ClaimHit

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

No annotations are provided, so the description carries the full burden. It explains what the tool produces ('maps patent claim elements to product features element-by-element with evidence') but lacks details about behavioral aspects like processing time, error conditions, authentication needs, or rate limits. The description doesn't contradict any annotations since none exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by explanatory details and usage guidelines. Every sentence adds value: the first defines the tool, the second explains the output, the third states requirements, and the fourth provides usage context. No wasted words.

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 2 parameters, 100% schema coverage, and no output schema, the description is reasonably complete. It explains the tool's purpose, usage, and output concept. However, without annotations or output schema, it lacks details on behavioral traits and exact return format, which could be important for an AI agent to understand fully.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description mentions the parameters ('Requires a search_id and target name') but adds minimal semantic context beyond what the schema provides, such as clarifying that search_id comes from 'a previous claimhit_search' and target_name examples. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('generate', 'maps') and resources ('AI Hit Chart', 'patent claim elements to product features'). It distinguishes from siblings by specifying it works 'from a previous search' and requires a search_id, unlike claimhit_search which presumably performs 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 provides explicit guidance on when to use this tool: 'when the user asks to chart, map claims, or analyse a specific target in detail.' It also specifies prerequisites ('Requires a search_id and target name') and distinguishes it from search tools by indicating it works with previous search results.

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 the full burden of behavioral disclosure. It states the tool checks credit balances, implying a read-only operation, but does not disclose other traits such as authentication requirements, rate limits, error conditions, or whether the data is real-time or cached. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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 a single, clear sentence that directly states the tool's function without any extraneous information. It is front-loaded and efficiently conveys the essential purpose, making it highly concise 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?

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description adequately covers the basic purpose. However, it lacks details on behavioral aspects like authentication or data freshness, and without an output schema, it does not explain the return format (e.g., numeric values, structured object). For a credit-checking tool, this leaves room for improvement in completeness.

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 parameters with 100% coverage, meaning no parameters are documented in the schema. The description does not add parameter details, which is appropriate since there are no parameters to describe. However, it could have mentioned if any implicit inputs (e.g., user context) are required, but the absence is not penalized heavily given the zero-parameter baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Check how many search credits and chart credits the user has remaining.' It specifies the verb ('check') and the resources ('search credits and chart credits'), making the function unambiguous. However, it does not explicitly differentiate from sibling tools like 'claimhit_get_history' or 'claimhit_get_result', which might also retrieve user-related data, so it falls short of a perfect score.

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 prerequisites (e.g., authentication needs), timing (e.g., before initiating a search or chart generation), or comparisons to sibling tools like 'claimhit_search' or 'claimhit_generate_chart'. Without such context, the agent lacks explicit usage instructions.

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 provided, the description carries the full burden of behavioral disclosure. It mentions the return data types but omits critical details like authentication requirements, rate limits, error handling, or whether the operation is read-only (implied by 'Get' but not stated). This leaves gaps for safe agent invocation.

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, efficient sentence that front-loads the purpose and return data. It avoids redundancy, though it could be slightly more structured by separating usage context from output 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?

Given the tool's low complexity (one optional parameter) and no output schema, the description adequately covers the basic purpose and return data. However, without annotations, it lacks completeness on behavioral aspects like safety and operational constraints, which are important for a tool accessing user data.

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%, with the 'limit' parameter fully documented in the schema (default 5, max 20). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline for high schema coverage without compensating value.

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 ('Get') and resource ('user's recent patent scans'), specifying what data is returned (patent numbers, dates, and top results from past searches). It distinguishes from siblings like 'claimhit_search' (which likely performs new searches) by focusing on historical data, though it doesn't explicitly name alternatives.

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 retrieving past search history rather than performing new searches (contrasted with 'claimhit_search' and 'claimhit_rerun_search'), but it lacks explicit guidance on when to use this tool versus alternatives like 'claimhit_get_result' or prerequisites such as authentication needs.

