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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds useful context by mapping to GET /v1/case-law and stating the output fields per item (ecli, human_readable_citation, source_url). However, it does not disclose any additional behavioral traits such as pagination limits, rate constraints, or query syntax requirements beyond what the schema implies.

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 with no filler. It front-loads the core purpose, then provides the API mapping and key output attributes. Every sentence earns its place, making it efficient and well-structured.

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 a nested query object and an output schema, the description gives the essential action and result fields, but lacks usage guidance relative to its many siblings. It does not explain when to prefer this tool, which is a notable gap given the crowded sibling list. The description is adequate for basic invocation but incomplete for correct selection among alternatives.

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 input schema (including the query object's description listing search_term, date_from/to, size, page_index, sort) already explains the parameters. The tool description adds nothing about parameter semantics—its mention of output fields is not related to inputs. Baseline of 3 applies since the schema carries the semantic weight.

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 ('Search') and a specific resource ('German federal court decisions in NeuRIS'), making the tool's purpose unambiguous. It does not explicitly name sibling alternatives, but the specificity of 'German federal court decisions in NeuRIS' is sufficiently distinctive to separate it from broader tools like de_search or de_get_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 provides no guidance on when to use this tool versus any of the many siblings (e.g., de_rii_case_search, de_oldp_case_search). It does not state exclusions, prerequisites, or alternative tools. An agent must infer usage solely from the resource name.

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 destructiveHint=false, so the description doesn't need to repeat those safety guarantees. It adds valuable behavioral context beyond annotations by explaining that the tool's purpose is to identify gaps in the connector itself and that each gap carries a fallback. It also details the return structure (families, as-of note, gaps). This enriches agent understanding but stops short of describing edge cases (e.g., what happens if no gaps exist), so a 4 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 succinct, front-loaded with the core purpose, then usage guidance, then expected return. Every sentence serves a function: stating coverage, prescribing when to call, explaining the rationale, and outlining the return object. No fluff or redundancy. The structure is logical and efficient.

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 tool's simplicity (no parameters, output schema available), the description provides everything an agent needs: what the tool does, when to call it, why it matters (gap identification), and what to expect in the response. No missing critical information. The mention of fallbacks within gaps is a nice touch for decision-making.

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 the schema description coverage is 100% (empty object). With 0 params, the baseline is 4, and the description doesn't add parameter-specific details because there are none to document. It does explain the return semantics, which complements the output schema, but parameter semantics is not directly enhanced. The baseline holds.

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 action ('Declare') on a specific resource ('what this connector covers, how it is sourced, and what it does NOT cover'). It also differentiates from sibling search/get tools by focusing on coverage metadata rather than data retrieval. The phrase 'the absence may be a gap in this connector' further clarifies its unique role.

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?

Explicit when-to-use guidance is provided: 'Call this before telling a user that the law does not contain something, and whenever a search comes back empty.' It also explains the rationale and the fallback behavior, making the usage context unambiguous. No alternative tools are mentioned, but none are needed since this is a complementary metadata tool.

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, idempotent, and non-destructive, so the description does not need to repeat that. It adds useful behavioral nuance: it fetches exactly one entity by id and that '-text' resource variants return full document text rather than metadata.

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 a single, front-loaded sentence that conveys the core operation without any filler. It is appropriately sized for a simple fetch-by-id tool and every word contributes meaning.

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 fully documented parameters, an output schema, and annotations covering safety and idempotence, the definition provides enough for an agent to call the tool correctly. The only notable gap is the absence of explicit sibling differentiation, but that is a minor omission given the otherwise complete structured context.

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 both parameters already have rich descriptions: doc_id is tied to de_dip_search results with an example, and resource enumerates valid values with the '-text' semantics. The description itself adds no additional parameter-level detail, so the 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 states a specific action ('Fetch'), a clear object ('one entity'), and the retrieval key ('by id'), making the tool's purpose obvious. It distinguishes itself from search-oriented siblings like de_dip_search by emphasizing single-entity retrieval, though it does not explicitly name any alternative tool.

