Fdic Bank Find MCP Server

🚀 FDIC Bank Data API Project Documentation

This project offers a modern FDIC bank data API with multiple features, such as automatically generating code to handle YAML definition files, validating parameters, and supporting dynamic response fields. The code is written in Rust and developed with the assistance of AI tools like Windsurf Editor and GPT - 4.1.

✨ Features

1. Automatic Generation of YAML - handling Code

The project can parse the YAML definition files provided by the FDIC and automatically generate corresponding Rust code to handle these definitions. This significantly enhances the API's scalability and maintenance efficiency.

2. Parameter Validation and Data Processing

It implements parameter validation logic to ensure the validity of requests and formats the returned data to comply with the OpenAPI specification.

3. Support for Dynamic Response Fields

Some interfaces, such as /institutions, return additional fields (e.g., highlighting and scoring) based on the query results. These fields are directly sourced from the underlying search engine (e.g., Elasticsearch).

🚀 Quick Start

📦 Installation

There is no installation - related content in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

// Example: Parse a YAML file and generate handling code
fn generate_handler(yaml_path: &str) -> Result<String, Box<dyn Error>> {
    let yaml = std::fs::read_to_string(yaml_path)?;
    let spec: Spec = serde_yaml::from_str(&yaml)?;
    // Generate corresponding code based on spec
    Ok(format!("pub fn handler(req: Request) -> Response {{\n  // Handling logic\n}}"))
}

Advanced Usage

// Example: Automatically generate route registration code
#[automatically_registered]
mod handlers {
    use super::*;

    #[get("/institutions")]
    pub fn get_institutions() -> Response {
        // Handling logic
    }
}

📚 Documentation

Contribution Guidelines

1. Submitting Issues and Feature Requests

Please submit issues or feature requests using the ISSUE_TEMPLATE in the repository.

2. Making Code Contributions

• Branch names should follow the feature/<issue_id> convention.
• Before submitting a PR, ensure that you run comprehensive tests and clean up irrelevant commits.

3. Adhering to the Code Style Guide

The code must comply with the Rust formatting specification, maintain high readability, and pass the CI/CD pipeline checks.

Governance and Documentation

Specification Files

• CONTRIBUTING.md: Contribution guidelines
• CODE_OF_CONDUCT.md: Code of conduct
• SECURITY.md: Security specification
• LICENSE: Project license

Open Resources

All open resources are hosted in the project's GitHub repository. The community is welcome to participate in their maintenance.

Explanation of Dynamic Response Fields

Example Response

{
  "type": "success",
  "data": [
    {
      "data": { "NAME": "Wells Fargo Bank", ... },
      "highlight": { "NAME.raw": ["<em>Wells</em> ..."] },
      "score": 1091.385
    }
  ],
  "meta": { ... }
}

Client Considerations

• The data object is always returned, but it may contain optional highlight and score fields.
• These fields help clients or proxies interpret and sort the results.

Conclusion

This project demonstrates how to combine modern programming languages with AI tools to build an efficient and reliable API service. Community developers are welcome to actively participate and jointly promote the project's development.