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

No annotations are provided, so the description bears the full burden. It describes the tool as performing analysis (not modification) and lists the data it analyzes. It does not explicitly state that it is read-only or safe, but the context of 'analysis' and 'recommendations' implies non-destructive behavior. It lacks details on authentication, rate limits, or side effects, but is sufficiently transparent for an analysis tool.

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 relatively concise, with a clear structure: first a paragraph on what the tool does, then a line differentiating from a sibling, then parameter explanations. It front-loads the key purpose. However, the German text is slightly verbose in the first line, but overall 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 complexity (multi-dimensional landscape analysis), the description covers its purpose, key outputs (structured analysis, recommendations), and parameter details. There is no output schema, but the description states it returns structured analysis. It also distinguishes from a key sibling among 14 tools. It is sufficiently complete for an agent to understand usage.

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 has 0% description coverage, but the tool description includes an 'Args' section that explains both parameters: 'query' as a technology search term with examples, and 'years' as the analysis period with range (1-20) and default (5). This adds substantial meaning beyond the schema's type-only definitions.

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 does a comprehensive landscape analysis ('Umfassende Landschaftsanalyse') for a technology area, listing specific aspects like top assignees, trends, IPC/CPC codes, geographic distribution, and competitive intensity. It distinguishes itself from the sibling tool 'get_patent_landscape' by emphasizing structured insights and recommendations over raw data.

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 differentiates from 'get_patent_landscape' by noting it focuses on structured analysis insights and gives recommendations, which guides when to use this tool vs the sibling. It implies use for strategic decision-making ('ideal für Reports und strategische Entscheidungen'), but does not specify when not to use it or mention 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?

Given no annotations, the description carries the full burden. It mentions 'uses parallel API queries for fast results' (performance trait) and describes outputs. However, it does not disclose potential limits (e.g., rate limiting) or any side effects. Adequate but not comprehensive.

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?

Description is concise, with a clear purpose sentence followed by output details and argument list. It is front-loaded and every sentence adds value. Slightly longer than necessary but still 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 no output schema and two parameters, the description sufficiently explains inputs and outputs. It mentions what the tool returns, which is enough for an agent to understand its use. No major 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 has no descriptions (0% coverage), so the description adds essential context: query is a technology search term with examples, years has a range (1-20) and default (5). This compensates well for the missing schema descriptions.

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 analyzes patent application trends for a technology, listing specific outputs (number per year, growth rate, top applicants). It distinguishes from sibling tools like analyze_patent_landscape (broader landscape) and get_top_patent_holders (just top holders).

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 guidance on when to use this tool versus siblings. While the purpose is clear, it does not mention when to prefer it over analyze_patent_landscape or search_patents, leaving the agent without comparative 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?

No annotations provided, so the description bears full weight. It discloses the output includes total number, growth trend, and newest patents, but does not mention read-only nature, data source, rate limits, or any side effects. The behavioral traits beyond purpose are minimal.

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 brief, front-loaded with the main purpose, and structured with a clear list of parameters. Every sentence serves a purpose, and there is no unnecessary verbosity. The 'Args' section is slightly redundant but acceptable.

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 mentions what the tool shows (total, trend, newest patents), which provides a basic idea of the output. However, it does not specify the data format or details like ordering. For a simple comparison tool with 3 parameters, this is largely sufficient but could be more explicit.

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?

Input schema has 0% description coverage, so the description adds meaning. It provides examples for company_a and company_b (e.g., 'Apple'), and specifies the range and default for years (1-10, default 5). This compensates for the schema's lack of descriptions.

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 compares patent portfolios of two companies and lists what it shows (total count, growth trend, newest patents). The verb 'vergleicht' and resource 'Patent-Portfolios' are specific. It distinguishes from sibling tools like 'analyze_patent_landscape' which are broader.

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 guidance on when to use this tool versus alternatives. The purpose implies it's for pairwise comparisons, but no statements about when not to use it or alternatives. The context from sibling tools somewhat differentiates, but the description itself lacks usage guidelines.

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?