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?

Usage is implied rather than explicit: the doc_id parameter references a de_dip_search result, and the resource description directs users to choose a '-text' variant for full document text. The description does not explain when to prefer this tool over similar siblings such as de_get_text or de_get_act.

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 carry the safety profile (readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true), so the bar for additional value is lower. The description adds genuinely useful context beyond the annotations by disclosing API key handling: 'Uses the documented public API key by default (rotates ~yearly; override with DE_DIP_API_KEY)'. No contradiction with annotations exists. It falls short of explaining error behavior or rate limits, so 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 primary purpose is front-loaded and the text is compact overall. The sentence 'Legislative history... is a standard aid of German statutory interpretation' provides useful domain motivation for a legal researcher but is somewhat tangential to actually invoking the tool, keeping this from a 5.

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?

An output schema exists, so return values need no prose explanation; the input schema fully documents all parameters; and annotations cover the safety profile. The description supplies the domain scope and the API-key behavior that structured fields don't convey. The one notable gap is sibling routing (how this differs from de_search or de_dip_get_document), which is minor given the rich schema and 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 description coverage is 100% — every sub-property (titel, cursor, date_end, resource, zuordnung, wahlperiode, vorgangstyp, dokumentnummer) has a description, and the nested query object itself carries a summary. Per the rubric, high coverage earns a baseline 3. The tool description adds only marginal parameter context, mapping the covered document types to the resource enum, so it doesn't need to compensate.

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 verb-resource pair ('Search the Bundestag DIP - Germany's official parliamentary documentation') and enumerates the exact content types covered (Drucksachen, plenary transcripts, legislative procedures of Bundestag and Bundesrat). This is clear and specific. However, it does not differentiate from siblings like de_search or the several case-search tools (de_rii_case_search, de_oldp_case_search), so an agent must infer the distinction from the domain terms alone rather than an explicit boundary.

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 explicit when-to-use vs alternatives guidance is given. The description implies this is the entry point for DIP parliamentary documentation, and the note that 'Legislative history... is a standard aid of German statutory interpretation' gestures at a use case, but there is no statement of when to prefer this over de_search, de_dip_get_document, or the case-law searchers. The agent must route by inference.

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, openWorldHint=true, and destructiveHint=false, so the agent knows this is a safe read operation. The description adds no additional behavioral context such as error handling, rate limits, or behavior on missing ELI, but it does not contradict annotations either. Since annotations carry the safety profile, a 3 is appropriate – the description adds no extra value 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 a single, well-structured sentence: 'Fetch act metadata from NeuRIS by ELI.' It is front-loaded with the core action and object, contains zero waste, and is appropriately sized for a simple retrieval tool.

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 tool is simple (one parameter, read-only, has an output schema), and the description covers its essential purpose. However, it lacks any usage guidance or sibling differentiation, which is critical given the large list of sibling tools. While not seriously incomplete for a straightforward retrieval, the missing context for tool selection prevents a higher score.

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 provides a thorough description of the 'eli' parameter, including an example and accepted formats (API path or work-level ELI). The description simply says 'by ELI' without adding any new meaning. With schema coverage at 100%, the baseline of 3 is correct – the description does not enhance understanding of the parameter 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 the action (fetch), the object (act metadata), the source (NeuRIS), and the key (by ELI). It is specific and unambiguous, but it does not explicitly differentiate from sibling tools like de_get_text or de_get_decision, which might also deal with acts. Thus it is clear but lacks sibling differentiation.

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 provided on when to use this tool versus alternatives. There is no mention of when to prefer de_get_text for full text or de_search for searching. The description offers no context about preconditions or selection criteria, so an agent receives no direction on tool choice.

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 carry the full safety profile (readOnlyHint, idempotentHint, non-destructive). The description adds that this returns metadata rather than full text, which is useful context. It does not disclose anything further like pagination or missing-document behavior, but the annotations lower the burden and the metadata-vs-text distinction adds genuine value.

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?

A single front-loaded sentence with zero filler. The core purpose and identifier are stated immediately.

