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USPTO Patent MCP Server

USPTO Patent MCP Server

by riemannzeta
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

odp_search_datasets

Read-onlyIdempotent

Search USPTO bulk data products to find downloadable datasets for large-scale patent analysis. Filter results by query and paginate with offset and limit.

Instructions

Search USPTO bulk data products/datasets.

USE THIS TOOL WHEN: You need to find bulk download datasets
available from USPTO for large-scale analysis.

Args:
    query: Search query for dataset names/descriptions
    offset: Starting position (default: 0)
    limit: Max results (default: 25)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "odp_search_datasets"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
queryNo
offsetNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.8.1
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "additionalProperties": true,
-      "title": "Result",
-      "type": "object"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "odp_search_datasetsOutput",
-  "type": "object"
-}New value: +null
  2. First observedv1.1.1

TDQS

A3.6/5.0
Behavior2/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, covering the safety profile. The description adds no behavioral context of its own — no pagination semantics, no result shape, no rate limits — so it does little beyond the structured data.

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

Conciseness4/5

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

Purpose is front-loaded, the trigger sentence follows, and the args list is compact. Nothing is padded, though the Args block largely restates schema defaults rather than adding new information.

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

Completeness4/5

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

With annotations present and only three optional parameters, the description plus schema is nearly sufficient for correct invocation. The main residual gap is that no output schema exists and the description doesn't hint at what a result record contains.

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?

Schema description coverage is 0%, so the description must carry the parameter burden, and it does: query, offset (starting position, default 0) and limit (max results, default 25) are all explained. It compensates for the empty schema, though the semantics remain minimal.

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

Purpose4/5

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

Names a specific verb+resource: 'Search USPTO bulk data products/datasets', which is distinguishable from the sibling odp_get_dataset by the search verb. It stops short of an explicit sibling comparison, so it lands at a clear-but-undifferentiated 4.

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?

The 'USE THIS TOOL WHEN' block gives a concrete trigger — finding bulk download datasets for large-scale analysis — which is more than implied usage. It omits the natural exclusion (once you have a dataset ID, use odp_get_dataset), so no alternative is named.

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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