With no annotations, the description carries full burden. It discloses that the tool returns both forward and backward citations and computes an impact score. However, it does not mention data freshness, pagination, or authorization requirements, which would enhance transparency.

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-loading the primary purpose in the first sentence. Every sentence adds value, and there is no redundant or irrelevant information. It is well-structured for quick comprehension.

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 core functionality (citation network and impact score) but lacks detail on output format, impact score characteristics (e.g., range, interpretation), or any limitations. Given the tool's simplicity and lack of output schema, more context would improve usability.

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 has one required parameter (patent_number) with no description. The description's 'Args' section provides an example ('11234567'), adding some meaning, but does not specify format details (e.g., whether leading zeros or country codes are needed). With 0% schema coverage, the description partially compensates but could be more precise.

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 retrieves a patent's citation network, showing both cited and citing patents, and calculates an impact score. It uses a specific verb ('get citation network') and resource (patent), effectively differentiating it from sibling tools like get_patent_details or get_patent_family.

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 describes what the tool does but provides no guidance on when to use it instead of alternatives. It implies usage for citation analysis but doesn't explicitly state contexts or exclusions, leaving room for ambiguity.

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?

No annotations exist, so the description carries the full burden. It only states 'extrahiert' (extracts), implying a read operation, but does not explicitly confirm idempotency, safety, permissions, or any side effects.

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 relatively concise at two sentences plus an args section. The first sentence front-loads the core purpose. The educational note about claim types is informative but could be considered slightly extraneous for a tool description.

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 one parameter and no output schema, the description covers the input adequately. However, it lacks usage guidelines and behavioral details, making it minimally complete but with clear 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 has 0% description coverage, but the description adds examples and format guidance for patent_number (e.g., '11234567' or 'US-11234567'), which significantly aids correct invocation for the single parameter.

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 extracts patent claims (Patentansprüche) and explains what claims are. It implicitly distinguishes from siblings like get_patent_details by focusing specifically on claims, but does not explicitly contrast with similar 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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention when not to use it or suggest sibling tools for other patent information.

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?

No annotations provided, so description carries full burden. It lists what the tool returns but does not disclose any behavioral traits (e.g., read-only, authentication, rate limits, or error handling). Minimal transparency beyond output 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?

Extremely concise: two short sentences that first state purpose and outputs, then define the only parameter. No redundant information; every sentence is valuable and well-structured with proper front-loading.

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 (1 parameter, no output schema, no annotations), the description adequately covers what the tool does, what it returns, and how to use the parameter. Could mention error behavior (e.g., patent not found) but not essential.

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 0%, but description compensates with a clear explanation of the patent_number parameter including example formats ('11234567' or 'US-11234567'), adding meaningful guidance beyond the schema's type constraint.

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?

Clearly states it gets complete patent details and lists returned fields (title, abstract, inventor, assignees, etc.), distinguishing it from sibling tools that focus on analysis, comparison, or specific aspects like claims or citations.

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 usage when full details of a specific patent are needed, but does not explicitly state when not to use it or provide alternative tools (e.g., get_patent_claims for claims-only queries). The description lacks usage exclusions or context-appropriate 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?

No annotations are provided, so the description must fully convey behavior. It mentions what is returned (family members, continuations, divisionals, assignee citations) but lacks details on data source, pagination, or limitations. It adequately sets expectations for a read operation.

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 with three sentences plus a parameter line. Every sentence is informative, and the structure is clear and front-loaded.

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 (one parameter, no output schema), the description covers the essential functionality. It explains what family members are and how they are identified. Minor omission: it does not specify the output format (e.g., list of patent numbers or full patent objects).

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 has no description for patent_number (0% coverage), so the description compensates by providing format examples (e.g., '11234567' or 'US-11234567') and explaining it is a patent number. This adds meaning 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 clearly states the tool finds related patents in the same patent family, specifying continuations, divisionals, and assignee-cited patents. This distinguishes it from siblings like get_patent_citations or get_patent_details.

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 finding family members but does not explicitly state when not to use it or provide alternatives among siblings like get_patent_citations or search_patents. Context of use is implied but no 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?

