Skip to main content
Glama

Train KB from Text

lyzr_kb_train_text

Ingest text chunks into a knowledge base by providing the RAG ID and array of texts. This action feeds training data to the RAG system for agent use.

Instructions

Ingest text chunks into a knowledge base.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textsYesText chunks to ingest
rag_idYesKnowledge base id
Behavior2/5

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

The description adds no behavioral context beyond what annotations already provide. It does not disclose whether the operation appends or replaces existing content, whether it is asynchronous, or whether there are rate limits or other consequences. With readOnlyHint=false, idempotentHint=false, the agent knows it's a write and non-idempotent, but the description offers no additional transparency.

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?

The description is a single concise sentence that is front-loaded with the action and resource. Every word earns its place, with no unnecessary filler.

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

Completeness2/5

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

Given the simplicity of the tool, the description remains incomplete: it does not explain the return behavior, whether the operation is synchronous, or any prerequisites. The absence of an output schema means the agent may be left unsure about the outcome or error conditions.

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% with both parameters ('texts', 'rag_id') already described. The description adds no new parameter semantics, so the baseline of 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 clearly states the action (ingest) and the resource (text chunks into a knowledge base), distinguishing it from sibling tools like train_website or train_documents. It lacks explicit sibling differentiation, but the resource type is specific enough.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives such as lyzr_kb_train_website or lyzr_kb_train_documents. There is no mention of prerequisites (e.g., KB must exist) or context for choosing this over other training methods.

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/nandanNM/lyzr-mcp'

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