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 provided, the description carries full burden but only states the basic retrieval function. It doesn't disclose behavioral traits such as whether results are cached, time-limited, require specific permissions, or include pagination/format details. This leaves significant gaps for a tool that likely returns complex data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose ('Get full results') without unnecessary words. Every part earns its place by specifying the resource and required parameter context.

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 likely complexity (retrieving 'full results' from a search system), no annotations, and no output schema, the description is insufficient. It doesn't explain what 'full results' entail, potential data formats, error conditions, or usage constraints, leaving the agent under-informed for effective 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%, with the single parameter 'search_id' well-documented in the schema. The description adds minimal value beyond the schema by mentioning it's 'returned from claimhit_search or claimhit_get_history,' which provides slight context but no deeper semantics. Baseline 3 is appropriate given high schema coverage.

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 ('Get full results') and resource ('from a previous ClaimHit search'), specifying it retrieves complete output using a search ID. It distinguishes from siblings like claimhit_search (which performs new searches) and claimhit_get_history (which lists searches rather than retrieving results), though it doesn't explicitly name these alternatives.

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 context by stating 'from a previous ClaimHit search by search ID,' suggesting it should be used after obtaining a search ID from claimhit_search or claimhit_get_history. However, it lacks explicit guidance on when not to use it or direct comparisons to alternatives like claimhit_rerun_search.

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 provided, the description carries the full burden. It effectively discloses key behavioral traits: it's a read-only operation (implied by 're-run' and 'get fresh results'), has a time-based constraint (free within 6 months), and serves a monitoring purpose. However, it doesn't mention potential costs after 6 months, rate limits, or error conditions.

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 efficiently structured in two sentences: the first states the core purpose and constraint, the second provides usage guidance. Every phrase adds value, with zero redundant information, making it easy to parse and front-loaded with key 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 single-parameter tool with no annotations and no output schema, the description provides good context: purpose, usage guidelines, and a key constraint. It adequately covers the tool's role, though it could benefit from mentioning what the output looks like (e.g., fresh results format) since there's no 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%, with the single parameter 'search_id' well-documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides, such as format examples or where to find the search_id. Baseline 3 is appropriate when 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 clearly states the specific action ('Re-run a previous patent search') and resource ('patent search'), distinguishing it from sibling tools like claimhit_search (new search) and claimhit_get_result (retrieve existing results). The purpose is unambiguous 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 states when to use this tool ('when the user wants to refresh results or check if new infringers have appeared') and includes a key constraint ('Free within 6 months of the original search'), providing clear usage context and distinguishing it from alternatives like claimhit_search for new searches.

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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it runs 'multiple AI models in parallel' and 'returns ranked results with Hit Scores'. However, it doesn't mention potential limitations like rate limits, execution time, or authentication requirements, which would be helpful for a complex AI-powered 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?

The description is perfectly sized and front-loaded with the core purpose in the first sentence. Every sentence earns its place: the first states what it does, the second explains how it works, and the third provides usage guidance. There's zero waste or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (AI-powered patent infringement search with 4 parameters) and the absence of both annotations and output schema, the description does well but has gaps. It explains the purpose, method, and usage context effectively, but doesn't describe the output format or potential limitations. For a tool with no output schema, some indication of return format would improve completeness.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions the tool's function but doesn't explain parameter interactions or provide additional context about how parameters affect the search. Baseline 3 is appropriate when 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 clearly states the specific action ('Search for products or technical standards that potentially infringe a given patent'), identifies the resource ('patent'), and distinguishes this tool from siblings by specifying its unique function of running AI models to find infringers. It explicitly mentions what makes it different from other tools on the server.

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 explicit guidance on when to use this tool ('when asked to find infringers, check infringement, or screen a patent'), which gives clear context for its application. It doesn't mention when not to use it or alternatives, but the explicit 'when' statements are sufficient for full credit.

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