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list_lambda_functions_impl

List available AWS Lambda functions accessible through the MCP2Lambda server to understand their capabilities and use them as tools for accessing resources, data, or computation.

Instructions

Tool that lists all AWS Lambda functions that you can call as tools. Use this list to understand what these functions are and what they do. This functions can help you in many different ways.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • main.py:68-92 (handler)
    The main handler function for the 'list_lambda_functions_impl' tool. It lists AWS Lambda functions using boto3, filters those matching the FUNCTION_PREFIX or listed in FUNCTION_LIST, and returns their names and descriptions as a JSON string.
    def list_lambda_functions_impl(ctx: Context) -> str:
        """Tool that lists all AWS Lambda functions that you can call as tools.
        Use this list to understand what these functions are and what they do.
        This functions can help you in many different ways."""
    
        ctx.info("Calling AWS Lambda ListFunctions...")
    
        functions = lambda_client.list_functions()
    
        ctx.info(f"Found {len(functions['Functions'])} functions")
    
        functions_with_prefix = [
            f for f in functions["Functions"] if validate_function_name(f["FunctionName"])
        ]
    
        ctx.info(f"Found {len(functions_with_prefix)} functions with prefix {FUNCTION_PREFIX}")
        
        # Pass only function names and descriptions to the model
        function_names_and_descriptions = [ 
            {field: f[field] for field in ["FunctionName", "Description"] if field in f}
            for f in functions_with_prefix
        ]
        
        return json.dumps(function_names_and_descriptions)
  • main.py:125-125 (registration)
    Registration of the list_lambda_functions_impl tool when pre-discovery of Lambda functions is disabled (PRE_DISCOVERY=False).
    mcp.tool()(list_lambda_functions_impl)
  • main.py:181-181 (registration)
    Fallback registration of the list_lambda_functions_impl tool when dynamic pre-discovery of individual Lambda tools fails.
    mcp.tool()(list_lambda_functions_impl)
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. It states the tool lists functions but doesn't disclose behavioral traits such as whether it requires authentication, rate limits, pagination, or what the output format looks like. The description is minimal and lacks critical operational details for a tool that interacts with AWS Lambda.

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 three sentences, but the last two are redundant and vague ('This functions can help you in many different ways'), adding no value. It's front-loaded with the core purpose but wastes space on fluff, reducing overall efficiency.

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?

Given no annotations, no output schema, and a simple tool with zero parameters, the description is incomplete. It fails to explain what the list includes (e.g., function names, ARNs, configurations) or how to interpret the results, leaving gaps in understanding the tool's behavior and output.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. Baseline is 4 for zero parameters, as the schema fully covers the absence of inputs.

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

Purpose3/5

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

The description states the tool lists AWS Lambda functions, which is a clear purpose, but it's vague about what 'functions that you can call as tools' means. It doesn't distinguish from the sibling 'invoke_lambda_function_impl' tool, which would handle calling those functions. The description adds generic phrases like 'help you in many different ways' that don't clarify the specific action.

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 mentions using the list to understand what functions are and what they do, implying it's for discovery, but it doesn't provide explicit guidance on when to use this tool versus the sibling 'invoke_lambda_function_impl'. There's no mention of alternatives, prerequisites, or exclusions, leaving usage context unclear.

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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