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DatabasesQuality Score: 34

Notion API MCP Configuration

The Notion API MCP configuration provides a hosted JSON schema that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Notion API API via the Model Context Protocol. This configuration maps 10 API endpoints as callable tools, including Retrieve a block, Delete a block, Update a block, and more. No authentication credentials are needed — it works out of the box. The configuration is auto-generated from the Notion API OpenAPI specification (v1.0.0) and has a quality score of 34/99 (fair documentation coverage). Use the hosted URL below to auto-load this schema into any compatible MCP client.

Quick Specs Reference

AuthenticationNo Auth Required
Available Endpoints10 tools mapped
Integration Modeauto Generation

Hosted Config URL

Use this hosted URL in any client that supports remote MCP schema auto-loading.

https://mcpbridge.org/config/notion-com.json

One-Click Client Setup

Copy the configurations below to wire your local coding assistant directly.

Claude Desktop

claude_desktop_config.json
{
  "mcpServers": {
    "notion-com": {
      "command": "npx",
      "args": [
        "-y",
        "@mcp/notion-com"
      ],
      "env": {
        "NOTION_API_API_KEY": "your_notion_api_api_key"
      }
    }
  }
}

Cursor & VS Code

MCP Server URL Setup
{
  "mcpServers": {
    "notion-com": {
      "url": "https://mcpbridge.org/config/notion-com.json"
    }
  }
}

Raw Configuration JSON

For local command line wrappers or dynamic shell bindings.

{
  "mcpServers": {
    "notion-com": {
      "command": "npx",
      "args": ["-y","@mcp/notion-com"],
      "env": {
      "NOTION_API_API_KEY": "your_notion_api_api_key"
}
    }
  }
}

Required Environment Keys

Substitute these secrets inside your configuration directory environment definitions.

NOTION_API_API_KEY
Replace your_notion_api_api_key with your secret key credential

Mapped Web APIs & Tools

The following routes will be exposed directly as protocol tools for the LLM.

GET/v1/blocks/{id}

Retrieve a block

DELETE/v1/blocks/{id}

Delete a block

PATCH/v1/blocks/{id}

Update a block

GET/v1/blocks/{id}/children

Retrieve block children

PATCH/v1/blocks/{id}/children

Append block children

GET/v1/comments

Retrieve comments

GET/v1/databases/{id}

Retrieve a database

PATCH/v1/databases/{id}

Update a database

POST/v1/databases/{id}/query

Query a database

GET/v1/pages/{id}

Retrieve a Page

Similar Configurations

PostgreSQL (MCP)

Query and manage PostgreSQL databases directly from your AI agent. Read schemas, run queries, and manage data.

