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WeKnora MCP Dispatch

by mwe-support

create_model

Create a new AI model for knowledge QA, embedding, or rerank tasks, specifying type, source, and API credentials. Use this tool to add models to your WeKnora setup through the MCP Dispatch server.

Instructions

Create a new model

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesModel name
typeYesModel type (KnowledgeQA, Embedding, Rerank)
sourceNoModel sourcelocal
api_keyNoModel API key
base_urlNoModel API base URL
is_defaultNoSet as default model
descriptionYesModel description
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It merely states that it creates a model, but does not disclose side effects (e.g., whether duplicate names cause errors), authorization requirements, return behavior, or any constraints, which is insufficient for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence that is front-loaded, but it is under-specified rather than concise. It adds no value beyond the tool name, so it fails the criterion that every sentence should earn its place.

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?

For a tool with 7 parameters, no output schema, and no annotations, this description is inadequate. It does not explain what a model is, when to create one, what the response is, or any operational context, making it incomplete for an agent to invoke correctly.

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?

All 7 parameters have descriptions in the input schema, so schema coverage is 100%. The description adds no additional parameter context, but the baseline of 3 is appropriate since the schema already documents each parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Create a new model' is essentially a restatement of the tool name 'create_model'. It provides a verb and resource but does not differentiate from sibling tools or clarify what a model is in this context, making it a tautology.

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?

The description offers no guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or how it relates to other model-related tools like list_models or get_model.

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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