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Lenses MCP Server

by lensesio
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Python

get_deployment_targets

Retrieve available Kubernetes and Connect clusters for deployment in a specified environment to identify where to deploy Kafka data operations.

Instructions

Returns deployment information including available Kubernetes clusters and Connect clusters.

Args: environment: The environment name.

Returns: Dictionary containing available deployment targets (Kubernetes clusters and Connect clusters).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
environmentYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • src/lenses_mcp/tools/sql_processors.py:111-123 (handler)
    The handler function for the 'get_deployment_targets' tool. It is decorated with @mcp.tool() for automatic registration and makes an API call to fetch deployment targets.
    @mcp.tool()
async def get_deployment_targets(environment: str) -> Dict[str, Any]:
    """
    Returns deployment information including available Kubernetes clusters and Connect clusters.
    
    Args:
        environment: The environment name.
    
    Returns:
        Dictionary containing available deployment targets (Kubernetes clusters and Connect clusters).
    """
    endpoint = f"/api/v1/environments/{environment}/proxy/api/v1/deployment/targets"
    return await api_client._make_request("GET", endpoint)
  • src/lenses_mcp/server.py:32-32 (registration)
    Top-level registration call to register_sql_processors(mcp), which includes the registration of the get_deployment_targets tool via its decorator.
    register_sql_processors(mcp)
  • src/lenses_mcp/server.py:16-16 (registration)
    Import of the register_sql_processors function used to register the tools.
    from tools.sql_processors import register_sql_processors

Tool Definition Quality

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns information, implying a read-only operation, but doesn't clarify permissions, rate limits, error handling, or data freshness. For a tool with zero annotation coverage, this leaves significant behavioral gaps, though it correctly indicates a non-destructive action.

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 well-structured and front-loaded, starting with the core purpose, followed by clear Args and Returns sections. Every sentence earns its place without redundancy, making it efficient and easy to parse for an AI agent.

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 tool's moderate complexity (1 parameter, no annotations, but has an output schema), the description is reasonably complete. It explains the purpose, parameter, and return value. The output schema handles return details, so the description doesn't need to elaborate further, though it could benefit from more behavioral context.

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?

The description adds meaningful context for the single parameter 'environment' by specifying it as 'The environment name' in the Args section, which clarifies its role beyond the schema's basic type. With schema description coverage at 0%, this compensates well, though it doesn't detail format or constraints like valid environment names.

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 tool's purpose: 'Returns deployment information including available Kubernetes clusters and Connect clusters.' It specifies the verb ('Returns') and the resource ('deployment information'), making the function explicit. However, it doesn't differentiate from siblings like 'get_environment' or 'list_environments', which might also retrieve environment-related data, so it misses full sibling distinction.

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. The description mentions an 'environment' parameter but doesn't explain prerequisites, such as whether the environment must exist or be accessible. Without explicit when/when-not instructions or named alternatives, usage context is minimal.

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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