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TsvetanG2

cognigy-ai-mcp-management-server

create_llm

Creates a new LLM configuration to power AI agents, Knowledge AI, and generative features. Requires a connection with provider credentials. Set dryRun=false to create.

Instructions

Creates a new Cognigy.AI large language model configuration. LLMs power Knowledge AI, AI Agents, and generative features. Requires a connection with provider credentials. MUTATING: Set dryRun=false to create.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName for the LLM configuration
dryRunNoIf true (default), validates without creating. Set to false to actually create.
providerYesThe LLM provider
isDefaultNoSet as the default LLM for the project
modelTypeYesThe model type (e.g., 'gpt-4o', 'claude-3-opus-20240229', 'gemini-2.0-flash')
projectIdYesThe project ID to create the LLM in
modelGroupNoModel group: 'chat' for conversational, 'completion' for text generation, 'embedding' for embeddings
descriptionNoDescription of the LLM's purpose
connectionIdYesThe connection ID containing the provider credentials
providerConfigNoProvider-specific configuration (e.g., resourceName, deploymentName for Azure)
Behavior4/5

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

Annotations indicate mutation (readOnlyHint=false) and no destructiveness. Description adds 'MUTATING' label and explains dryRun behavior, which is beyond annotations. No contradictions.

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: purpose, prerequisite, and mutation guidance. Front-loaded, no fluff, every sentence earns its place.

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?

Given 10 well-documented parameters and no output schema, the description covers purpose, prerequisite, and mutation. Could hint at response but is largely complete.

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 100%, baseline 3. Description adds value by highlighting dryRun's role and the connectionId prerequisite, providing extra meaning 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?

The description clearly states 'Creates a new Cognigy.AI large language model configuration' with a specific verb and resource. It differentiates from siblings like update_llm and clone_llm by focusing on creation.

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

Usage Guidelines4/5

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

States prerequisite ('Requires a connection with provider credentials') and provides usage guidance ('Set dryRun=false to create'). Does not explicitly exclude alternatives, but context is clear.

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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