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Enkrypt AI MCP Server

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by enkryptai

add_model

Add a new AI model to the Enkrypt AI MCP Server by providing configuration details, including model name, version, provider, and API key.

Instructions

Add a new model using the provided configuration.

Args: config: A dictionary containing the model configuration details. The structure of the ModelConfig is as follows: Example usage: { "model_saved_name": "example_model_name", # The name under which the model is saved. "model_version": "v1", # The version of the model. "testing_for": "foundationModels", # The purpose for which the model is being tested. (Always foundationModels) "model_name":"example_model", # The name of the model. (e.g., gpt-4o, claude-3-5-sonnet, etc.) "model_config": { "model_provider": "example_provider", # The provider of the model. (e.g., openai, anthropic, etc.) "endpoint_url":"https://api.example.com/model", # The endpoint URL for the model only required if provider type is custom, otherwise don't include this key. "apikey":"example_api_key", # The API key to access the model. "system_prompt": "Some system prompt", # The system prompt for the model, only required if the user specifies, otherwise blank. "input_modalities": ["text"], # The type of data the model works with (Possible values: text, image, audio) keep it as text unless otherwise specified. "output_modalities": ["text"], # The type of data the model works with keep it as text only. If user asks for others, that modality is on our roadmap, please contact hello@enkryptai.com if you need early access to this. }, } Ask the user for all the details before passing the config to the tool.

Returns: A dictionary containing the response message and details of the added model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
configYes
Behavior3/5

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 implies a write operation ('Add a new model') and includes an example config with detailed fields, but doesn't cover permissions, error handling, or side effects. This is adequate but lacks depth 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.

Conciseness3/5

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

The description is front-loaded with the core purpose, but includes a lengthy example that could be streamlined. The example is informative but makes the description verbose; some details (like contact information for early access) are extraneous and reduce efficiency.

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 the complexity (1 parameter with nested objects, no annotations, no output schema), the description does a good job by explaining the parameter thoroughly and hinting at the return value. However, it could better address behavioral aspects like authentication or error cases to be fully complete.

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?

The schema description coverage is 0%, but the description compensates fully by providing a detailed example of the 'config' parameter, including nested structure, field meanings, and usage notes (e.g., 'only required if provider type is custom'). This adds significant value beyond the minimal schema.

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 ('Add a new model') and the resource ('using the provided configuration'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'add_model_from_url' or 'modify_model_config', which would be needed for a perfect score.

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?

The description provides explicit guidance to 'Ask the user for all the details before passing the config to the tool,' which is helpful for usage timing. It doesn't mention when not to use this tool or alternatives like 'add_model_from_url,' but the guidance is clear and actionable.

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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