cenaodhad – ceny bytů v Praze z katastru

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

Annotations already supply readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context beyond the schema: the data source is realized sales from the Czech land registry and the metric is median CZK/m². It does not describe response format in detail, but that is less critical given the simple output.

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 primary Czech and English sentences are front-loaded and compact, stating the core behavior immediately. The keyword lists add redundancy and length, but they do not obscure the essential information or make the description unwieldy.

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 lookup with two parameters and full schema coverage, the description is largely complete: it states the input scope, the output metric, and the data source. There is no output schema, but the description explicitly says what is returned (median CZK/m²), which reduces ambiguity.

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%, and the schema already documents both parameters with descriptions and examples for lokalita and enum/default for jazyk. The description mostly restates the locality concept and adds no extra parameter semantics, so the 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 opens with the specific verb 'Vrátí' (returns) and clearly names the resource: median price CZK/m² for a Prague district, borough, or street from realized land-registry sales. This directly distinguishes it from siblings like odhad_ceny_bytu (estimate) or srovnani_ctvrti (comparison).

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 clearly communicates the intended use case: retrieving realized median prices per m² for a specified Prague locality. It does not explicitly name alternatives or say when not to use it, but the scope restriction to Prague and realized sales provides clear contextual 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 indicate non-read-only, non-idempotent behavior, but the description adds concrete behavioral details: the agent responds within 24 hours, GDPR consent is required, and there is a rate limit of 5 requests/hour. These are meaningful beyond the 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?

Two compact bilingual sentences cover the target, action, response time, consent requirement, and rate limit without filler. The structure is front-loaded and easy to parse.

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 request-submission tool with no output schema, the description provides sufficient context to select and invoke it correctly: what it does, who it contacts, when a response is expected, and what constraints apply. Required parameter details are fully covered by the 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 description coverage is 100%, so the schema already documents every parameter. The description contributes global constraints like consent and rate limit, but no parameter-specific semantics beyond what the schema provides.

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 uses a specific verb ('Odešle žádost / Sends a consultation request') and identifies the resource (Reality Style agent Petr Kubačka, Prague). It clearly distinguishes this action-oriented tool from the sibling price/comparison 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?

The description clearly signals its use case: submitting a request for a personal consultation. It does not explicitly name alternatives or exclusions, but the sibling tools are all analysis tools, so the intended context is unambiguous.

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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: the data source is the Czech land registry with realised sales, not advertisements, and the result is explicitly not an official expert valuation. This helps an agent understand the epistemic status of the result without contradicting any annotation.

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 core description is well structured and front-loaded with the main behavior, followed by a clear 'not an official valuation' caveat. However, the bilingual CZ/EN duplication and the keyword lists repeat much of the same information, so it is not as concise as it could be for an agent-facing definition.

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 estimation tool with two required parameters and no output schema, the description provides enough context: what it computes, the input variables, the data source, and the fact that it is not an official valuation. It does not specify the exact return format, but the 'indicative price range' and 'price per m²' language conveys the core output sufficiently.

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 all three parameters already have useful documentation. The description reinforces the key inputs (Prague district and area in m²) but adds no new parameter-level semantics beyond what the schema provides. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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: 'Vypočítá orientační cenové rozpětí pro byt' / 'Computes an indicative price range for a Prague apartment by district and area.' It also clearly distinguishes the result from an official valuation ('Není znalecký posudek'). However, it does not explicitly differentiate the tool from its siblings such as ceny_bytu_lokalita or srovnani_ctvrti, so it misses the top distinction criterion.

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 when the tool is appropriate: it provides an approximate, non-authoritative price estimate from realised land-registry sales rather than listings. It also explicitly warns it is not an official valuation, which is a useful when-not. However, it gives no alternative tool names or explicit conditions for choosing this tool over its siblings, leaving the usage guidance largely implicit.

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, idempotent, and non-destructive behavior. The description adds valuable context about data source (land registry, realised sales) and a key output feature (% difference vs. most expensive), which goes beyond annotations. No contradictions exist.

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 well-structured, with the core purpose front-loaded, followed by keywords. It is bilingual, which adds length but also serves multi-language users. There is some redundancy between the CZ and EN versions and keyword lists, but the essential information is concise and clear.

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 no output schema, the description should explain the full return format. It mentions the % difference but does not detail the overall output structure (e.g., whether it returns a table, individual prices, or just comparisons). The tool is simple, but for an agent to fully understand the response, more detail on output format would be beneficial.

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 both parameters (lokality and jazyk) are already documented in the schema. The description does not add extra parameter-level detail beyond the schema, so it remains at the baseline of 3. Keywords like 'price per m²' are general context, not parameter semantics.

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 purpose: comparing apartment prices across 2–8 Prague localities using realised sales from the Czech land registry, and including a percentage difference vs. the most expensive locality. This distinguishes it from siblings like ceny_bytu_lokalita (single locality) and top_ctvrti_praha (ranking), making its unique function 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 description implies usage for multi-locality comparisons but does not explicitly state when to prefer it over alternatives or mention exclusions. It does not reference sibling tools or provide explicit guidance on selecting this tool vs. others, leaving the agent to infer from the tool's specific function.

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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that the data comes from the land registry and that it returns a ranking, which is useful context. However, it does not disclose details like whether the ranking is based on realized sales or current listings, or any rate limits or data freshness. With annotations covering the safety profile, a 3 is appropriate.

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 bilingual and includes keywords, which adds length but serves a clear purpose for search/discoverability. The core functional sentence is front-loaded in both languages, and the keywords are clearly separated. It is slightly longer than necessary but well-structured and every part 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 read-only ranking tool with full schema coverage and no output schema, the description is complete enough. It explains the source (land registry), the metric (median price), and the available filters. The only minor gap is that it does not describe the return format or whether the ranking includes additional data like price per m², but the schema and annotations cover the essentials.

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. The description adds the context that the ranking is by median price and that filtering is by borough, which aligns with the parameters. It does not add new syntax or format details beyond the schema, so the baseline 3 is correct.

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 a ranking of Prague districts by median apartment price from the land registry, with a specific verb ('Vrátí'/'Returns') and resource. It also distinguishes itself from siblings by mentioning the ranking and filtering capabilities, which is enough to differentiate it from related tools like srovnani_ctvrti or ceny_bytu_lokalita.

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 the tool supports borough filtering and sort order, giving clear context for when to use it. It does not explicitly name alternatives or state when not to use it, but the sibling list and the description's focus on ranking by median price imply its specific use case. A clear exclusion or alternative mention would push this higher.

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