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

Annotations already indicate readOnlyHint, destructiveHint, idempotentHint. The description adds the key behavioral trait of 'exakter Lookup ohne Fuzzy-Fallback', which clarifies the exact-match nature and absence of fuzzy search behavior. No contradiction with 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?

The description is extremely concise: two sentences plus a use-case line, front-loaded with the core action. Every sentence serves a purpose 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 simple tool with one required parameter and no output schema, the description covers the main behavior (exact lookup, use-case). Minor gaps: does not mention return format or error handling for invalid signatures, but overall adequate.

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 input schema already provides descriptions for both parameters (signature and language). The tool description does not add new parameter semantics beyond what the schema offers. With high schema coverage, baseline 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 clearly states it retrieves a single court decision by signature, with a specific use-case (detail view) and exact lookup without fuzzy fallback. It distinguishes from sibling search_court_decisions which would return multiple results.

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 guides when to use: for a detail view of a specific judgment using a signature obtained from search_court_decisions. It explicitly says it's an exact lookup without fuzzy fallback, implying when not to use. However, it does not explicitly exclude other siblings like search_by_law_reference or list_courts.

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, destructiveHint, idempotentHint, and openWorldHint. The description adds that it returns statistics, but does not disclose additional behavioral traits like rate limits or data freshness. This is adequate given 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?

The description is two sentences, front-loaded with the core purpose, and contains no extraneous information. Every sentence adds value.

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?

The description clearly explains the purpose and use case, which is sufficient for a simple statistics tool with two optional parameters. The return value is not described, but the annotations (openWorldHint) and the nature of statistics make this acceptable.

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 top-level 'params' parameter has no schema description (0% coverage), and the description does not explain the parameters at all. The inner properties have descriptions, but the description fails to compensate for the top-level lack of 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 clearly states the tool returns statistics on the number of indexed court decisions, specifying the use case for volume and distribution by canton/year. This differentiates it from sibling tools that deal with individual decisions or searches.

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 mentions the use case 'Mengengerüst und Verteilung nach Kanton/Jahr', which tells the agent when to use the tool. However, it does not provide explicit when-not-to-use guidance or alternatives.

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 the readOnly/idempotent hints, the description adds valuable behavioral details: it does not download anything, it can optionally query the Zenodo API, and it explains the ENV variable to force the dump. This is non-obvious and useful 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 concise, well-structured with a 'Use-Case' section, and front-loaded with the primary purpose. Every sentence adds useful context without 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?

Since there is no output schema, the description provides high-level output details (state, version, coverage) but could be more explicit about return format. However, for a status tool with one optional parameter, it is sufficiently 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 only parameter, check_updates, is explained both in the schema and the description, with the description adding that it queries the Zenodo version API when true. Despite the 0% schema coverage signal (which appears inconsistent with the schema having a description), the description compensates adequately for a single boolean.

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 shows the state and coverage of the offline fallback (SCD dump), specifying what it checks (presence, version, coverage, force method). It contrasts with sibling tools focused on searching/retrieving decisions, making it distinct.

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 clear use-case (transparency) and explicitly notes what the tool does NOT do (downloads nothing), which helps avoid misuse. It does not name alternative tools, but the use-case is self-explanatory and distinct from siblings.

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 and idempotentHint=true. The description adds that results are sorted chronologically and filterable, which is beyond annotations. But it omits details like pagination (though a limit parameter exists) or the structure of returned data.

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 concise with two sentences, no redundancy. It front-loads the main purpose and then provides use-case and filtering options. However, it could be slightly more structured to separate use-case from parameters.

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 list tool with no output schema, the description covers core functionality and use-case. However, it lacks details on result format, pagination behavior, and how it differs from similar search tools, which may leave some 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?

The schema already describes each parameter (e.g., 'Kanton filtern') with enumerations and defaults. The description only restates that filters are available, adding no new meaning beyond the schema. With 0% schema description coverage in the tool definition, but full descriptions in the schema itself, the baseline is 3.

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 it returns the newest court decisions and includes a use-case for tracking legal developments. It implies a difference from sibling tools like search_court_decisions by focusing on recent, chronologically sorted results, but does not explicitly differentiate.

