Australia Cash Rate (RBA) MCP

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

With no annotations, the description carries the burden, and 'FREE' is a genuinely useful disclosure about cost/auth. However, it says nothing about data freshness, update cadence, or that the call is a safe read — gaps that matter for a rates lookup where staleness is the main risk.

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 short sentences with zero waste, and the 'FREE' qualifier is front-loaded where an agent will see it first. Nothing is repeated from structured fields.

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, annotation-free tool with no output schema, the description is adequate to identify the data but leaves the return shape and freshness unspecified. An agent knows what it gets conceptually but not the currency or format of the values.

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 description to disambiguate; the baseline of 4 applies. The description correctly implies no input is needed.

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 the specific resource and both figures returned (RBA cash rate target and interbank overnight cash rate), which is concrete enough to know it retrieves Australian rate data. It does not differentiate itself against the siblings 'column' and 'recent', whose names give no clue about overlap.

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?

Beyond the 'FREE' cost marker, there is no guidance on when to call this versus 'recent' or 'column'. No prerequisites or exclusions are stated, so the agent must guess the selection rule.

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 full behavioral burden. It discloses that the tool returns the latest value (not historical), but says nothing about matching rules, return format, rate limits, or the meaning of the 'PRO' tag. The access_token parameter hints at authentication, but the description does not explain that requirement.

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 very short and front-loaded, but 'PRO.' is unexplained jargon and the phrasing is fragmentary. It is concise without being fully structured, leaving the agent to infer what 'PRO' and substring matching imply.

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, no annotations, and two parameters, the description is too thin. It does not explain authentication requirements, what the returned value looks like, or how substring matching handles multiple columns.

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 50%: access_token is documented in the schema, while name is not. The description usefully clarifies that name is a substring match and gives an example, which adds meaning beyond the bare string schema, but it omits case sensitivity and matching behavior.

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 retrieval action: the latest value for a column name substring, with an example ('Cash Rate Target'). It is clear enough to distinguish from a specific fixed-rate tool, though the sibling tools cash_rate and recent are not named.

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?

There is no explicit guidance on when to use this tool versus cash_rate or recent. The substring hint implies a flexible lookup, but the description never states exclusions, alternatives, or the conditions that select one sibling over another.

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 behavioral burden and provides very little: it does not explain what "PRO" means, whether a subscriber token is required, whether results are ordered, how fresh the data is, or what the response looks like. The approximate count "~15" is disclosed, but that alone is thin for an unannotated 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 short and front-loaded, but "PRO." consumes the opening position without conveying usable meaning. The remaining fragment is terse but not structured enough to explain the tool's role.

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 tool with no output schema, the description gives the return scope (last ~15 observations), which is useful. It still leaves the "PRO" qualifier, authentication expectations, and result ordering unclear, so it is only minimally 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 sole parameter access_token is fully documented in the schema (100% coverage), so the schema already carries parameter meaning. The description adds no parameter-level detail, which is acceptable at this coverage level but earns only the baseline.

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?

"Last ~15 cash-rate observations" identifies the resource and the recency window, so the agent can infer it returns recent observations. However, the leading "PRO." is unexplained jargon rather than a verb, and the description does not distinguish this tool from siblings cash_rate or column.

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?

No guidance is given on when to use this tool versus cash_rate or column. The name "recent" and the phrase "Last ~15" imply a recency-limited variant, but the description never states the condition that should select it over the full cash_rate tool.

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