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baryhuang

AWS Resources MCP Server

by baryhuang
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

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as executing boto3 code snippets for AWS operations.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'aws_resources_query_or_modify' follows a clear verb_noun pattern and is descriptive.

    Tool Count2/5

    A single tool for an AWS resources server is too few for the apparent scope, as AWS involves many distinct services and operations. This forces all functionality through one generic interface, which is insufficient for comprehensive coverage.

    Completeness2/5

    The tool surface is severely incomplete for an AWS resources domain. While the tool allows generic boto3 execution, it lacks specific operations for common AWS resources (e.g., EC2 instances, S3 buckets, IAM roles), leaving significant gaps that will likely cause agent failures.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • 1 of 3 community issues answered or closed 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.

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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool can 'query or modify' AWS resources, implying both read and write operations, but fails to detail critical aspects like authentication requirements, error handling, rate limits, or safety considerations. This leaves significant gaps in understanding 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.

    Conciseness4/5

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

    The description is concise and front-loaded in a single sentence: 'Execute a boto3 code snippet to query or modify AWS resources.' It efficiently conveys the core purpose without unnecessary details, though it could be slightly improved by structuring usage hints separately.

    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 tool's complexity (executing arbitrary code for AWS operations) and the absence of annotations and output schema, the description is incomplete. It lacks information on return values, error cases, security implications, and operational constraints, which are crucial for safe and effective use by an AI agent.

    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?

    The input schema has 100% description coverage, with the 'code_snippet' parameter well-documented in the schema. The description adds no additional meaning beyond what the schema provides, as it only repeats the boto3 and AWS context. According to the rules, with high schema coverage, the baseline is 3 even without param info in the description.

    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: 'Execute a boto3 code snippet to query or modify AWS resources.' It specifies the action (execute), technology (boto3), and target (AWS resources). However, it doesn't distinguish from siblings since there are none, so it cannot achieve the full differentiation required for a score of 5.

    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 mentions querying or modifying AWS resources but offers no context about specific scenarios, prerequisites, or exclusions. This lack of usage direction limits its effectiveness for an AI agent.

    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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