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 use-case ('aktuelle Rechtsprechungsentwicklungen verfolgen') and mentions filtering options, which helps understand when to use it. However, it does not mention when not to use this tool or suggest alternatives among siblings.

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 description adds context beyond annotations by specifying the data source (entscheidsuche.ch) and scope (federal and cantonal courts). Annotations already declare readOnlyHint, destructiveHint, and idempotentHint, so the description complements them well without contradiction.

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 concise sentences that are front-loaded with the core action. Every word adds value, with no redundant or vague phrasing.

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 list tool with rich annotations and a clear use case, the description is fully adequate. It covers purpose, scope, and filtering without needing to detail return format or additional behaviors.

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% (based on context signals), so the description must compensate. It mentions the optional canton filter, which mirrors the schema's own description. No additional semantic detail is added, so baseline 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 clearly states the tool lists all courts indexed in entscheidsuche.ch and mentions optional canton filtering. It distinguishes itself from sibling tools which all deal with court decisions, making the purpose unambiguous.

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 clear use case ('Überblick über verfügbare Bundes- und Kantonsgerichte') and indicates when to use the canton parameter. It does not explicitly state when not to use the tool, but the context with siblings makes it obvious.

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, destructiveHint, idempotentHint, and openWorldHint. The description adds that it searches BGer decisions with optional filter, which is consistent but does not provide additional behavioral context beyond what annotations already convey.

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 short lines in German, front-loaded with key information. Every sentence is necessary and there is 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 search tool with one required parameter and no output schema, the description adequately covers the domain and main filter. It could mention the other optional parameters (date, language, limit) but they are documented in 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?

The schema already provides descriptions for all parameters (query, chamber, date_from, date_to, language, limit). The description adds the domain context and emphasizes the chamber filter, but does not add new 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 clearly states it searches specifically in Swiss Federal Supreme Court decisions (BGer/BGE), with an optional chamber filter. This is a specific verb+resource combination that distinguishes it from siblings like search_court_decisions or get_court_decision.

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 mentions the use-case 'höchstrichterliche Rechtsprechung' (highest court rulings) and optional chamber filter, providing context for when to use. However, it does not explicitly state when not to use this tool or suggest alternatives among the siblings.

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 indicate read-only and idempotent behavior. The description adds the multi-step search approach and examples, but does not detail other behaviors like pagination or error handling.

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 concise with no fluff, front-loaded with the main action, and includes helpful examples. Every sentence adds value.

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?

The description covers the core purpose and use-case but lacks details on output format, pagination, or full parameter usage. Given the absence of an output schema, more completeness 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 low (0% per context). The description does not explain parameters like date_from, date_to, language, or limit beyond examples for law_reference. It fails to compensate for missing parameter explanations.

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 searches for court decisions citing a specific legal article. It gives context and examples, but does not explicitly distinguish from sibling search tools like search_bger_decisions.

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 use-case (finding practice to a norm) and suggests synergy with fedlex-mcp, but does not specify when to use this tool versus alternatives or 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 declare read-only, non-destructive, idempotent, and open-world behavior. The description adds value by detailing the response structure (title, abstract, link, curated Markdown, and machine-readable envelope with source, license, match_type, count, total, results), which goes beyond 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?

The description is composed of two short, focused sentences followed by a bulleted list of response components. It is efficiently front-loaded with the core purpose and provides all necessary information without 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?

Given the rich annotations and detailed input schema, the description sufficiently covers the tool's purpose, usage context, and output structure. It compensates for the lack of an output schema by enumerating response fields, making the tool fully understandable for selection and 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?

The input schema already provides thorough descriptions for all parameters (query, filters, limit, etc.) with examples. The description adds no new parameter-level information beyond mentioning the supported filter types, so it provides minimal added value over 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 clearly states it performs full-text search of Swiss court decisions via entscheidsuche.ch, covering federal and cantonal courts. This verb+resource+scope effectively distinguishes it from siblings like get_court_decision (single retrieval) or search_bger_decisions (limited to federal court).

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 is explicitly described as legal research across all Swiss courts with filter support. However, it does not specify when to avoid this tool in favor of siblings (e.g., for narrower searches use search_bger_decisions), so it misses explicit alternatives.

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