Skip to main content
Glama
deeparchi-ai

Patent MCP Server

by deeparchi-ai

competitor_citation_matrix

Check whether competitors cite your target patents by matching citing assignees against competitor keywords, returning a citation matrix and summary counts.

Instructions

Check if a set of target patents are cited by competitors. Searches each patent's cited-by list and matches citing assignees against competitor keywords (case-insensitive substring). Returns matrix: {patent: [{citing_patent, title, assignee, matched_keyword}]} and summary counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
competitor_keywordsYesCompetitor assignee substrings, e.g. 百度, Baidu, 华为
publication_numbersYesPatent numbers to check (e.g. CN110286864A)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.9.2

TDQS

A4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it does disclose the non-obvious behavior: per-patent cited-by traversal, case-insensitive substring matching of assignees, and the exact return structure. It omits rate limits, handling of invalid publication numbers, and pagination, but the core algorithmic behavior is transparent.

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 tight sentences: purpose first, matching mechanism second, return shape last. Every sentence earns its place with no filler.

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 no output schema, the description correctly explains the return shape (matrix plus summary counts), which is essential. Largely complete for a two-parameter read tool, though edge cases like empty matches or invalid patent numbers are unaddressed.

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 100% and both parameters are documented with examples, so the schema does the heavy lifting. The description reinforces the keyword matching semantics (case-insensitive substring) but adds no syntax or format detail beyond 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?

States a specific verb and resource: checking whether target patents are cited by competitors, with the exact matching mechanism named. This is clearly distinguishable from siblings like get_cited_by (unfiltered) and bidirectional_citation_graph.

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?

The description implies when to use it (filtering citing assignees by competitor keywords) but never states it explicitly or names alternatives such as get_cited_by for an unfiltered cited-by list. Usage must be inferred from the sibling set.

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