Skip to main content
Glama

akb_search

Search documents using hybrid retrieval that fuses dense vector and BM25 keyword matching, handling both natural language questions and short keyword queries.

Instructions

Search documents with hybrid retrieval — dense vector (semantic) fused with BM25 sparse (keyword) via Reciprocal Rank Fusion. Handles both natural-language questions and short keyword queries well. For exact string / regex matches (code, URLs, version numbers) prefer akb_grep. Returns each hit's uri; use akb_drill_down or akb_get with that URI for full content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query
vaultNoLimit search to a specific vault
collectionNoLimit search to a specific collection
typeNoFilter by document type
tagsNoFilter by tags
limitNo
Behavior4/5

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

Describes retrieval approach (hybrid, RRF) and output (URI per hit). No annotations provided, so description carries burden; it covers main behavior but lacks details on permissions or rate limits.

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, each purposeful: first explains algorithm, second usage guidance, third output and next steps. No wasted words.

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?

Covers essential behavior: search method, when to use, what output. Lacks mention of pagination or sorting for the limit parameter, but overall adequate for a search tool without output schema.

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 coverage is 83% (high), baseline 3. Description adds value by explaining search algorithm (hybrid, RRF) and handling of query types, beyond schema descriptions.

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?

Clearly states it searches documents using hybrid retrieval (dense+sparse fusion), handles both natural language and keywords, and distinguishes from sibling akb_grep for exact matches.

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 tells when to use (natural language/keyword queries) and when not (exact matches, prefer akb_grep). Also mentions next steps after getting URIs.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dnotitia/akb'

If you have feedback or need assistance with the MCP directory API, please join our Discord server