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deeparchi-ai

Patent MCP Server

by deeparchi-ai

get_legal_status

Retrieve a patent's legal status, kind code, filing, grant, priority dates, assignee, and legal events from Google Patents for CN, US, EP, and other jurisdictions.

Instructions

Get legal status of a patent from Google Patents page. Extracts status (granted/application/utility_model), kind_code (A/B/U), filing_date, grant_date, priority_date, assignee, and legal events timeline. Works for CN, US, EP, and most other jurisdictions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
publication_numberYesPatent publication number, e.g. 'US-7650331-B1', 'CN-110286864-A'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.9.2

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose an important behavioral trait: the data is scraped from a Google Patents page, implying external dependency and possible failure. It does not state whether the operation is read-only (implied by 'Get'), what happens for unsupported jurisdictions, or any rate-limit/auth constraints.

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?

Three tight sentences; the core action is front-loaded and the field list and jurisdiction note follow in support. The enumeration of returned fields is long but earns its place since there is no output schema.

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?

Without an output schema, the description correctly compensates by enumerating the returned fields and the supported jurisdictions. Remaining gaps are behavioral rather than informational: no error/fallback behavior for unsupported jurisdictions and no timing or rate-limit expectations.

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?

Single parameter with 100% schema description coverage, including worked examples ('US-7650331-B1', 'CN-110286864-A'). The description adds no format or syntax detail beyond the schema, so the baseline 3 applies.

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?

The description names a specific verb and resource ('Get legal status of a patent') and enumerates exactly what it extracts (status, kind_code, dates, assignee, legal events timeline), which separates it from siblings like get_patent_claims or get_patent_family. However, it never explicitly contrasts itself with get_patent, which likely also returns bibliographic data, so an agent must infer the boundary.

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

Usage Guidelines3/5

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

No explicit when-to-use or when-not-to-use guidance and no named alternative. The jurisdiction note ('Works for CN, US, EP, and most other jurisdictions') implies applicability scope but does not tell the agent when this tool is the right choice versus get_patent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.