With no annotations, the description carries the full burden of behavioral disclosure. It describes the output components but fails to disclose any behavioral traits such as side effects, auth needs, rate limits, or how results are returned. This is insufficient for a mutation-like tool (creating a landscape).

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 two main sentences plus the Args list. It is front-loaded with the purpose and then lists components. No wasted words, though it could be slightly more streamlined.

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 complexity of the tool (multiple analyses combined) and no output schema, the description should explain the return format or structure of the landscape. It lists components but does not describe how they are presented. This is a gap for agent 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 schema coverage is 0%, but the description's Args section provides detailed semantics: query includes examples, years specifies range 1-20 with default 5, top_n specifies range 1-20 with default 10. This fully compensates for the lack of schema descriptions, adding constraints and examples.

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 that the tool creates a comprehensive technology landscape for a field, listing specific components like trend analysis, top players, CPC classification, etc. This provides a specific verb+resource combination that distinguishes it from siblings, though explicit differentiation is not given.

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 it is ideal for strategic patent research, due diligence, and market analysis, providing some context. However, it does not explicitly state when to use this tool versus alternatives like analyze_patent_landscape or search_patents, nor does it include 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?

No annotations exist, so the description must disclose behavior. It does not mention output format, pagination, rate limits, or whether it is a read operation. It only states it shows a ranking, which is insufficient.

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 a main purpose statement, usage context, parameter details, and a list of sectors. It is fairly concise and front-loaded with the key function.

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 two simple parameters and no output schema, the description covers the main aspects. However, it lacks details about the output structure (e.g., fields returned), which would be helpful for an agent to interpret results.

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%, but the description adds meaning by specifying limit range (1-50, default 20) and listing all sector options with brief explanations. This compensates for the missing schema descriptions.

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 ranks companies by patent count, optionally filtered by sector. This distinguishes it from other patent tools that focus on individual patents or different aspects.

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 usefulness for competition analysis and identifying dominant players, but does not explicitly guide when to use this tool versus alternatives like analyze_patent_landscape or search_by_assignee.

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?

No annotations provided, and description mentions only searching assignee database without disclosing behavior like result format, matching logic, or rate limits. Insufficient for a search tool.

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?

Short and efficient description with no wasted words. Two sentences plus docstring-style Args. Front-loaded purpose.

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?

Adequate for a simple search tool, but missing output format details and any mention of sorting or filtering. No output schema exists to compensate.

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 has 0% description coverage, but description adds value by exemplifying company_name and specifying limit range and default, compensating for schema gaps.

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 patents by assignee (company or organization) and differentiates from sibling tools like search_by_inventor or search_by_classification.

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?

Provides examples of company names and limit range, implying usage context, but lacks explicit guidance on when to use vs alternatives or prerequisites.

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?

No annotations are provided, so the description carries full burden. It mentions support for all classification levels and lists popular codes, but does not explicitly state that it is a read-only operation, nor does it cover side effects, permissions, or output format. The behavioral transparency is partial but adequate.

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: first sentence states purpose, second compares with sibling, third lists supported levels, then a helpful list of popular codes, and finally parameter descriptions. Every sentence earns its place, and it is front-loaded with essential 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?

The description covers input parameters in detail but lacks information about the output format, pagination, or any constraints like rate limits. Given no output schema, the absence of return value description makes it less complete for a search 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 coverage is 0%, but the description compensates well. It explains ipc_code with examples and mentions popular codes, and explains limit with range and default. This adds significant meaning beyond the schema's minimal type definitions.

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 'Sucht Patente nach IPC/CPC-Klassifikationscode,' clearly specifying the verb (search) and resource (patents by classification). It also distinguishes from the sibling tool search_by_cpc by noting it uses subgroup level for detailed searches, making the purpose specific and differentiated.

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 'Präzisere Suche als search_by_cpc — nutzt Subgroup-Ebene für detaillierte Technologie-Recherchen,' directly indicating when to use this tool over the sibling. It implies that for broader searches, search_by_cpc should be used, providing clear usage 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?

No annotations are provided, so the description carries full burden. It fails to disclose any behavioral traits such as authentication needs, rate limits, or side effects. It does not specify that it is a read-only operation or what the response format looks like.

