AWS Knowledge Base Retrieval MCP Server
Server Quality Checklist
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 and distinct by default.
Naming Consistency5/5A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions.
Tool Count2/5One tool is too few for a server focused on AWS Knowledge Base retrieval, as it lacks essential operations like listing knowledge bases, managing queries, or handling configurations, making the scope feel incomplete and limiting.
Completeness2/5The tool set is severely incomplete for the domain of AWS Knowledge Base operations. It only provides retrieval, missing critical functions such as creating, updating, or deleting knowledge base entries, which are necessary for a full workflow.
Average 2.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 mentions retrieval but fails to describe key traits like authentication requirements, rate limits, error handling, or what the retrieval entails (e.g., returns text snippets, metadata). This leaves significant gaps in understanding how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function and inputs without any fluff. It's front-loaded with the core purpose and appropriately sized for the task, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the retrieval returns (e.g., format, content), error conditions, or operational details like latency or costs. For a tool with 3 parameters and missing structured data, this leaves the agent under-informed about critical context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters (query, knowledgeBaseId, n). The description adds no additional meaning beyond what's in the schema, such as examples or constraints. This meets the baseline for high schema coverage but doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('performs retrieval') and the resource ('from the AWS Knowledge Base'), making the purpose immediately understandable. It specifies the required inputs (query and Knowledge Base ID), which helps distinguish it from generic retrieval tools. However, without sibling tools, it doesn't need to differentiate further, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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, prerequisites, or contextual constraints. It merely states what the tool does without indicating appropriate scenarios or limitations, leaving the agent without usage direction.
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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