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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context beyond that: alias behavior, deprecation status, removal timeline, and the SEC-022 server-identity namespace note. No contradiction exists.

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 compact and front-loaded with the deprecation warning. The SEC-022 parenthetical is mildly tangential, but the overall structure is efficient and easy to scan.

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 zero parameters, an output schema, and a clear alias relationship to fiscal_status, the description covers everything an agent needs: what the tool reports, that it is deprecated, and where to route usage instead.

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 an empty schema, so the baseline is 4. The description adds no parameter-specific detail, but none is needed since the schema fully documents that the tool takes no inputs.

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 tool as a deprecated alias and states its function: 'Reports cache freshness and upstream health per dataset.' It also names the replacement, fiscal_status, which distinguishes it from the sibling 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?

It explicitly says 'DEPRECATED — use fiscal_status' and warns it will be removed in a future minor release. This gives unambiguous guidance on when not to use this tool and which alternative to select.

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 annotations already cover read-only, idempotent, and non-destructive behavior, so the description adds value by explaining the hierarchical drill-down semantics and the `note` on empty results. It also clarifies that `contains` filters a path substring. This is useful behavior beyond the structured fields.

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, all dense with useful information, and the core purpose is front-loaded. The use case and parameter examples are not filler; they directly help invocation correctness.

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 exists and annotations already carry safety and idempotency, this description covers the remaining invocation-relevant context: topic choices, hierarchy levels, contains behavior, and empty-result handling. Nothing critical is missing for this read-only drill-down tool.

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 0%, so the description carries the full burden for parameter meaning. It explains topic with concrete examples, level with hierarchy depth semantics, and contains with a path-substring filter. Year is at least mentioned as part of the core use case, which compensates for the bare 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 opens with a specific noun phrase: 'Hierarchical federal-budget breakdown for one topic and year', which names the resource and the operation. It also gives a concrete use case and topic examples, so an agent can identify what this tool returns and how it differs from sibling tools like fiscal_by_institution or fiscal_headline.

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 explains when to use it: 'see where the money goes', with a concrete example question and sample topic values. It does not explicitly name alternatives or state when not to use it, but the context is clear enough that a capable agent can 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 already declare the tool read-only and idempotent, so the description does not need to repeat that. It adds meaningful behavior beyond annotations: the data starts in 2007, and empty results carry a `note` with guidance, which is valuable operational context for an agent.

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 compact and every sentence earns its place: definition, use case, parameter guidance, and empty-result handling. It is front-loaded with the core purpose and contains no filler or redundancy.

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 read-only query tool with a rich annotation set and an output schema, this is nearly complete: it gives valid variable values, data range, an illustrative query, and empty-result behavior. The main gap is not mentioning how to discover valid `departement` values or when to prefer sibling tools like `fiscal_list_dimensions`.

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 0%, so the description must compensate. It enumerates the accepted values for `variable`, and the example 'IT spending of the Finanzdepartement since 2010?' gives practical meaning to `departement` and the year range parameters. It does not specify valid `departement` spellings, but it still adds substantial semantic value beyond the bare 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 identifies the resource (federal spending data) and the unit of analysis (department/administrative unit), and the use case clarifies it is for cross-department comparisons. However, it lacks an explicit action verb like 'returns' or 'lists' and never names sibling tools to differentiate them, so it stops short of a 5.

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 'Use case' line explicitly states when this tool is appropriate — comparing personnel, IT, or external-services spending across departments — and gives a concrete query example. It does not state exclusions or point to alternative sibling tools, so it misses the explicit when-not-to-use guidance that would earn a 5.

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, and openWorld hints. The description adds meaningful behavioral context: it includes both actuals and forward-looking years, each point carries an is_projection flag, and not every household has forward years. This goes beyond the annotations and helps the agent interpret results.

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 paragraphs with a clear lead sentence, a use case, and parameter examples. It is not overly verbose and front-loads the core purpose. It could be slightly more compact but is well-structured overall.

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 present, the description doesn't need to detail return values beyond the is_projection flag it mentions. It covers the main parameters, points to fiscal_list_dimensions for valid values, and explains the data scope. The only minor gap is the incomplete description of year_from/year_to, but the overall guidance is sufficient 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 0%, so the description must compensate. It provides example values for variable, household, and model (e.g., 'saldo', 'bund', 'fs'), which helps. However, it does not explicitly explain year_from and year_to, their defaults, or the meaning of the null values. This leaves gaps for two of the five 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 states the tool returns fiscal time series (revenue, expenditure, balance, debt ratios) from 1990 onward, with a concrete use case. It does not explicitly differentiate from sibling fiscal tools like fiscal_budget_breakdown or fiscal_by_institution, so it earns a 4 rather than a 5.

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 a specific use case ('track how a federal aggregate evolved over time') and instructs the agent to call fiscal_list_dimensions first to discover valid values. It does not list when not to use this tool or name alternatives, so it falls short of a 5 but is still clear context.

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, openWorldHint, idempotentHint, and non-destructiveness. The description adds the useful behavioral caveat that it loads all three dumps and may be slow on a cold cache, which 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?

Two short, front-loaded sentences: the first states the core purpose and scope, the second gives the use case and a performance caveat. Every sentence adds value with no redundancy.

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 an output schema, the description covers purpose, scope, use case, and a relevant performance cost. Nothing essential is missing for an agent to select and call it correctly.

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 schema has nothing to document and the description need not compensate. The description still reinforces its role by noting it produces exact filter values that feed parameters into other tools.

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 ('List') and a clear resource ('valid dimension values across all datasets'), enumerating the covered categories. It also distinguishes itself from sibling query tools by positioning this as the discovery step that builds correct parameters.

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 explicitly says when to call it first: to turn free-text guesses into exact filter values for other tools. It gives clear context for its use, though it does not explicitly name alternatives or state when not to use it.

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 cover read-only, non-destructive, idempotent behavior. The description adds meaningful context beyond that: 'Never returns empty silently; used for graceful degradation,' which is a behavioral guarantee not present in the annotations. This goes beyond the baseline.

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 extremely concise: two sentences, with the core purpose front-loaded in the first line and the use case and behavior in the second. Every sentence adds value 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?

For a zero-parameter health endpoint with a rich output schema and strong annotations, the description covers purpose, usage, and behavioral nuance. There are no missing pieces an agent would need to call it correctly.

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 description is not required to explain parameter behavior. The baseline of 4 applies, and the description adds no parameter-related information but also does not need to.

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: 'Report cache freshness and upstream health per dataset.' It also explicitly labels itself as 'the health endpoint of this server,' distinguishing it from the sibling data-querying tools like fiscal_headline and fiscal_budget_breakdown.

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 a clear use case: 'check whether the data is fresh, cached or degraded before trusting a figure.' This tells the agent when to use it, though it does not explicitly mention alternatives or when not to use it, so it stops short of a full 5.

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