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NimbleBrainInc

AWS SES MCP Server

by NimbleBrainInc
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Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.2

  • Disambiguation5/5

    With only a single tool, there is no possibility of overlap or confusion between tools. The tool's purpose is clear and unambiguous.

    Naming Consistency5/5

    The tool name 'send_email' follows a clear verb_noun pattern and is self-consistent. While there is only one tool, the naming is predictable and professional.

    Tool Count2/5

    AWS SES is a broad service with many capabilities beyond sending email, such as managing verified identities, templates, and sending statistics. A single tool is far too few for the scope implied by the server name, making the server under-scoped.

    Completeness1/5

    The tool surface is severely incomplete for AWS SES. Essential operations like listing verified identities, checking sending limits, and managing configuration sets are missing, leaving agents with no way to handle common SES workflows.

  • Average 3.4/5 across 1 of 1 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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?

    The description only states the action without disclosing any behavioral traits such as authentication requirements, side effects, rate limits, or error handling. Since no annotations are provided, the description carries the full burden, and this minimal statement fails to provide meaningful 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 a single concise sentence that front-loads the core action without any unnecessary words. It is appropriately sized for the purpose, though it could benefit from additional details while still remaining concise.

    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?

    An output schema exists, so return values need not be explained. However, the description lacks behavioral context such as permissions or limitations, and no annotations are present. For a simple tool, this is minimally sufficient but leaves gaps in completeness.

    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 100%, so all parameters are already documented in the schema. The description adds no parameter-specific meaning beyond what the schema provides, keeping it at the baseline score.

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

    Purpose5/5

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

    The description clearly states it sends an email via AWS SES, identifying the action, resource, and service. This is specific and leaves no ambiguity about the tool's function, even though there are no sibling tools to differentiate from.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description does not explicitly state when to use this tool or provide any alternatives, but the action of sending an email implies its use case. With no sibling tools, the lack of alternatives is less critical, but there is no mention of prerequisites or scenario-based guidance.

    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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  • Evaluate tool definition quality.

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