https://mcpbridge.org/config/postgres.json

Amazon CloudWatch Application Insights

Amazon CloudWatch Application Insights is a specialized observability service provided by Amazon Web Services (AWS) designed to simplify the monitoring and troubleshooting of applications, particularly those built on Microsoft IIS and .NET frameworks running on EC2 instances or within Elastic Beanstalk environments. Its core capability lies in automatically discovering application components, analyzing correlated metrics, logs, and traces to identify anomalies, and then surfacing actionable insights that pinpoint the root cause of common operational issues. By integrating seamlessly with other AWS services like CloudWatch, AWS X-Ray, and AWS Systems Manager, it provides a unified view of application health, reducing the mean time to resolution (MTTR) for performance degradations and errors. The typical use case spans enterprise environments managing distributed microservices or monolithic .NET applications, where teams need to proactively detect issues such as memory leaks, high CPU utilization, or specific application errors without manually configuring complex monitoring dashboards and alarms. When exposed as tools to an AI coding assistant through the Model Context Protocol (MCP), the Amazon CloudWatch Application Insights API becomes a powerful asset for intelligent development and operations automation. An AI agent, such as one integrated into Claude Desktop or Cursor, can leverage these endpoints to perform context-aware diagnostics and infrastructure adjustments directly within a developer's workflow. For instance, an AI could use the `DescribeApplication` and `DescribeComponent` tools to instantly fetch the current health status and configuration of a running application, providing a developer with a real-time summary during a debugging session. It could then utilize `DescribeComponentConfigurationRecommendation` to suggest optimal monitoring settings based on AWS best practices, or dynamically call `CreateLogPattern` to ingest new error logs identified during an AI-assisted code review, thereby automating the setup of precise observability for newly added application features. This transforms the AI from a passive code generator into an active participant in the application lifecycle, capable of bridging the gap between code deployment and operational monitoring. Practical workflows enabled by this MCP integration include dynamic infrastructure provisioning and reactive incident response. A developer could instruct the AI agent: "Analyze the error logs from the last deployment and, if a database connection timeout pattern is detected, create a new CloudWatch Application Insights component for the database tier and configure a log pattern to capture all related timeout events." The AI would execute the sequence by first querying logs, then using `CreateApplication` and `CreateComponent` to structure the monitoring, followed by `CreateLogPattern` to focus on the relevant data. Another scenario involves automated optimization: "Review the current monitoring configuration for my 'Checkout' service, compare it against the recommended settings, and apply the recommendations where they improve visibility into latency." Here, the AI would chain `DescribeComponentConfiguration`, `DescribeComponentConfigurationRecommendation`, and then update the configuration accordingly, automating a best-practice audit that would otherwise require manual console navigation and comparison. Despite the API endpoint listing showing "None" for authentication, all actions within Amazon CloudWatch Application Insights are governed by AWS Identity and Access Management (IAM) policies. Critical security best practices include enforcing the principle of least privilege by granting only the specific permissions required for the intended task, such as `cloudwatch:Describe*` for read-only access or `cloudwatch:Create*` and `cloudwatch:Delete*` for management functions. It is essential to use IAM roles with temporary credentials for any AI agent integration, never embedding long-term access keys in configuration files. Furthermore, network security should be maintained by ensuring the API calls originate from within a trusted VPC or are secured via AWS PrivateLink if applicable, and all access should be monitored and audited through AWS CloudTrail to maintain a compliance trail for any automated changes made by the AI assistant.

https://mcpbridge.org/config/amazonaws-com-application-insights.json

Application Auto Scaling

The Application Auto Scaling API, provided by Amazon Web Services (AWS), is a robust service designed to automate the scaling of computing resources for a wide array of AWS services, ensuring optimal performance, availability, and cost efficiency. Its core capability is to define policies that automatically adjust the provisioned capacity of supported resources in response to changing demand, as measured by CloudWatch metrics or predefined schedules. Beyond the initially listed resources, it supports scaling for Amazon DynamoDB tables and global secondary indexes, Amazon ECS services running on Fargate or EC2, Amazon ElastiCache replication groups, Amazon Neptune clusters, Amazon SageMaker endpoint variants, and custom resources via the AWS Lambda-backed scalable target. This makes it a central tool for architects and DevOps engineers in building resilient, self-optimizing cloud architectures. Typical enterprise use cases include dynamically adjusting the number of Aurora read replicas to handle database query load spikes, scaling ECS task counts during peak traffic for a microservices application, or optimizing costs by scaling down SageMaker inference endpoints during off-hours. When exposed as tools to an AI coding assistant via the Model Context Protocol (MCP), this API gains significant contextual power. An AI agent, such as Claude or a specialized coding assistant, can directly inspect, reason about, and manipulate an application's scaling configuration in real-time. The value lies in transforming static infrastructure code or manual console operations into dynamic, conversational management. The AI can query the current scaling state (e.g., "describe all registered scalable targets and their current capacity"), analyze scaling activity logs to diagnose performance issues (e.g., "what scaling activities occurred on my ECS service in the past hour?"), or even propose and validate configuration changes (e.g., "draft a scaling policy to maintain average CPU at 40% for my Aurora cluster"). This creates a powerful feedback loop where the AI assistant can act as an expert collaborator, helping developers quickly understand, debug, and evolve their auto-scaling strategies. Practical workflows enabled by this MCP server are numerous and impactful. A developer could instruct the AI: "List all my scalable targets for Amazon Aurora and describe their current scaling policies to check for misconfigurations." The agent would execute the corresponding DescribeScalableTargets and DescribeScalingPolicies calls, then summarize the findings, perhaps flagging a policy with an aggressive cooldown period. Another dynamic task could be: "For my ECS service named 'checkout-service,' create a scheduled action to scale out to 10 tasks every weekday at 9 AM EST and scale in to 3 tasks at 5 PM EST." The AI would use PutScheduledAction to implement this, verifying the time zone and parameters. Furthermore, an AI agent could be tasked with cleanup and optimization: "Identify any scaling policies for DynamoDB tables that have not triggered a scaling activity in 30 days and suggest whether to keep or delete them, then remove the unused ones." This involves querying DescribeScalingActivities and then calling DeleteScalingPolicy based on the analysis, automating routine maintenance. Critical for implementation are authentication and security, as the API actions perform privileged infrastructure changes. While the endpoint list notes "None" for authentication, in a real-world deployment, this API must be invoked with temporary AWS credentials obtained through an IAM role or user with precisely scoped permissions. Developers must adhere to the principle of least privilege when creating the policy document for the AI assistant's execution role. Permissions should be narrowly tailored to only the necessary actions and resource ARNs. For example, a role might allow `application-autoscaling:DescribeScalableTargets` and `application-autoscaling:PutScalingPolicy` only for a specific service namespace and resource ID, preventing unintended modifications. All API calls should be logged via AWS CloudTrail for auditability. It is also essential to ensure that the MCP server configuration securely manages any AWS credentials or role assumptions, preferably through environment variables or a secure secret manager, and never hardcodes them.

