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aws-samples

AWS DR Cost Estimator MCP Server

Official
by aws-samples
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Python
Remote

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct purpose: listing strategies, comparing all strategies, and analyzing a single strategy. The scope difference between compare and analyze is clear, so there is minimal risk of misselection.

    Naming Consistency5/5

    All tool names follow a verb_noun pattern with snake_case, using 'dr_strategy' as the core noun. The only variation is singular vs plural, which is semantically appropriate and does not break the overall consistency.

    Tool Count5/5

    With 3 tools, the server is tightly scoped to its purpose of DR cost estimation. Each tool covers an essential operation (list, compare, analyze) without redundancy or unnecessary bloat.

    Completeness5/5

    The tool set covers the full read-only lifecycle of DR strategy costing: discovering available strategies, comparing all, and drilling into a single strategy. No obvious CRUD or analysis gaps exist for the stated purpose.

  • Average 4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT No Attribution.

  • 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 must disclose behavior. It mentions using 'live Cost Explorer data' (source and recency) but does not state whether the operation is read-only, what permissions are needed, or any side effects. This is a significant gap for a tool that queries live data.

    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 a single, concise sentence that front-loads the essential action and scope, with no wasted words.

    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 has 8 parameters and an output schema, the description is minimal but covers the core purpose. However, it lacks context about tool usage in workflows and behavioral details, making it thin for a tool of this complexity.

    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 the schema already documents all 8 parameters thoroughly. The description itself adds no parameter-specific information, so it meets the baseline of 3.

    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 uses the specific verb 'Compare' with a clear resource ('DR cost across all four strategies') and distinguishes from sibling tools like list_dr_strategies and analyze_dr_strategy by focusing on comparing all strategies.

    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 implies a clear use case (comparing DR costs) but does not explicitly mention when to use it versus alternatives such as analyzing a single strategy or listing strategies. It provides clear context but no exclusions.

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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden. It discloses the key behavioral trait of using 'live Cost Explorer data', implying real-time AWS access. However, it omits other useful details like whether the operation is read-only, any rate-limit considerations, or the nature of the output (covered by output schema).

    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 a single, front-loaded sentence that immediately states the action and scope. No wasted words, highly concise and effective.

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

    Completeness4/5

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

    The description is minimal but sufficient given a rich input schema and an existing output schema. It clearly communicates the purpose and data source. A slight gap is that it doesn't hint at what 'analyze' produces (e.g., cost breakdown, average), but the output schema covers return values, so completeness is good.

    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%, and the parameter descriptions in the schema are detailed (e.g., tag_filters scoping, warm_standby_capacity rescaling). The tool description itself adds no additional parameter meaning beyond the schema, which is acceptable given high coverage.

    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 the tool analyzes DR cost for a single strategy using live Cost Explorer data, with a specific verb ('Analyze') and resource ('DR cost for a single strategy'). It explicitly distinguishes from sibling tools like compare_dr_strategies by limiting to a single strategy.

    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 phrase 'for a single strategy' provides clear usage context, implying this should be used when analyzing one strategy rather than comparing. However, it does not explicitly mention alternatives like compare_dr_strategies for multi-strategy comparison, though sibling names make the distinction apparent.

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

  • Behavior4/5

    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 clearly indicates a read-only listing operation, and the mention of 'four supported' strategies adds a specific behavioral detail. There is no hidden or destructive behavior, but the description could be slightly more explicit about the lack of side effects.

    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 a single, compact sentence that conveys the essential information without any wasted words. Every part of the sentence contributes meaning.

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

    Completeness5/5

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

    The tool is simple with no parameters and an output schema present, so the description's mention of the four strategies and their labels/descriptions is sufficient. It is complete for the tool's intended use.

    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 tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and the empty input schema confirms there is nothing to document.

    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 the action ('List') and the resource ('DR strategies'), and specifies that it returns exactly four items with labels and descriptions. This distinguishes it from sibling tools like 'compare_dr_strategies' and 'analyze_dr_strategy', which imply different operations.

    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 gives a clear context for when to use the tool (to get the list of supported strategies), but does not explicitly mention alternatives or when not to use it. The implied usage is adequate for a simple list tool, but there is no direct comparison with sibling tools.

    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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