL or product path), and the relationship to the authoritative header. Missing minor context: there's no output schema, so return-format specifics beyond the field list are absent, and there's no mention of error cases or prerequisites. But given the low complexity, the description covers what an agent needs.

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% — the only parameter, product, is described with several concrete examples. The description adds that the parameter should be a payable URL or product path, which reinforces the parameter semantics but doesn't add a lot beyond the schema's examples. Baseline of 3 is appropriate because the schema already covers the parameter 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 name and title already convey the resource, but the description sharpens it: 'Exact x402 payment terms for one payable URL or product path (price, asset, network, payTo, scheme).' This gives a specific verb (get/exact) and a concrete resource (payment terms for a product path), and enumerates the fields returned. It doesn't explicitly differentiate from siblings like how_to_pay_x402, but the emphasis on exact constants versus a guide is reasonably 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?

The description implies the use case: retrieving the exact payment terms for a payable URL. It also gives a valuable usage steer: 'The live 402 response's PAYMENT-REQUIRED header remains the authoritative terms document; this tool mirrors the same constants the gates are built from.' This tells the agent when the tool is authoritative and when to prefer the live header. It doesn't explicitly say when to use check_10q_coverage or list_research instead, but the scoping to a single product path is clear.

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?

The readOnlyHint annotation already signals a safe read-only operation, so the description does not need to restate that. The description adds useful scope about payment flow and libraries but does not disclose what the tool actually returns or how the information is delivered.

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 compact sentence with no filler. It could be slightly improved by adding a verb, but every phrase currently included adds relevant 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 parameterless, read-only informational tool, the description covers the essential topics: the payment flow, libraries, and authoritative terms. It does not specify the output format, but the lack of parameters and the narrow scope make this a minor gap rather than a critical omission.

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 no parameters and an empty input schema, so parameter guidance is unnecessary. The description appropriately focuses on the tool's content rather than parameters.

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 subject: the x402 payment flow, client libraries, and authoritative terms. It is not a tautology and is distinguishable from siblings like check_10q_coverage or get_product_terms, but it lacks an explicit action verb such as 'returns' or 'explains'.

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 does not state when to use this tool or when to prefer a sibling. There is no mention of scenarios for paying, searching terms, or retrieving research, so the agent must infer usage from the title and topic.

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?

Annotations only declare readOnlyHint=true. The description adds beyond that by specifying that the response covers every payable product, includes prices and payable URLs, and clarifies that all prices are USDC per call over x402. That is useful behavioral/commercial context; pagination or response shape is not disclosed, but it is acceptable for a simple list operation.

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?

One dense, front-loaded sentence states the scope first ('The full alpha402x catalog'), then enumerates the product categories and closes with the pricing detail. Every clause adds relevant information and there is 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, the description needs to convey what the response will contain, and it does: products, prices, payable URLs, and free guide pages. It could go slightly further by describing the response format or field structure, but for a zero-parameter read-only catalog listing this is sufficiently 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?

The tool has zero parameters, so parameter semantics are trivially clear and the baseline is 4. There is no schema to enrich and 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 description clearly names the resource ('alpha402x catalog'), picks a specific verb ('list'), and enumerates the content areas (empirical multibagger profiles, WACC builds, 10-Q extractions, quarterly cards). This makes it readily distinguishable from sibling tools like check_10q_coverage or get_product_terms.

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 intended use is implied strongly: an agent that needs an overview of payable research products with prices should call this tool. However, it does not explicitly contrast itself with alternatives or state when not to use it, leaving some routing to inference rather than guidance.

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