https://mcpbridge.org/config/amazonaws-com-application-autoscaling.json

AWS Cost Explorer Service

The AWS Cost Explorer API, provided by Amazon Web Services, serves as the programmatic backbone for the Cost Explorer service, a powerful tool designed to help organizations visualize, understand, and manage their AWS cloud spending. At its core, this API enables developers and financial operations (FinOps) teams to move beyond the web console and directly query their cost and usage data, unlocking the ability to build custom dashboards, automated reports, and sophisticated cost management applications. Its capabilities range from retrieving high-level aggregated data, such as monthly service costs or daily usage totals, to drilling down into granular, resource-level details, including the specific write operations of a DynamoDB table or the data transfer metrics of an EC2 instance. This granular access is critical for enterprises implementing showback/chargeback models, identifying optimization opportunities, and enforcing budget guardrails across complex, multi-account AWS environments. When exposed as a set of tools via the Model Context Protocol (MCP) to an AI coding assistant, the Cost Explorer API transforms from a query tool into a dynamic, conversational FinOps engine. An AI agent like Claude can leverage these tools to provide unprecedented, actionable cost intelligence directly within a developer's workflow. Instead of manually constructing API calls or navigating the console, a developer can simply ask the AI to "analyze our EC2 spending in the EU-West region over the last 90 days and identify any instances with consistently low utilization" or "compare our month-over-month costs for the RDS service and summarize the top three drivers of the increase." The AI can orchestrate calls to endpoints like `GetCostAndUsage` for data retrieval, then use anomaly-focused tools like `GetAnomalies` and `GetAnomalyMonitors` to proactively surface unexpected spending spikes, effectively acting as a real-time cost advisor that contextualizes financial data against technical usage patterns. Practical workflows enabled by this MCP integration are highly dynamic and task-oriented. A developer can instruct the AI agent to "create a new anomaly monitor to watch for daily cost variances greater than 15% in our production account" by invoking the `CreateAnomalyMonitor` tool. The AI can then use the `DescribeCostCategoryDefinition` tool to understand the existing cost allocation tags and, based on a developer's rule, automatically create a new `CostCategoryDefinition` to segment costs by team or project. For ongoing management, a developer could command, "Generate a daily summary of all anomalies detected in the last 24 hours and draft an email report," prompting the AI to chain calls to `GetAnomalies` and format the results. This allows for the automation of reporting, the proactive management of cost controls, and the acceleration of optimization initiatives by embedding expert cost analysis directly into the development and DevOps toolchain. It is critical to note that while the service name in the API target header is "AWSInsightsIndexService," authentication is not "None" in practice. The API is secured through standard AWS Identity and Access Management (IAM) authentication mechanisms. All requests must be signed using the AWS Signature Version 4 process, which requires valid IAM credentials (access key and secret access key) from an authorized IAM user or role. Security best practices are paramount: developers should adhere strictly to the principle of least privilege, creating IAM policies that grant only the specific Cost Explorer API actions required for a tool's function (e.g., `ce:GetCostAndUsage`, `ce:CreateAnomalyMonitor`) and scoping them to specific resources or periods where possible. Credentials should never be hardcoded; instead, environment variables, AWS roles for services (like Lambda), or the default credential provider chain should be used. Furthermore, API calls should be monitored via AWS CloudTrail, and any MCP server integration must ensure secure handling and storage of any temporary credentials used to interact with this sensitive financial data.

https://mcpbridge.org/config/amazonaws-com-ce.json