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

The description goes well beyond the readOnlyHint/openWorldHint annotations by disclosing what the call returns (latest rates plus spread stats), how stale quarterly publishers are handled, that missing rates are cross-computed only within the same bank's table, and that banks are never mixed. It even links to a methodology page.

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 but well-organized: use case and examples first, then return shape, then edge-case handling, then methodology link. Every sentence adds relevant behavioral info, though it is slightly longer than the minimum needed.

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 compensates by enumerating the return contents and stats fields, and by explaining staleness and cross-computation rules. It does not spell out the exact JSON shape or what 'covered bank' means, but the methodology link and sibling list_central_banks fill most gaps.

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%, with both source and target fully documented by ISO 4217 patterns and examples. The description reinforces the 'currency pair' concept but adds no new parameter-level meaning beyond the schema, so the baseline 3 applies.

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 clear verb and resource: compare one currency pair across ALL institutions at once. It gives concrete example queries and emphasizes the cross-bank scope, which distinguishes it from per-bank or per-pair siblings like get_official_rates.

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 opens with 'Use this when the user wants one currency pair across ALL institutions at once' and provides example user phrasings. It does not explicitly name alternative tools or state when not to use it, but the use-case framing is clear enough for an agent to route correctly.

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 cover readOnly and openWorld hints, so the bar is lower, yet the description still adds high-value non-obvious behavior: the exact return shape since there is no output schema, the paid-plans-only requirement, and the volume-based billing model (one API call per month of history). The BILLED BY VOLUME warning is exactly the kind of behavioral context an agent needs to avoid costly misuse.

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?

Six dense sentences, each earning its place: purpose, examples, return shape, parameter alternatives, defaults, billing warning, and sibling routing. The most important scoping info is front-loaded, and the description is structured logically from what → output → how → constraints. No filler or repetition of schema content.

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 6-parameter tool with no output schema, the description is complete: it provides the return format inline ({ series: [{ date, rate, rate_type, derived, method }] }), explains parameter alternatives and defaults, discloses billing cost that shapes range choices, and routes to get_publication_calendar for a related use case. Annotations cover safety (readOnly) and world-data behavior (openWorld). Nothing an agent needs to call it correctly 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?

Schema coverage is 100%, so baseline 3 applies. The description does add modest semantic value beyond the schema by explaining the either/or relationship between `symbol` and source+target ('Give either symbol... or a source+target pair') and clarifying that symbol 'matches either side of the pair vs the bank's home currency'. This clarifies rather than replaces the schema, which remains the primary 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 opens with a precise verb+resource+scope: 'Use this for a date-by-date series of one bank's OFFICIAL rates' and grounds it in concrete examples ('ECB USD rate for every day of 2025'). It implicitly distinguishes from siblings through 'one bank's' (vs compare_official_rates) and 'date-by-date series' (vs get_official_rates), and explicitly from get_publication_calendar. An agent can tell exactly what this tool is for.

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 states the when explicitly ('Use this for a date-by-date series...'), gives a direct when-not with an alternative ('For publication dates without values (free) use get_publication_calendar'), and adds hard operational guidance that affects invocation decisions ('keep ranges as narrow as the question needs' due to per-month billing). This fully routes the agent across the sibling space.

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 mark readOnly and openWorld, and the description adds behavior beyond that: weekend/holiday rollback to the last published date, a derived flag when cross-computed, paid-plan gating for date, and the response's rate_date field. This gives the agent operational knowledge not inferable from annotations.

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?

Four dense sentences, front-loaded with the purpose and followed by examples, parameter semantics, and an alternative. No filler or redundancy; each sentence adds a distinct operational fact.

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, the description covers the return shapes ({rates:[...]} and derived flags), edge cases (weekends/holidays, paid plans), input selection, and the alternative for live rates. Sibling tools are not explicitly routed, but the scope is defined clearly enough that history/comparison/calendar tools are not confused with this point-in-time official rate lookup.

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?

With 100% schema coverage, the baseline is 3, but the description meaningfully enriches each parameter: bank codes are lowercase abbreviations (call list_central_banks first), date is optional and requires a paid plan for specific days, and source/target must be given together to narrow to one pair. It also explains output shape implications, which the schema does not.

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 leads with a specific verb and resource: getting official published rates from a specific institution. Concrete examples ('ECB rate for USD', 'HMRC rate for invoicing') and the explicit contrast with live market rates make the tool's purpose unmistakable and distinguish it from other rate-related tools.

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?

Explicitly says when to use this tool ('Use this for OFFICIAL rates...') and when not ('NOT live market rates'), and names the alternative package for live mid-market rates. It also gives parameter-based guidance: omit date for latest, supply date for a specific table, omit source/target for full table, provide both for one pair.

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 readOnlyHint and openWorldHint, so the read-only nature is covered. The description adds valuable behavior: it returns only dates, caps large ranges and flags them with `truncated`, and interprets gaps as weekends/holidays/cadence. This goes beyond the annotations without contradicting them.

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 sentences, front-loaded with the primary use case and examples, then constraints and caveats. No wasted words; every sentence carries semantic weight.

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 read-only tool with no output schema, the description covers the key operational concerns: what is returned (dates only), what is excluded (rate values), and how range limits behave (truncation). It could be more explicit about the exact output shape, but the 'dates only' statement is a reasonable substitute given the missing output schema.

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%, so baseline is 3. The description strengthens parameter understanding by prescribing '?year=YYYY or a from/to range', noting `year` is an alternative to from/to, and warning that large ranges are capped. This is genuine added meaning 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 a specific action and resource — 'see WHICH dates a bank actually published a rate table' — and gives concrete example questions. It explicitly differentiates itself from history/rates tools by stating it 'Returns dates only, no rate values', so an agent can distinguish it from siblings.

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 frames the tool with use cases ('did the ECB publish on 2026-01-01?', 'how often does the US Treasury publish?') and notes it is 'available on every plan (unlike history)'. It doesn't name every sibling alternative or spell out when to choose get_official_rate_history or compare_official_rates, so it stops short of full when-not guidance.

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 mark this as read-only, and the description adds useful behavioral context: it returns a specific shape, covers 60+ institutions, includes tax authorities as well as central banks, and notes that it is cheap to call. This is more than annotations alone 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?

Three focused sentences with the most important instruction front-loaded. Every sentence adds value: when to call, what is returned, and how the result connects to other tools.

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 no-parameter, read-only discovery endpoint, the description is complete enough even without an output schema. It names the key fields of the response, explains coverage, and gives concrete usage triggers.

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 the baseline is 4. The description correctly focuses on what the response provides rather than parameter details, and the input schema already confirms no arguments are 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?

The description clearly states a specific verb and resource: listing covered central banks and tax authorities. It also distinguishes this discovery tool from rate/history/calendar siblings by emphasizing it is the lookup for bank codes.

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?

Explicitly instructs to call this FIRST when a bank code is needed, and shows example user queries that should trigger it. It also explains that the returned `code` is the parameter used by every other tool, making the relationship to alternatives explicit.

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