Skip to main content
Glama
lensesio

Lenses MCP Server

by lensesio
OverviewSchemaRelated ServersScoreDiscussions
Python

list_sql_processors

Retrieve SQL processor details from Kafka environments to manage and monitor data processing pipelines in Lenses.io.

Instructions

Retrieves all SQL processor details.

Args: environment: The environment name.

Returns: A dictionary containing a list of all SQL processors with their details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
environmentYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • src/lenses_mcp/tools/sql_processors.py:16-28 (handler)
    The handler function decorated with @mcp.tool() that implements the logic for listing SQL processors by making an API GET request to the specified endpoint.
    @mcp.tool()
async def list_sql_processors(environment: str) -> Dict[str, Any]:
    """
    Retrieves all SQL processor details.
    
    Args:
        environment: The environment name.
    
    Returns:
        A dictionary containing a list of all SQL processors with their details.
    """
    endpoint = f"/api/v1/environments/{environment}/proxy/api/v2/streams"
    return await api_client._make_request("GET", endpoint)
  • src/lenses_mcp/server.py:32-32 (registration)
    The call to register_sql_processors(mcp) which registers the list_sql_processors tool along with other SQL processor tools.
    register_sql_processors(mcp)
  • src/lenses_mcp/tools/sql_processors.py:10-10 (registration)
    The registration function that defines and registers the SQL processors tools, including list_sql_processors, using FastMCP decorators.
    def register_sql_processors(mcp: FastMCP):

Tool Definition Quality

C2.9/5.0
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 'Retrieves' data, implying a read-only operation, but doesn't cover critical aspects like authentication needs, rate limits, error handling, or pagination behavior. For a list operation with zero annotation coverage, this is a significant gap in transparency.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded, with the core purpose stated first ('Retrieves all SQL processor details'), followed by structured Args and Returns sections. There's no wasted text, making it efficient, though the Args section could be more integrated into the flow for optimal structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a simple list operation with one parameter) and the presence of an output schema (which handles return values), the description is moderately complete. It covers the basic purpose and parameter, but lacks usage guidelines, behavioral details, and richer parameter context, making it adequate but with clear gaps for effective agent use.

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 0%, meaning the parameter 'environment' has no description in the schema. The tool description adds minimal semantics by noting 'environment: The environment name' in the Args section, which clarifies the parameter's purpose but lacks details like format, examples, or valid values. With one parameter, this provides basic compensation but remains inadequate for full understanding.

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 with a specific verb ('Retrieves') and resource ('all SQL processor details'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_sql_processor' (which likely retrieves a single processor), so it doesn't reach the highest score.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_sql_processor' (for single processor details) or 'list_sql_processors' (if it exists elsewhere), nor does it specify prerequisites or contexts for use. This leaves the agent with minimal usage direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/lensesio/lenses-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server