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 a clear overview of CPC sections and parameter explanations. It is fairly concise but could be slightly tighter by removing redundant phrases like 'Nützlich für gezielte Technologie-Recherchen' which is implied.

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 complexity and numerous sibling tools, the description is moderately complete. It lacks output schema and behavioral details, and does not explain what the results contain or how they are formatted. However, it does cover the basic functionality and parameter usage.

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 description adds significant meaning beyond the schema: for cpc_code, it provides examples and relevant codes; for limit, it explains maximum results (1-50) and default value (10). Since schema description coverage is 0%, the description adequately compensates.

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 patents by CPC classification code, provides an overview of CPC sections, and gives specific examples. It distinguishes itself from sibling tools by specifying the search method (by CPC) and explains the purpose for targeted technology 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 explains what CPC is and when to use it (for targeted technology searches). While it doesn't explicitly differentiate from similar siblings like search_by_classification, it provides enough context for an agent to understand its specific use case.

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?

No annotations provided, so the description bears full burden. It mentions supporting full or last name but omits behavioral traits like what happens on no results, error handling, or rate limits. Lacks side-effect disclosure.

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?

Extremely concise: two short introductory lines then an args list. Every sentence adds value, no fluff. Well-organized with clear separation between general behavior and parameter details.

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 2-parameter tool with no output schema or annotations, the description fully covers input semantics and basic behavior. Could optionally mention output format but is otherwise 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?

With 0% schema description coverage, the description fully adds meaning: examples ('Elon Musk' vs 'Musk'), meaning (inventor name), and range (limit 1-50, default 10). This goes well beyond the schema's bare types.

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 patents by a specific inventor, using 'Sucht Patente eines bestimmten Erfinders.' It also specifies support for full name or last name, which is specific and distinguishes it from sibling tools like search_by_assignee.

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 guidance on when to use this tool versus alternatives (e.g., search_patents). The description implies use when searching by inventor name but does not provide when-not-to-use or mention other search options.

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?

No annotations exist, so the description carries the burden. It discloses that only US patents are supported (via PatentsView) and that the country parameter is limited. However, it does not state whether the operation is read-only or discuss rate limits, pagination, or error behavior.

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 relatively concise with a short intro and a clear Arg list. However, it mixes German and English, which slightly reduces clarity. The structure is functional but could be more streamlined.

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 return values or results format, which it does not. It covers input well but lacks completeness for a full understanding of the tool's behavior and output.

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 has 0% description coverage, but the parameter descriptions in the text add crucial details: query examples, country limitation, limit range, and date format. This fully compensates for the schema's lack of descriptions.

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 that the tool searches patents by keywords in title and abstract, specifically for US patents. This distinguishes it from other search tools that focus on assignees or classifications, though it doesn't explicitly contrast with siblings like 'search_recent_patents'.

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 guidance on when to use this tool versus alternatives. The mention of optional date filters hints at use cases for time-range searches, but there is no discussion of when not to use it or which sibling tools are better suited for other scenarios.

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?

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only describes what the tool does but not traits like rate limits, data freshness, or whether results are sorted by date. This lack of transparency makes it harder for an agent to anticipate behavior.

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 well-structured, with a clear purpose statement followed by a bulleted parameter list. Every sentence is necessary and front-loaded, making it easy for an agent to parse quickly.

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?

While parameter explanations are complete, the description lacks context on return format, sorting logic, and whether 'recent' means grant date or publication date. Given no output schema and no annotations, more contextual completeness would improve usability.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining each parameter: query with examples, days with time range and defaults, limit with min/max, and category with CPC examples. This adds significant 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 clearly states the tool searches for recent patents in a technology area, distinguishing it from general patent search tools. It mentions focusing on recently granted patents and optional category filter, but could be more precise about what 'recent' means (e.g., grant date vs. publication date).

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 indicates the tool is useful for finding current innovations and new developments, implying it should be used for time-sensitive searches. However, it does not explicitly specify when not to use it or compare it with siblings like search_patents or search_by_cpc, leaving room for ambiguity.

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