Built using Python and provides AWS service recommendations through Python-based tools for suggesting appropriate AWS services based on use case descriptions and browsing AWS services by category.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AWS Advisor MCP Server suggest AWS services for a scalable web application"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that provides AWS service recommendations based on your use case descriptions.
suggest_aws_service: Get AWS service recommendations by describing your use case
list_aws_categories: Browse AWS services organized by category (compute, storage, database, etc.)
Related MCP server: AWS Documentation MCP Server
Install uv (if not already installed):
curl -LsSf https://astral.sh/uv/install.sh | shSync dependencies:
uv syncThis will create a virtual environment and install all dependencies.
Add this server to your Cursor MCP settings:
Open Cursor Settings
Navigate to the MCP section
Add a new server with this configuration:
{
"mcpServers": {
"aws-advisor": {
"command": "uv",
"args": [
"--directory",
"/Users/antonioschaffert/workspace/tony/my-dev-mcp",
"run",
"python",
"src/aws_advisor_server.py"
]
}
}
}Or add it to your MCP config file (usually ~/.cursor/mcp_config.json or similar):
{
"aws-advisor": {
"command": "uv",
"args": [
"--directory",
"/Users/antonioschaffert/workspace/tony/my-dev-mcp",
"run",
"python",
"src/aws_advisor_server.py"
]
}
}Once configured, you can use the tools in Cursor:
"What AWS service should I use for serverless computing?"
"Suggest AWS services for storing images"
"What's the best AWS service for a relational database?"
"Show me AWS services for real-time data streaming"
"List all AWS categories"
suggest_aws_service
Input: use_case (string) - Description of what you want to build
Returns: Recommended AWS services with explanations
list_aws_categories
No input required
Returns: All AWS service categories and their services
You can test the server directly:
uv run python src/aws_advisor_server.pyThen send MCP protocol messages via stdin (for advanced testing).
Compute: EC2, Lambda, ECS, EKS, Fargate, Lightsail
Storage: S3, EBS, EFS, Glacier, FSx
Database: RDS, DynamoDB, Aurora, DocumentDB, ElastiCache, Neptune, Redshift
Networking: VPC, CloudFront, Route53, API Gateway, Direct Connect, ELB
Analytics: Athena, EMR, Kinesis, Glue, QuickSight
ML/AI: SageMaker, Rekognition, Comprehend, Polly, Transcribe, Translate, Lex
Security: IAM, Cognito, KMS, Secrets Manager, WAF, GuardDuty
Messaging: SQS, SNS, EventBridge, SES
Monitoring: CloudWatch, X-Ray, CloudTrail
Python 3.10+
mcp package (>=0.9.0)
List all available AWS service categories (compute, storage, database, etc.) to help you explore services by type.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 describes the tool's purpose and output type ('list all available AWS service categories'), but lacks details on behavioral traits such as rate limits, authentication needs, or whether the list is static or dynamic. The description does not contradict any annotations, but it provides only basic operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose, resource, and usage context. It is front-loaded with the main action ('List all available AWS service categories') and avoids any redundant or extraneous information, making it highly concise and effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no annotations, no output schema), the description is complete enough for basic understanding. However, it lacks details on output format (e.g., list structure, categories as strings or objects) and behavioral aspects like caching or updates, which could be helpful for an AI agent. Without an output schema, the description should ideally hint at the return values, but it only states the purpose broadly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema description coverage is 100% (since there are no parameters to describe). The description does not need to add parameter semantics, but it appropriately explains the tool's function without unnecessary parameter details. A baseline of 4 is applied for zero-parameter tools, as the description adequately covers the tool's purpose without schema redundancy.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List all available AWS service categories') and the resource ('AWS service categories'), with examples of what those categories include ('compute, storage, database, etc.'). It distinguishes from the sibling tool 'suggest_aws_service' by focusing on listing categories rather than suggesting individual services.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('to help you explore services by type'), which implies it's for initial exploration or categorization. However, it does not explicitly state when not to use it or name alternatives (e.g., 'use suggest_aws_service for service recommendations'), so it falls short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get AWS service recommendations based on your use case. Provide a description of what you want to build or accomplish, and get relevant AWS service suggestions.
| Name | Required | Description | Default |
|---|---|---|---|
| use_case | Yes | Description of your use case, requirements, or what you want to build (e.g., 'serverless API', 'store images', 'real-time data processing') |
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 describes the basic function but lacks details on behavioral traits such as response format, whether it's a read-only operation, potential rate limits, or error handling. The description is minimal and doesn't compensate for the absence of annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two efficient sentences that directly state the tool's purpose and usage. There is no wasted text, and it effectively communicates the core information in a structured manner.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no output schema, no annotations), the description is adequate but has gaps. It covers the basic purpose and parameter, but without annotations or output schema, it lacks details on behavioral aspects and return values, making it minimally viable but incomplete for full transparency.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 the single parameter 'use_case' with a clear description. The description adds minimal value by restating the parameter's purpose ('Provide a description...') without providing additional syntax or format details beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get AWS service recommendations based on your use case.' It specifies the verb ('Get recommendations') and resource ('AWS service'), though it doesn't explicitly differentiate from the sibling tool 'list_aws_categories' beyond the recommendation focus. The description is specific but lacks sibling comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool ('based on your use case') and provides an example of what to provide, but it doesn't explicitly state when not to use it or mention alternatives like the sibling tool. Usage is contextually implied rather than explicitly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
list_aws_categoriessuggest_aws_serviceThe two tools have clearly distinct purposes: one lists AWS service categories for exploration, while the other provides service recommendations based on use cases. There is no overlap or ambiguity between these functions.
Both tools follow a consistent verb_noun pattern with 'list_' and 'suggest_' prefixes, and they use snake_case uniformly. The naming is predictable and readable across the set.
With only 2 tools, the server feels thin for its apparent scope of AWS advisory. While the tools cover basic exploration and recommendation, the domain suggests potential for more operations like detailed service comparisons or cost analysis, making the count inadequate.
The server provides a starting point for AWS service discovery but has notable gaps. It lacks tools for deeper analysis, such as comparing services, checking pricing, or managing recommendations, which limits its utility for comprehensive advisory tasks.
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