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 rich annotations and an output schema, this is largely complete. The main gap is that with 13 siblings, an explicit pointer distinguishing this from de_get_decision_text and the search tools would strengthen it, but the 'metadata' qualifier already does most of that work.

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% — document_number is fully described with an example ('KARE600069049'). The description merely reinforces 'by document number' without adding format or validation details, so it stays at the baseline for a fully-covered 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?

States a specific verb ('Fetch'), resource ('court-decision metadata'), source system ('NeuRIS'), and lookup key ('by document number'). The 'metadata' qualifier cleanly distinguishes it from the sibling de_get_decision_text, so an agent can tell them apart.

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 conveys it is the metadata-lookup path for a document number, which implicitly contrasts with text retrieval tools. However, it never names an alternative or states when not to use it, and there are many siblings (de_get_decision_text, de_search, de_case_search) that could be confused with it. No explicit routing guidance is given.

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 value by explaining the internal mechanism (resolving the manifestation from the encoding array) and that it downloads the text. However, it doesn't disclose potential error conditions or that the 'format' parameter affects the downloaded content. This is adequate but not rich.

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 sentences with no filler. The core purpose is front-loaded, and the second sentence adds relevant technical context without digression. Every word 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 simple two-parameter tool with high schema coverage and an output schema present, the description covers the essential behavior. It explains how the text is fetched and that it resolves from encoding. Minor omissions like format handling are already in the schema, so the description is sufficient.

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%: both 'format' and 'document_number' have descriptions in the schema. The description does not add further meaning beyond what the schema provides; it omits even mentioning the format param. Baseline 3 is appropriate since the schema does the heavy lifting.

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 ('Fetch') and resource ('full text of a court decision'), clearly distinguishing it from siblings like de_get_decision (metadata) and de_get_act (acts). It also adds a concrete detail about resolving the manifestation from the encoding array, making the purpose unmistakable.

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 provided on when to use this tool versus alternatives. It doesn't mention that de_get_decision returns metadata instead of text, nor does it suggest prerequisite steps like obtaining a document_number from a search tool. The agent is left to infer usage context from the name and schema.

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, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds valuable non-obvious behavior: how the manifestation is resolved and that format selection depends on file extension rather than Accept headers. This goes beyond annotations and helps an agent anticipate the tool's behavior without exceeding what annotations already 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?

Two sentences, no filler. The core action is front-loaded, and the behavioral caveat about format selection is stated succinctly. Every word contributes to understanding the tool's purpose and mechanics.

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 text-fetching tool with an output schema and rich annotations, the description covers the essential resolution and download behavior, plus the non-standard format selection quirk. It doesn't address failure cases or the exact return format, but the output schema likely handles that. What's missing (e.g., handling of missing formats, network errors) is minor and not required 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 coverage is 100% with good descriptions for both parameters. The description enriches this by explaining the role of 'format' in the resolution process (file extension upstream) and hints at the 'eli' being the expression-level identifier via the encoding array. This adds meaning beyond the schema's existing descriptions, so it earns above the baseline 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 states a specific verb and resource ('Fetch the full text of an act') and explains the mechanism (resolving from the encoding array and downloading). This clearly distinguishes it from siblings like de_get_act (presumably metadata) and other text-fetching tools, even without naming them explicitly.

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 implies usage—when you need the full text of an act, use this tool—but does not explicitly contrast with alternatives like de_get_act or de_get_decision_text. The mention of 'Format is selected by file extension upstream' gives a behavioral caveat but not when to choose this tool over others. It is clear for the primary use case, but lacks explicit routing 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 it read-only, idempotent, and non-destructive. The description adds value by explaining the data source (ELI agent slot) and the non-exhaustive limitation, which goes beyond the annotations. No contradiction detected.

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 and front-loaded with the primary action. It includes necessary context (NeuRIS source, origin) and a clear return type, with no redundant sentences. Each sentence 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?

