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danilop

MCP2Lambda

by danilop
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one lists available Lambda functions, and the other invokes a specific function. There is no overlap or ambiguity between them, as they serve separate steps in a workflow.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with '_impl' suffix: list_lambda_functions_impl and invoke_lambda_function_impl. This naming convention is predictable and uniform across the tool set.

    Tool Count2/5

    With only 2 tools, the server feels thin for its apparent scope of interacting with AWS Lambda functions. While it covers listing and invoking, typical Lambda operations like updating, deleting, or configuring functions are missing, making the set under-scoped.

    Completeness2/5

    The tool set is severely incomplete for AWS Lambda management. It only supports listing and invoking functions, lacking essential CRUD operations such as creating, updating, deleting, or monitoring functions, which limits agent capabilities in this domain.

  • Average 3/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • 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.

  • 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 invokes a Lambda function but doesn't mention critical behavioral traits such as authentication requirements, error handling, rate limits, or what happens upon invocation (e.g., synchronous vs. asynchronous execution). This leaves significant gaps for an agent to understand the tool's behavior.

    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 highly concise and well-structured with two sentences: the first states the purpose, and the second provides usage guidance. Every sentence earns its place by adding clear value without redundancy, making it front-loaded and efficient.

    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 the complexity of invoking a Lambda function, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on authentication, execution behavior, error responses, and parameter specifics, which are essential for an agent to use this tool effectively in a real-world context.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 0% description coverage, so the description must compensate. It mentions 'JSON payload' which relates to the 'parameters' parameter, adding some meaning beyond the schema. However, it doesn't explain the 'function_name' parameter or provide details on payload structure, format, or constraints, failing to fully address the coverage gap.

    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: 'invokes an AWS Lambda function with a JSON payload.' It specifies the verb ('invokes'), resource ('AWS Lambda function'), and payload type ('JSON payload'), which is specific and actionable. However, it doesn't explicitly differentiate from its sibling 'list_lambda_functions_impl' beyond the implied distinction between invocation and listing.

    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 on when to use this tool: 'Before using this tool, list the functions available to you.' This indicates a prerequisite step involving the sibling tool 'list_lambda_functions_impl', offering clear context for usage. However, it lacks details on when not to use it or alternatives beyond this prerequisite.

    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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