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GleanMark Trademark Search

by com.gleanmark

Cohort Event Intervals

get_cohort_event_intervals
Read-only

Aggregate event-to-event pendency across a COHORT of marks — e.g. "average days from office action to publication for marks published in Q2 2026". Pick the cohort by an anchor event (preset: publication, notice_of_publication, registration, notice_of_allowance, abandonment, first_office_action — or raw event codes, trailing * = prefix) within a date window (max 366 days), and an interval start/end event. Returns avg/median/percentiles in days, how many cohort marks never had the start event, and example marks. Samples up to max_sample marks from the start of the window and says so when truncated. Use get_event_code_reference first if you need non-preset event codes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_eventNoInterval end event (first occurrence on/after the start event). Defaults to the cohort event itself.
max_sampleNoMax cohort marks to measure (default 1000, max 2000).
start_eventNoInterval start event (first occurrence on/before the cohort event). Defaults to first_office_action.
cohort_eventNoPreset anchor event defining cohort membership (e.g. publication = PUBO).
cohort_date_toYesCohort window end (YYYY-MM-DD). Required. Window max 366 days.
end_event_codesNoAlternative to end_event: raw event codes (trailing * = prefix).
cohort_date_fromYesCohort window start (YYYY-MM-DD). Required.
start_event_codesNoAlternative to start_event: raw event codes (trailing * = prefix).
cohort_event_codesNoAlternative to cohort_event: raw USPTO event codes; trailing * matches a prefix (e.g. "NPUB*").

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark this as a safe read-only operation, and the description adds substantial behavior: it returns averages/medians/percentiles, counts marks lacking the start event, and discloses sampling behavior ('Samples up to max_sample marks from the start of the window and says so when truncated'). It also explains the max 366-day date window.

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

Conciseness5/5

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

The description is front-loaded with the main action, uses a concrete example, and every sentence adds detail about parameters, return value, or usage. It is compact yet thorough, with no filler.

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

Completeness5/5

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

For a complex analytics tool with no output schema, the description adequately specifies return values (avg/median/percentiles, missing-start counts, example marks), explains the sampling behavior, and gives a full workflow. It also cross-references a sibling tool for code lookup, making the tool self-contained.

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

Parameters4/5

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

The input schema already covers all 9 parameters with descriptions (100% coverage). The description adds semantic context by explaining how the cohort anchor, interval start/end, and raw event codes work together, and it defines the prefix wildcard ('trailing *') behavior, which is only partially present in the schema.

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

Purpose5/5

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

The description opens with a specific verb 'Aggregate' and identifies the resource 'event-to-event pendency across a COHORT of marks', followed by a concrete example. It distinguishes itself from sibling individual-mark tools by emphasizing the cohort scope and by referencing get_event_code_reference for event codes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides a clear use scenario with an example and instructs users to 'Pick the cohort by an anchor event... within a date window... and an interval start/end event.' It also explicitly directs to 'Use get_event_code_reference first' for non-preset codes, offering an alternative workflow. However, it does not explicitly state when not to use the tool (e.g., for single-mark queries).

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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TDQS

B3.4/5.0
Disambiguation3/5

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.

Naming Consistency5/5

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.

Tool Count1/5

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.

Completeness2/5

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.

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