Given the tool has no parameters and an output schema exists, the description fully covers what an agent needs to know: what it lists, how it is derived, its limitations, and the return structure. Nothing essential 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?

There are zero parameters, so the description does not need to elaborate on them. The baseline for zero-parameter tools is 4, and the description appropriately focuses on output rather than 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 it lists German publication organs (ELI agent codes), a specific verb and resource. It explains the derivation from ELI agent slot and names the limitation (non-exhaustive), leaving no ambiguity about what the tool does.

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 explains the context (no publisher-dictionary endpoint, derived from ELI agent slot) and the non-exhaustive nature, which implies when to use it. It does not explicitly name alternatives, but among siblings it is the only publisher-list tool, making the use case 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 read-only, idempotent, and non-destructive behavior. The description adds valuable context: it is a community aggregator (~424k decisions), not an official service, and notes the database license. This goes beyond the annotations without contradicting them, though it does not cover pagination or response details (which the output schema likely handles).

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 core purpose, followed by essential context about coverage, licensing, and tool routing. Every sentence earns its place; no fluff or repetition.

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 that an output schema exists and the annotations cover safety, the description fully equips the agent: it explains what makes this tool unique, limitations (not official service), and when to use alternatives. Nothing critical is missing for correct 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?

Schema description coverage is 100% and each parameter has its own detailed description (e.g., text full-text search, court_slug examples, exact file_number). The description itself does not add extra parameter meaning beyond what the schema provides, so 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 specific action ('Search German case law') with scope ('all court levels') and names the exact source (Open Legal Data). It distinguishes itself from RII tools by emphasizing state-court coverage and full-text search, making its unique purpose unmistakable.

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 identifies when to use this tool (state courts, full-text needs) and when not to ('prefer the RII tools for the six federal supreme/constitutional courts'). It also clarifies that it is the only source covering state courts, leaving no ambiguity about the alternative.

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, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the 'fetch full text' behavior, but this is largely implied by the name and does not enrich beyond the annotations. No additional behavioral details (e.g., rate limits, error conditions) are provided, so a baseline score 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 a single sentence with zero superfluous words. It is front-loaded with the core action and object, making it immediately scannable. Perfect conciseness for a simple tool.

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 tool is simple (one parameter, no nested objects) and has an output schema, so the description need not explain return values. It states what the tool returns ('full text of one decision') and the source. The only minor gap is that it doesn't explicitly mention the requirement for a case_ref from search, but this is covered by the schema and is sufficiently implied by the naming convention. Overall, it is complete for a straightforward fetch operation.

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 single parameter case_ref is fully documented in the input schema (numeric id or slug from search). The description does not add any extra meaning to the parameter, but since the schema already does the heavy lifting, the baseline of 3 is warranted.

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 ('Fetch'), object ('full text of one decision'), and source ('Open Legal Data'). It is clear about the resource type but does not differentiate among sibling tools like de_get_decision or de_rii_get_case_text, which also fetch decisions. The name 'de_oldp_get_case' hints at the OLDP dataset, but the description alone does not explicitly distinguish it.

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 no guidance on when to use this tool versus alternatives. It does not mention that it requires a case_ref from a de_oldp_case_search result, nor does it explain when to prefer this over other case-fetching tools. The input schema implies the relationship to search, but the description itself offers no usage context or exclusions.

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 destructiveHint=false, covering the safety profile. The description adds behavioral context beyond that by stating the sort order (newest-first) and the underlying GET endpoint mapping. This is useful but not extensive, so a 3 is appropriate given the annotation coverage.

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 sentences with no filler. The primary behavior is front-loaded and the mapping/use case is secondary. The phrase 'Useful for a law-monitoring feature' is slightly extraneous but not wasteful; it would earn a 5 if even that were removed or if it named an alternative.

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 two documented parameters and an output schema, the description is nearly complete. It explains the scope (acts since a date), ordering, and API mapping. It does not mention any exclusions or alternative usage, but those are not strictly required given the tool's simplicity and the presence of an 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 description coverage is 100%, so both parameters (since_iso and limit) are already documented with their types and meanings. The description mentions since_iso but adds no new semantic detail beyond the schema. Baseline 3 applies because the schema does the heavy lifting.

