kb_search

Search knowledge entries by full-text content and tag filters.

Updated Aug 23, 2026

Search Tags:#search #knowledge-base #full-text #information-retrieval #local-index #tag-search

Ready to run

npx skills add https://github.com/MichaelYagi/mcp_a2a --skill kb-search-michaelyagi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: kb_search
Source: https://github.com/MichaelYagi/mcp_a2a/tree/main/servers/knowledge_base/skills/kb_search
Command: npx skills add https://github.com/MichaelYagi/mcp_a2a --skill kb-search-michaelyagi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables users to quickly locate knowledge entries by content or tag across a knowledge base, reducing time spent manual searching.

Core Features & Use Cases

Full-text search across notes, documents, and references.
Tag-based filtering to quickly narrow results.
Use Case: Find all entries about a topic or that are tagged with a specific category to assemble a focused set for review.

Quick Start

Use natural language queries to search across stored notes. Example: "kb_search find entries about onboarding" or "kb_search show entries with tag: project".

Frequently Asked Questions about kb_search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I search my knowledge base for specific content or tags?▼

You can search your knowledge base using natural language queries to retrieve entries by content, or apply tag-based filtering to quickly narrow results across stored notes and documents.

What's the best way to find all entries tagged with a specific category in my notes?▼

Finding tagged entries uses tag-based filtering, allowing you to quickly narrow results to a specific category and assemble a focused set of notes for review.

Does full-text search work across notes, documents, and references?▼

Full-text search works across stored notes, documents, and references, using a local index and simple query operators to return relevant results.

How do I use natural language queries to retrieve information from a local index?▼

You retrieve information from a local index by using natural language queries, such as asking to find entries about a specific topic or to show entries with a designated tag.

Can I narrow search results by combining content queries with tag filters?▼

You narrow search results by combining full-text search across content with tag-based filtering, reducing time spent manual searching across the knowledge base.

What are the limitations of using simple query operators for information retrieval?▼

Information retrieval relies on simple query operators and a local index, meaning search capabilities are limited to basic full-text matching and tag filtering rather than complex semantic queries.