YAML (YAML Ain't Markup Language) is a human-readable data serialization standard that can be used in conjunction with all programming languages and is often used for configuration files.
Why this server?
Offers YAML formatting functionality for structured data
Why this server?
Supports YAML format for the server's configuration system, allowing users to provide API credentials and other settings.
Why this server?
Supports flexible configuration through YAML files, enabling customization of server behavior and settings.
Why this server?
Supports parsing of YAML-formatted OpenAPI specifications for tool generation
Why this server?
Supports structured metadata using YAML frontmatter in prompt files
Why this server?
Leverages YAML for flexible configuration through configuration files, allowing customization of the time server's behavior
Why this server?
Provides tools for working with OpenAPI specifications in YAML format, allowing splitting and extracting endpoints.
Why this server?
Allows defining prompt templates in YAML format with structured metadata, arguments, and message content.
Why this server?
Supports loading and processing OpenAPI specifications in YAML format.
Why this server?
Enables working with document frontmatter (YAML metadata) for documentation files
Why this server?
Analyzes YAML files for infrastructure as code misconfigurations and security vulnerabilities.
Why this server?
Supports parsing OpenAPI specifications in YAML format to generate mock API servers
Why this server?
Enables defining and configuring multiple API endpoints using YAML syntax, supporting various HTTP methods, parameter generation, and request configurations.
Why this server?
Uses YAML configuration files to manage service configurations and determine which third-party enrichment services are supported for observable lookups
Why this server?
The server uses YAML for Smithery configuration, defining required parameters and integration with Claude Desktop.
Why this server?
Supports YAML as an output format for viewing OpenAPI specifications
Why this server?
Uses YAML for configuration files, including server definitions and connection settings.
Why this server?
Uses YAML configuration files to define custom cognitive frameworks and memory structures for the model.
Why this server?
Uses YAML frontmatter for document metadata in knowledge files, supporting properties like title and keywords for better organization
Why this server?
Supports configuration through YAML files, allowing customization of the MCP server behavior
Why this server?
Supports creation of Kubernetes resources from YAML manifests, enabling deployment of complex resources through the createorUpdateResource tool.
Why this server?
Supports YAML as a configuration format for defining server settings, connection parameters, and authentication details.
Why this server?
Processes Swagger/OpenAPI documentation in YAML format, enabling the parsing and exploration of API definitions specified in YAML.
Why this server?
Supports OpenAPI specifications in YAML format, allowing the MCP server to process and serve API documentation from YAML files.
Why this server?
Validates Ilograph diagrams written in YAML syntax, providing detailed error messages and suggestions for correction.
Why this server?
Used for bridge and tool definitions through YAML files in the bridges directory
Why this server?
Uses YAML files for policy definition and configuration, allowing customizable policy rules that can be enforced through the server's policy compliance tool
Why this server?
Uses YAML for configuration of reasoning strategies and model orchestration settings
Why this server?
Supports OpenAPI specifications defined in YAML format
Why this server?
The MCP server uses YAML configuration files for configuring txtai components during knowledge base building
Why this server?
Uses YAML for configuration of LLM environments, including resources, prompts, tools, and global settings.
Why this server?
Supports parsing and serialization of YAML for configuration and data exchange.
Why this server?
Offers extraction and formatting of key-value pairs into YAML format with type safety and validation.
Why this server?
Supports parsing and handling YAML Swagger definition files to extract API information and generate compatible code.
Why this server?
Uses YAML for configuration files including hosts, groups, and defaults, allowing structured definition of network devices and their properties.
Why this server?
Supports token storage in YAML format (default), allowing credentials and authentication data to be saved and retrieved in YAML files.
Why this server?
Provides a configuration format for defining MCP tool mappings to HTTP APIs, allowing users to specify server settings and tool definitions through structured YAML files.
Why this server?
Uses YAML configuration files to define tools and commands to be executed by the MCP server.
Why this server?
Provides a template system for defining and managing prompt templates without coding knowledge
Why this server?
Used for the workspace configuration with pnpm-workspace.yaml mentioned in the project structure.
Why this server?
Supports OpenAPI specs in YAML format
Why this server?
Supports working with Semgrep rules defined in YAML format, allowing for rule creation and management.
Why this server?
Supports returning query results in YAML format when interacting with the Cube semantic layers.
Why this server?
Uses YAML for configuration through the smithery.yaml file, which defines the MCP client settings and parameter schemas.
Why this server?
Used for MCP client configuration through the smithery.yaml file, which defines startup parameters and configuration schema for the Careerjet job search functionality.
Why this server?
Uses YAML for configuration management of the MCP server, enabling customization of API keys, logging levels, and connection settings.
Why this server?
Leverages YAML frontmatter for rich prompt metadata including categories, tags, and argument definitions
Why this server?
Enables working with YAML files through conversion workflows, commonly used with static site generators like Hugo and Jekyll.
Why this server?
Uses YAML for the MCP discovery file (model_context.yaml) which enables API discovery and interaction through the provided agent.
Why this server?
Utilizes YAML for pretty printing Bible data responses, making the verse and chapter text more readable when returned from the API.
Why this server?
Supports YAML-formatted configuration files for defining multiple Datasette instances, their connection details, and global server settings.
Why this server?
Supports configuration through YAML files, allowing customization of server settings including port, host, logging level, and enabled plugins.
Why this server?
Uses YAML frontmatter in journal entries for metadata storage and organization
Why this server?
Integrates with an OpenAPI YAML specification (cirro-data-latest.yml) to provide access to the Cirro Bio data platform API.
Why this server?
Employs YAML configuration files to manage tool registration and server settings, allowing users to easily extend the server with additional tools.
Why this server?
Connects to the PokeAPI which is defined using an OpenAPI specification in YAML format, allowing structured access to Pokémon data.
Why this server?
Supports parsing OpenAPI specifications in YAML format, enabling the dynamic conversion of YAML-defined APIs into MCP tools.
Why this server?
Supports structured data formatting for project management information, mentioned as an alternative format for organizing project data.
Why this server?
Includes specialized loaders for YAML files, enabling proper chunking and indexing of YAML configuration documents
Why this server?
Uses YAML for defining extension context information, providing detailed knowledge about PostgreSQL extensions like PostGIS and pgvector.
Why this server?
Uses YAML files for configuring agents and tasks without writing custom code, supporting variable replacement in templates.
Why this server?
Processes OpenAPI schema files in YAML format and presents schema information in YAML format for better LLM comprehension.
Why this server?
Handles compiled pipeline definitions that are stored in Google Cloud Storage and used to trigger Vertex AI training jobs.