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list_knowledge_bases

Discover available knowledge bases for Retrieval-Augmented Generation (RAG) workflows. Lists all document repositories to enable integration into AI applications.

Instructions

List all knowledge bases for Retrieval-Augmented Generation (RAG) workflows.

Purpose: Discover available knowledge bases containing document collections for RAG applications. Essential for understanding what document repositories are available, finding knowledge bases for flow integration, and managing document-based AI applications.

Parameters: None

Returns: Array of KnowledgeBaseInfo objects containing:

  • name (string): Knowledge base identifier/name (use this in get_knowledge_base or delete_knowledge_base)

  • description (string): Purpose and contents description

  • Additional metadata varies by knowledge base implementation (document count, vector store info, embedding model)

Usage Examples:

  1. List all knowledge bases: {}

Best Practices:

  • Call this before integrating knowledge bases into RAG flows to verify availability

  • Use knowledge base names in Langflow's Vector Store or RAG components

  • Document which flows use which knowledge bases for dependency tracking

  • Monitor knowledge base list to identify unused repositories for cleanup

  • Cache knowledge base names for flow configuration validation

  • Verify knowledge base exists before flow execution to avoid runtime errors

Common Errors:

  • Empty results: No knowledge bases configured (create knowledge bases in Langflow UI or via API)

  • Connection timeout: Check Langflow instance availability and vector store connectivity

  • "Knowledge base service unavailable": Vector store backend not configured or unreachable

Related Tools:

  • get_knowledge_base: Retrieve detailed information about specific knowledge base

  • delete_knowledge_base: Remove individual knowledge base when no longer needed

  • bulk_delete_knowledge_bases: Clean up multiple knowledge bases at once

  • create_flow: Build RAG flows using knowledge base names from this list

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided, so description carries full burden. It describes return type and fields but does not explicitly state read-only behavior or side effects. Implies non-destructive listing but lacks explicit safety guarantees.

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?

Well-structured with sections for purpose, parameters, returns, examples, best practices, errors, and related tools. Front-loaded with purpose. Slightly verbose but every section adds value.

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

Completeness5/5

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

Completely covers the tool's usage, return values, best practices, and common errors. No output schema, so description adequately explains return structure. Covers integration context with flows and error handling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters, so schema coverage is 100% trivially. Description adds full semantics by detailing the structure of the returned array (name, description, additional metadata).

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 'List all knowledge bases for Retrieval-Augmented Generation (RAG) workflows.' with specific verb and resource, and distinguishes from sibling tools like get_knowledge_base.

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?

Provides explicit guidance including usage examples, best practices, common errors, and related tools. Tells when to use (before integrating, verify availability) and mentions alternatives (get_knowledge_base for details).

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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