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

With no annotations present, the description carries the full disclosure burden. 'Research-only' conveys a read-only, side-effect-free operation, and 'when present' acknowledges optionality in the data. It lacks details on unknown-ticker behavior or potential fetch failures, but for a simple getter this is solid.

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, front-loaded sentence with no filler: the main action is stated first, with the content list in parentheses. Every word earns its place.

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-only tool with an output schema present, the description is sufficient: it names the source, target scope, data fields, and the research-only restriction. It does not discuss unknown-ticker behavior or contrast with siblings, but these are minor given the schema and sibling names.

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% and the ticker parameter already includes examples (SNTS, ORAC, ECOC), so the description adds no extra parameter semantics. The baseline of 3 applies because the schema handles the heavy lifting.

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 a specific verb ('Get') and resource ('brvm.org issuer profile') and enumerates the content: legal name, sector, listing date, and contacts when present. This clearly differentiates it from sibling tools like get_quote and get_ohlcv, which concern pricing and history.

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 'Research-only' signals the appropriate context, and the described output makes plain that this tool serves fundamental-data needs rather than market data. However, it does not explicitly name alternatives or state when not to use it, so it provides clear context without 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 provided, the description carries the full burden of behavioral disclosure. It does reveal the source (GitHub CSV archive) and the non-broker-feed status, which is important context. However, it omits other relevant behavior such as authentication needs, rate limits, error behavior for invalid tickers, or whether the data is delayed/static, leaving clear gaps.

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 concise sentences with no filler. The core action, resource, source, and key caveat are front-loaded and each clause earns its place.

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 full input schema and an output schema, the description only needs to fill domain-specific gaps. It explains the data source and research-only intent, which is useful. It stays thin on when to prefer this over get_quote, but sibling tool names and the tool's own name partially compensate.

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?

Input schema coverage is 100%, with all five parameters documented, so the baseline is 3. The description adds no additional parameter-level meaning beyond 'historical' and 'OHLCV', both already implied by the schema's period enum and tool name.

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 a specific action ('Get') on a specific resource ('historical OHLCV bars for a BRVM ticker') and identifies the data source. It clearly differentiates from sibling tools like get_quote, which suggests current quotes, so an agent can distinguish this tool without opening schemas.

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 explicit guidance about when to use this tool versus alternatives such as get_quote, list_equities, or get_issuer. The phrase 'Research-only; not a broker feed' is a caveat about the data's nature, not a routing rule. An agent must infer from the tool name that this is for historical series.

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. It discloses the HTML source and the non-executable nature, but does not elaborate on potential delays, error handling, or what constitutes an 'official-session quote.' The single-sentence description offers some transparency but leaves gaps that annotations might otherwise cover.

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 sentence that efficiently states the purpose and a critical limitation. It is front-loaded and avoids unnecessary details, making it easy for an agent to parse quickly.

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 tool with one parameter and an output schema, the description covers the essential context: what it does, the source, and its intended use. It could mention whether the quote is delayed or any specific freshness caveats, but the statement 'not an executable price feed' already implies a limitation. The presence of an output schema reduces the need to describe return values, so this is adequately 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 schema provides full coverage (100%) with a description for the ticker parameter, including examples. The tool description adds no additional parameter semantics beyond what the schema already states, so a baseline score of 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 clearly states the tool's action ('Get the latest official-session quote'), the target (one BRVM ticker), and the source (brvm.org HTML). This differentiates it from sibling tools like get_ohlcv (which likely provides OHLCV data) and get_issuer (issuer info).

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 explicit context: it is for 'Research/alerts only' and explicitly states it is 'not an executable price feed,' which tells the agent when this tool is appropriate and when it isn't. However, it does not name alternative tools for other use cases, leaving the agent to infer from sibling names.

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 burden of behavioral disclosure. It discloses that this is a status check, that it confirms research-only mode, and that adapter data may be fixtures or live. This gives the agent an accurate model of what the tool does without overstating side effects.

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 compact sentences with no filler. The key noun phrase ('Liveness and adapter status') is front-loaded, and the second sentence adds the specific checks without repeating the tool name or the schema.

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 zero-parameter health check with an output schema present, the description is complete: it identifies the purpose, the mode being validated, the adapters involved, and the fixture/live distinction. Nothing an agent needs to decide whether to call this tool 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?

The tool has zero parameters and the schema is empty, so there is no parameter burden to document. The baseline of 4 applies because the description is not required to add meaning to nonexistent parameters; the only additional value would be describing the output, which the output schema already covers.

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 a specific resource ('liveness and adapter status') and uses a concrete verb ('confirms') to state what the tool verifies. It is clearly distinct from the data-returning sibling tools like get_quote and get_ohlcv, which fetch market data rather than report system health.

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 a diagnostic use case—checking whether the service is alive and whether adapters are returning data—but it does not explicitly state when to prefer this over siblings or mention any preconditions. The role is clear enough, but the guidance is implicit rather than direct.

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 are absent, so the description carries the full burden. It adds some transparency by naming the official brvm.org source and declaring the tool as 'research-only', which hints at a read-only, non-operational context. However, it does not disclose potential rate limits, authentication needs, data freshness, or response behavior beyond the output schema.

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 sentence of 15 words, leading with the core action and resource. Every clause earns its place: source, research-only constraint, and filter capability. There is no redundancy or 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?

For a simple list tool with one optional parameter and an output schema, the description covers source, usage restriction, and filter behavior. It does not mention pagination or result limits, but the low complexity and presence of an output schema mean the description is sufficiently complete 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?

The schema already documents the query parameter fully ('Optional ticker or name substring'), and the description's 'Optional query filters by ticker or name' largely duplicates that. No additional meaning like case sensitivity, matching rules, or wildcard behavior is provided. With 100% schema coverage, a baseline of 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 starts with a specific verb and resource: 'List BRVM-listed equities from the official brvm.org quotes table'. The word 'List' distinguishes it from sibling tools like get_quote and get_ohlcv, which clearly target single-item data. The 'research-only' qualifier adds further specificity.

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 through its 'research-only' qualifier and the optional filter, but it does not explicitly guide the agent on when to use this tool versus alternatives such as get_quote or get_ohlcv. There is no when-not-to-use or alternative routing, leaving the selection largely to inference.

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