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?

States a specific verb-resource pair: 'Acts published since since_iso', with a clear sorting order ('newest-first'). The resource is 'legislation' via the mapped endpoint. It is distinct from siblings like de_search and de_get_act, but does not explicitly name or differentiate them, so it falls 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 description gives a use case ('Useful for a law-monitoring feature') which implies when to use it, but it does not explicitly state when not to use it or name alternative tools. There is no comparison with siblings like de_search, so the guidance is implied rather than explicit.

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 operation as read-only and idempotent. The description adds behavioral context beyond that: it filters over the master table of contents, has no full-text search (TOC carries no decision text), and covers specific federal courts. This informs the agent about the nature of the results and data source, which annotations do not 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?

Two sentences, no fluff. The first sentence gives the core purpose; the second packs source, coverage scope, alternative comparison, filter types, and a limitation into one well-structured sentence. The purpose is front-loaded, and every word 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 search tool with a nested query object and an output schema, the description covers the query scope, data source, completeness status, filter types, and the absence of full-text. It also differentiates from a known alternative. The agent has all necessary context to decide when to call this tool and what to expect.

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 documents all parameters with descriptions (100% coverage). The description mentions the filter fields (court, Aktenzeichen substring, date range) but adds no new semantics beyond what the schema states. It does clarify that the search operates on metadata (TOC) rather than full text, which helps interpret the parameters, but this is a general clarification, not parameter-specific detail.

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 ('Search'), a specific resource ('German federal court decisions'), and a specific source ('rechtsprech-im-internet.de (RII)'). It also distinguishes itself from NeuRIS's `/v1/case-law` by noting its completeness, making it clear which tool is which among the many search 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?

The description explicitly contrasts this tool with NeuRIS's `/v1/case-law`, calling the latter a 'small beta slice', which implies this tool is preferred for complete coverage. It also states a key limitation ('there is no full-text search'), indirectly telling the agent when to choose a different tool. While it doesn't list alternative tools by name, the context signals and sibling list provide enough to infer usage boundaries.

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 description does not need to repeat them. The description adds that the tool fetches full text from RII, which is useful context, but it does not disclose output format or any edge-case behavior. Given the rich annotations, a score of 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 a single, focused sentence that immediately communicates the tool's purpose and source. There is no filler or redundant detail.

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 single-parameter tool with complete schema coverage and meaningful annotations, the description is largely sufficient. It could mention that the input originates from de_rii_case_search, but that information is already present in the schema's parameter description.

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 provides 100% coverage for doc_id, including its origin and example format. The main description adds no additional parameter meaning beyond what the schema already specifies, so the baseline of 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 specific action ('Fetch') and a specific resource ('full text of a decision from rechtsprech-im-internet.de'). It clearly identifies the data source and content type, though it does not explicitly distinguish itself from similar sibling tools like de_get_decision_text or de_get_text beyond the RII source name.

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 does not state when to use this tool versus alternatives, nor does it provide exclusion criteria. The tool name and the doc_id description hint at usage after de_rii_case_search, but the main description itself offers no guidance on choosing this tool over sibling decision-text tools.

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, idempotentHint, destructiveHint. Description adds endpoint mapping and returned fields, providing moderate additional context but no behavioral surprises.

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 sentences: first states purpose, second maps endpoint and key return fields. No wasted words; front-loaded with core information.

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 complexity (nested object, many parameters) and rich schema/annotations, the description is adequate. Missing pagination or sorting details but schema covers those.

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 documents parameters well. Tool description adds no further parameter explanation beyond stating what fields are returned, which is already in the output 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?

Describes the tool as 'Search German federal legislation in NeuRIS', a specific verb+resource. Differentiates from siblings like de_case_search by specifying 'legislation' vs. case law.

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?

Implies use for German federal legislation but no explicit when-to-use, when-not-to-use, or alternative tools mentioned. Lacks guidance beyond scope.

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