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

Beyond the annotations, the description discloses key behavioral traits: it consumes one unit of the monthly AuraStream allowance, returns historical/delayed data rather than live quotes, and notes that the last bar may still be forming. These are exactly the caveats an agent needs before calling the tool.

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 three short sentences, front-loaded with the purpose and followed by quota and data-quality caveats. Every sentence adds useful information with no wasted text.

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 an output schema, full parameter documentation, and annotations already covering safety and idempotency, the description supplies the remaining quota and delayed-data caveats. It could explicitly name get_timeseries as the alternative, but it is otherwise complete enough 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 100%, so the schema already documents all four parameters including enums and defaults. The description reinforces that bars are returned per ticker but adds no syntax or constraints beyond what the schema provides, so the 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 states a specific verb and resource: return the most recent N OHLCV bars per ticker. The phrase 'without having to pick dates' implicitly contrasts with get_timeseries, but it does not explicitly name the sibling, so the differentiation is clear but not fully explicit.

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 'without having to pick dates' signals the convenience scenario, but the description never names get_timeseries or states when not to use this tool. The alternative is left to inference rather than explicitly routed.

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?

Beyond annotations, it discloses CSV return shape, per-ticker structure, truncation to the most recent max_rows rows with total count noted, monthly allowance consumption, and environment-based credential handling. These are meaningful behavioral details that annotations alone do not provide.

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 front-loaded with the core action and return format, then covers truncation, cost, and credentials. Every sentence contributes operational information without excess.

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 output schema and 100% parameter description coverage, the description supplies the key missing operational context: CSV output, truncation behavior, allowance cost, and credential handling. An agent has enough information to call it correctly and route source-specific details to list_sources.

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 parameter semantics are already largely handled by the schema. The description adds useful context for max_rows truncation and source-specific field discovery via list_sources, but it does not materially explain the many source-specific 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 states a specific verb and resource: fetch historical AuraStream data and return it as a CSV table per ticker. It also distinguishes the historical nature from the sibling get_latest and points to list_sources for source-specific fields.

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 clearly establishes this tool as the historical-data fetch path and references list_sources for source-specific fields. It does not explicitly contrast with get_latest, but the historical framing and sibling naming make the usage context 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?

Annotations already declare readOnly/idempotent, so the remaining value is cost and prerequisites, and the description delivers both: 'Free to call (does not use the API allowance)' and a note that it reports whether provider credentials are needed and configured. That is exactly the kind of context annotations cannot express. It does not state pagination or ordering behavior, keeping it at 4.

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 dense sentence, front-loaded with the verb and resource, and every clause carries information (formats, fields, credentials, cost). The enumeration is long but none of the items is 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 zero-parameter discovery tool with a full output schema, the description covers purpose, cost, and credential prerequisites, which is nearly everything an agent needs. The only gap is an explicit instruction on when to prefer this over calling get_timeseries directly.

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 takes zero parameters, so there is nothing for the schema to document and the baseline is 4. The description still uses the space usefully by explaining that some sources have source-specific parameters, which prepares the agent for what the output will contain.

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?

Names a specific verb+resource ('List the AuraStream data sources') and then enumerates exactly what the listing contains: coverage, ticker format, intervals, dtypes, optional source params, non-OHLCV fields, and credential status. That is far more specific than siblings get_timeseries/get_latest, which retrieve data rather than describe it.

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 subject matter makes the when-to-use clear (discover sources, formats and credential requirements before fetching), and being a zero-parameter catalog it has no per-call conditions to state. It does not explicitly say 'call this before get_timeseries' or name the alternatives, so it stops short of a 5.

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