AWS S3 MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool targets a distinct S3 resource and action: listing buckets, listing objects, and generating a presigned GET URL. No overlap or ambiguity between them.
Naming Consistency5/5All tools follow a consistent s3_verb_noun pattern, using clear, lowercase snake_case. The naming uniformly indicates the service and the operation.
Tool Count4/5Three tools is a compact set, slightly on the low side, but reasonable for a focused S3 access server. Each tool serves a distinct, useful function without redundancy.
Completeness2/5The server only supports listing and presigned downloads, lacking any write or delete operations. For a general S3 server, this is notably incomplete; it appears read-only, which may be acceptable for a narrow use case but leaves significant functional gaps.
Average 3.5/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
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- No critical vulnerability alerts
- No high-severity vulnerability alerts
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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?
With no annotations provided, the description alone must disclose behavioral traits. It only states the operation without mentioning required permissions (e.g., s3:ListAllMyBuckets), potential pagination, return value structure, or whether it is read-only. This leaves significant behavioral ambiguity for an AI agent.
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, front-loaded sentence that communicates the exact operation without any redundant words. It is appropriately sized for a tool with no parameters, and every word earns its place. This is a model of conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema, no annotations), the description states the core action but omits non-obvious context such as required IAM permissions, the exact return format (list of bucket names?), and when to prefer this over s3_list_objects. It is minimally complete but leaves gaps that could affect an agent's ability to use it confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema confirms this with an empty properties object. Since there are no parameters to document, the description bears no responsibility for parameter semantics. The baseline of 4 applies because the absence of parameters makes the tool trivially simple in this dimension.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('all S3 buckets in the AWS account'), making it unmistakable and differentiating it from sibling tools like s3_list_objects (which targets objects within a bucket) and s3_presign_get (which presigns a URL). The scope is precise and the verb is action-oriented.
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 the sibling tools. It doesn't mention scenarios like enumerating all buckets before selecting one, nor does it exclude alternatives. The purpose is inferable from the name, but explicit usage context is entirely missing.
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 must disclose behavioral traits. It states the core operation but omits details like pagination, truncation, sorting, access requirements, or error behavior. The description is too minimal to fully inform an agent of what to expect.
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, informative sentence that delivers the core information without any unnecessary words or repetition. It is front-loaded and efficiently structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple and the schema covers parameters, but there is no output schema and no annotations. The description does not mention return format, pagination, or other behavioral context, leaving notable gaps for an agent. Adequate for a basic list operation but incomplete.
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 parameters are already well-documented. The description adds little beyond the schema, only mentioning 'optional prefix filtering' which repeats the prefix parameter. Baseline 3 applies when schema covers everything.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and resource ('objects in an S3 bucket') and mentions optional prefix filtering, clearly distinguishing it from siblings like s3_list_buckets and s3_presign_get.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (listing objects with optional filtering) but does not explicitly contrast it with alternatives or state when not to use it. No sibling tool comparison or usage exclusions are provided.
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 only states the action, but does not mention permissions required, side effects (e.g., no object modification), whether object existence is validated, or what the returned URL contains. Minimal behavioral context.
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?
A single, front-loaded sentence that precisely communicates the tool's function without any filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with detailed schema, but no output schema is present and the description does not explicitly state that the return value is the presigned URL string. However, given the low complexity and clear purpose, it is minimally complete.
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?
All three parameters (bucket, key, expiresIn) are fully described in the schema with defaults and max for expiresIn. The description adds no additional parameter meaning beyond what the schema provides, matching the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the specific action: 'Generate a presigned URL for downloading an object from S3.' It identifies the resource (S3 object), the operation (presign for download), and is distinct from sibling list tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use guidance or mention of alternatives, but the sibling tools (s3_list_buckets, s3_list_objects) are semantically different, making the intended use case for generating downloadable links implied. The description does not provide exclusions or prerequisites.
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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