ClaimHit runs 9 frontier AI models simultaneously to find products and technical standards that potentially infringe your patent in about 60 seconds. Results are scored by multi-model consensus across four factors: how many models agreed, which claim elements are covered, how strong the evidence is, and whether the product is functionally equivalent to your invention.
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Average 4.1/5 across 6 of 6 tools scored.
Tools are mostly distinct: search, rerun, get_result, and get_history all deal with searches but have clear purposes. The slight overlap between get_result and get_history (both return past search data) could cause minor confusion, but descriptions clarify the difference.
All tools share the claimhit_ prefix, and most follow a verb_noun pattern (get_credits, get_history, generate_chart, rerun_search). The bare 'claimhit_search' deviates from the verb_noun pattern, but overall naming is predictable and readable.
With 6 tools, the server is well-scoped for patent infringement analysis. Each tool serves a clear function in the workflow: search, refine, retrieve, chart, and manage account.
The core lifecycle is covered: search, rerun, retrieve results, and generate charts. A minor gap is the lack of a dedicated tool to list or retrieve previously generated charts, but the existing tools allow agents to work around this.
claimhit_generate_chartGenerate claim chartAInspectGenerate an AI Hit Chart for a specific target (product or standard) from a previous search. A Hit Chart maps patent claim elements to product features element-by-element with evidence. Requires a search_id and target name. Use this when the user asks to chart, map claims, or analyse a specific target in detail.
| Name | Required | Description | Default |
|---|---|---|---|
| search_id | Yes | The search ID from a previous claimhit_search | |
| target_name | Yes | The exact name of the product or standard to chart e.g. "Samsung Galaxy S25" or "5G NR Release 17" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and destructive hints (readOnlyHint: false, destructiveHint: false). The description adds context by explaining the output nature ('maps patent claim elements to product features element-by-element with evidence') and the prerequisite of a previous search. However, it does not disclose side effects like credit consumption or result persistence, which are likely relevant given the sibling tools.
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 three sentences: it states the main action, explains what a Hit Chart is, and provides usage context. Every sentence adds value, with no redundancy or irrelevant details. It is front-loaded 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?
The tool has no output schema, so the description should explain what the return value or result of generation is. The description says 'Generate an AI Hit Chart' but does not clarify whether the chart is returned directly, stored for later retrieval (e.g., via claimhit_get_result), or if there are follow-up steps. Given the sibling set includes get_result, this is a notable gap. Still, the description covers the main action, prerequisites, and when-to-use, making it adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters well-described. The description adds no new parameter-level details beyond restating that search_id comes from a previous search and target_name is the exact name. Since the schema fully covers the parameters, the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Generate') and resource ('AI Hit Chart') for a target from a previous search. It explains what a Hit Chart is (maps claim elements to features), which distinguishes it from sibling tools like search or get_result. The scope is well-defined.
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 'Use this when the user asks to chart, map claims, or analyse a specific target in detail,' providing clear when-to-use guidance. It does not explicitly name alternatives, but the context is sufficient for an agent to differentiate it from search or retrieval operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
claimhit_get_creditsCheck ClaimHit creditsACheck how many search credits and chart credits the user has remaining.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful context by specifying the two credit types (search and chart), which goes beyond the title. No contradictions or missing side-effect warnings.
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 that is front-loaded with the verb and resource. Every word earns its place, with no fluff 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 simplicity (no parameters, no output schema, clear read-only intent) and the strong annotations, the description is complete enough for an agent to select and invoke it correctly. It clearly states what the tool returns (credit counts) without needing additional detail.
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, so the input schema is empty; the description fully compensates by explaining what is being checked (remaining search and chart credits). This gives the agent complete understanding of the tool's purpose without needing parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('check') and resource ('search credits and chart credits'), clearly distinguishing this tool from siblings that generate charts, retrieve history, run searches, or rerun searches. The scope is precise and immediately understandable.
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 makes it obvious this tool is for checking remaining credits, which is distinct from the other tools' actions. It doesn't explicitly state when not to use it or name alternatives, but the context makes the usage clear, and no exclusions are needed for such a simple read-only query.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
claimhit_get_historyRecent patent scansAGet the user's recent patent scans. Returns patent numbers, dates, and top results from past searches.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of recent searches to return (default 5, max 20) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds that it returns patent numbers, dates, and top results, giving some behavioral context, but does not elaborate on ordering, time window, or other details.
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?
Single sentence, front-loaded with verb and resource, no redundant words. It is approximately 14 words, making it perfectly concise and easy to scan.
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 one-parameter history tool with comprehensive annotations and schema coverage, the description sufficiently communicates functionality and return fields. There is minor ambiguity around 'top results', but overall it is complete enough for an agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'limit' is fully documented in the schema (default 5, max 20), so the description does not need to add anything. Schema coverage is 100%, meeting the baseline of 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 uses a specific verb 'Get' and resource 'user's recent patent scans', and explicitly lists return contents (patent numbers, dates, top results). This clearly distinguishes it from sibling tools like search (new searches) and get_result (specific result).
