GleanMark Trademark Search

Run Safe Analytics Query

run_safe_analytics

Read-only

Run a constrained business-level analytics query without exposing schema details. This is the default fallback for bespoke rankings, counts, snapshots, and timelines across owners, firms, and correspondents. Prefer this before chaining search, summary, or web research tools for aggregate business questions.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum number of ranked rows, preview marks, or timeline events to return.
`entity`	Yes	What to analyze. Ranking currently supports owners. Count, snapshot, and timeline also support firms and correspondents.
`metric`	No	Required for count. Ranking currently supports filings only.
`filters`	No
`subject_name`	No	Required for count, snapshot, and timeline. Examples: "Ideaya Biosciences", "Goodwin Procter", or "Todd Schneider".
`analysis_type`	Yes	Business analytics mode: ranking, count, snapshot, or timeline.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`title`	Yes
`entity`	Yes
`metric`	Yes
`summary`	Yes
`rankings`	Yes
`returned`	Yes
`resolution`	Yes
`count_result`	Yes
`subject_name`	Yes
`analysis_type`	Yes
`filters_applied`	Yes
`snapshot_result`	Yes
`timeline_result`	Yes
`resolved_subject`	Yes
`open_in_gleanmark`	Yes
`total_matching_marks`	No
`total_matching_entities`	No
`total_matching_live_marks`	No

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful context: 'constrained', 'without exposing schema details', and its role as a safe fallback. It doesn't contradict annotations and offers non-obvious behavioral traits.

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?

Three sentences, front-loaded with the core purpose, followed by scope and usage guidance. No wasted words; each sentence earns its place.

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?

Given the rich schema and output schema, the description provides sufficient decision context: what it does, when to prefer it, and its constraints. It covers the essential behavioral contract without needing to repeat schema details.

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

Parameters3/5

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

Schema coverage is high (83%), so the schema handles most parameter semantics. The description adds high-level context about analysis types and entities but doesn't explain individual parameters beyond the schema. Baseline 3 is appropriate.

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 clearly specifies the verb ('Run'), resource ('constrained business-level analytics query'), and scope ('rankings, counts, snapshots, and timelines across owners, firms, and correspondents'). It distinguishes from siblings by framing it as the 'default fallback' and a safer abstraction layer.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'default fallback for bespoke...' and instructs to 'Prefer this before chaining search, summary, or web research tools for aggregate business questions.' This provides clear context and alternatives.

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.

Resources

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