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Create Knowledge Base

lyzr_kb_create

Create a RAG knowledge base to enable retrieval-augmented generation. Returns the new knowledge base ID for immediate use in your applications.

Instructions

Create a RAG knowledge base. Returns its id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesKB name — lowercase letters, numbers, underscores only
llm_modelNoLLM model (default gpt-4o)
descriptionNo
vector_storeNoVector store: qdrant, weaviate, pg_vector, milvus, neptuneqdrant
embedding_modelNoEmbedding model (default text-embedding-3-large)
Behavior3/5

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

Annotations already indicate this is not read-only, is not idempotent, and is not destructive, so the description's 'Create' action aligns without contradiction. It adds a small behavioral detail by noting the tool returns the new KB's id, but it does not describe any further side effects, prerequisites, or what happens on duplicate names. With annotations present, this is adequate but not rich.

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 consists of two short, front-loaded sentences: one stating the action and one stating the return value. Every word earns its place, with no redundancy or 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?

For a 5-parameter create tool with high schema coverage and no output schema, the description plus schema provide enough to invoke the tool correctly. It communicates the key outcome (returns the id), and the sibling tool names imply related next steps like training. It is slightly incomplete in not mentioning how the KB should be populated, but it is not a serious gap given the available structured metadata.

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 description coverage is 80%, so the input schema already explains most parameters (name pattern, llm_model, vector_store, embedding_model). The description itself adds no parameter-level meaning beyond labeling the resource as a RAG knowledge base, so it does not exceed the baseline for well-documented schemas.

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?

The description uses a specific verb ('Create') and a precise resource ('RAG knowledge base'), clearly distinguishing this from sibling KB tools like lyzr_kb_update or lyzr_kb_delete. It also states the primary return value ('Returns its id'), making the tool's purpose unmistakable.

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 the tool is for creating a knowledge base before training/querying it, but it does not explicitly state when to use this versus alternatives like lyzr_kb_update or lyzr_kb_train_text. There is no exclusion or direct comparison to sibling tools, leaving usage context implicit.

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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