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 establishes clear context as a history/listing tool, implying it should be used for viewing past scans rather than performing new ones. However, it does not explicitly name alternatives or provide exclusion criteria, but the context is strong enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
claimhit_get_resultGet full search resultsAGet full results from a previous ClaimHit search by search ID.
| Name | Required | Description | Default |
|---|---|---|---|
| search_id | Yes | The search ID returned from claimhit_search or claimhit_get_history |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds the behavior that it returns 'full results' from a prior search, but it does not disclose additional traits like pagination, limits, or error states. Without annotations, this would be insufficient, but with them it meets the minimum viability for a read-only retrieval 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 a single sentence that conveys the purpose and the key requirement (search ID). It contains no redundant words or fluff; 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 retrieval tool with one well-documented parameter and strong annotations, the description is sufficient. It communicates the essential context: it operates on a previous search and returns complete results. It does not explain the return format, but no output schema exists, and 'full results' gives a reasonable expectation. This is adequate for the low complexity.
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 covers both parameters (actually one) completely, with a clear description of search_id as 'The search ID returned from claimhit_search or claimhit_get_history'. The description adds no additional nuance beyond the schema, and the baseline for high schema coverage is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets full results from a previous ClaimHit search using a search ID. It uses a specific verb ('get'), a clear resource ('full results from a previous ClaimHit search'), and clearly distinguishes from siblings like claimhit_search (starts a search) and claimhit_get_history (likely lists 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 context that this tool is used after a search has been performed, by referencing 'a previous ClaimHit search' and requiring the search ID. It does not explicitly compare with other siblings such as claimhit_rerun_search, but the 'previous search' phrasing gives clear intended use without ambiguity. This fits 'clear context, no exclusions'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
claimhit_rerun_searchRe-run patent searchAInspectRe-run a previous patent search to get fresh results. Free within 6 months of the original search. Use when the user wants to refresh results or check if new infringers have appeared.
| Name | Required | Description | Default |
|---|---|---|---|
| search_id | Yes | The search ID of the previous search to re-run |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint=false, openWorldHint=true, idempotentHint=false, and destructiveHint=false. The description adds valuable context beyond annotations by specifying the free-within-6-months business rule, which is not captured in the structured fields. It could further disclose whether the re-run returns results directly or requires a subsequent fetch via claimhit_get_result, but the added cost context justifies a 4.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose, and every sentence adds value. There is no redundancy or filler.
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?
No output schema is present, so the description should clarify what the tool returns. 'Get fresh results' is vague—it doesn't explain whether the tool returns results immediately, returns a new search ID for later retrieval via claimhit_get_result, or something else. The tool is simple (1 parameter) but this missing return behavior keeps it from being complete; score 3 reflects the minimal viable level.
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 100% description coverage for the single parameter, search_id, which is already described as 'The search ID of the previous search to re-run.' The tool description does not add further semantic nuance beyond what the schema provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Re-run a previous patent search to get fresh results.' It specifies the resource (patent search) and the verb (re-run), and distinguishes this from the sibling search tool by focusing on re-running an existing search rather than performing a new one.
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 explicit usage context: 'Use when the user wants to refresh results or check if new infringers have appeared.' It also notes the free-within-6-months condition, which helps decide when to use this tool. However, it does not mention when not to use it or explicitly reference alternatives like claimhit_search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
claimhit_searchSearch patent for infringersAInspectSearch for products or technical standards that potentially infringe a given patent. Runs multiple AI models in parallel and returns ranked results with Hit Scores. Use this when asked to find infringers, check infringement, or screen a patent.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | What to search: products (default), standards (SEP/FRAND analysis), or both | products |
| user_context | No | Optional: additional hints to guide the search. E.g. "focus on automotive camera systems", "look for semiconductor companies", "prioritise claim 3 which covers the image stabilization feature". Passed to all AI models in the ensemble. | |
| patent_number | Yes | Patent number with kind code. Example: US10123456B2 or EP3456789B1 or WO2020123456A1 | |
| target_standard | No | Optional: specific standard to focus on e.g. "5G NR", "Wi-Fi 6", "HEVC" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavior beyond annotations: it reveals parallel execution of multiple AI models and the return of ranked results with Hit Scores. It also uses 'potentially infringe' to set probabilistic expectations. Annotations provide openWorldHint and non-read-only hints, but the description enriches the agent's understanding of how the tool operates, though it does not mention cost/credit consumption.
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?
Three concise, front-loaded sentences: purpose first, then behavioral detail, then usage guidance. No filler or redundancy. Every sentence adds value, 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?
For a 4-parameter tool with no output schema, the description covers purpose, behavior, and usage context. It mentions the return format ('ranked results with Hit Scores') which is a minimal but acceptable descriptor. However, it lacks details on pagination or how results might be retrieved later (sibling get_result hints at persistence), leaving some gaps for a complex 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 description coverage is 100% with detailed parameter descriptions (examples, defaults, enums). The description adds no extra parameter semantics beyond confirming products/standards which aligns with the 'mode' parameter. Baseline 3 is appropriate because the schema carries the full semantic load.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Search'), identifies the resource ('a given patent'), and defines the scope ('products or technical standards') with clear intent ('potentially infringe'). It differentiates from siblings by mentioning parallel AI models and Hit Scores, and the usage cases (find infringers, check infringement, screen a patent) distinguish it from claimhit_get_result or claimhit_rerun_search.
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 states when to use this tool: 'when asked to find infringers, check infringement, or screen a patent.' This is clear context, but it does not mention alternatives or exclusions, failing to reach the level of explicitly naming sibling tools for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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