get_mark_ai_summaryGet an AI-generated deep-dive summary of a specific trademark. Covers mark details, goods/services, status, owner, prosecution context, and any active legal proceedings.
| Name | Required | Description | Default |
|---|---|---|---|
| serial_number | Yes | USPTO serial number |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds that the summary is AI-generated and includes specific content areas, but does not disclose potential latency, cost, or that AI-generated output may be interpretive. This is acceptable given annotations but lacks deeper behavioral context.
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 fluff. The first sentence front-loads the core purpose, and the second enumerates coverage without redundant wording. Every sentence contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but the description enumerates what the summary covers (mark details, goods/services, status, owner, prosecution context, active legal proceedings). While it doesn't describe the exact return format or error behavior, for a single-parameter summary tool, this is largely sufficient. Slight gap: no mention of what happens if the serial number is invalid or not found.
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%: the only parameter serial_number is described as 'USPTO serial number'. The description adds no additional parameter syntax or format details, staying at baseline where the schema fully handles parameter semantics.
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 ('Get') and resource ('AI-generated deep-dive summary of a specific trademark'), and lists the content areas covered. This distinguishes it from siblings like get_mark_prosecution_summary (narrower scope) and lookup_trademark (basic details) by emphasizing the AI-generated, comprehensive nature.
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 when to use the tool: when a comprehensive AI-generated overview of a mark is needed, including prosecution context and legal proceedings. It does not explicitly exclude alternatives or name sibling tools, but the clear scope provides enough contextual guidance for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly defined scopes, but several search/count tools overlap (search_trademarks, phonetic_search, run_knockout_search, get_similar_marks) and owner/firm analytics tools have similar boundaries. Descriptions help differentiate them, but the large set increases the chance of misselection.
All tool names follow a consistent lowercase snake_case verb_noun pattern (get_, search_, run_, analyze_, etc.). The only minor deviation is web_research, which is noun_verb, but it remains perfectly readable and consistent with the overall style.
61 tools is extreme for any server, far exceeding the 50+ threshold. Even for a comprehensive trademark platform, this number overwhelms agents with selection complexity and makes the toolset difficult to navigate.
Several tools launch asynchronous processes and instruct users to call status tools (get_prosecution_history_status, get_ttab_proceeding_analysis_status, get_office_action_research_status) that are not present in the toolset. Additionally, search_trademarks and phonetic_search reference list_marks_containing_term, which is also missing. These critical gaps cause agent failures when following the described workflows.