Ollama is an open-source project that allows you to run large language models (LLMs) locally on your own hardware, providing a way to use AI capabilities privately without sending data to external services.
Why this server?
Enables running, managing, and interacting with Ollama models including execution, information retrieval, downloading, listing, deletion, and chat completion
Why this server?
Provides integration with Ollama for local LLM support and embedding generation
Why this server?
Integration with Ollama for local language model inference to power browser automation
Why this server?
Supports local models through Ollama, allowing integration with locally hosted LLMs alongside cloud-based options.
Why this server?
Connects to locally running Ollama models to query multiple AI perspectives on a single question, with support for customizing system prompts and assigning different roles to each model.
Why this server?
Allows access to LLMs hosted through Ollama via the LLM_MODEL_PROVIDER environment variable
Why this server?
Enables research capabilities using any local LLM hosted by Ollama, supporting models like deepseek-r1 and llama3.2
Why this server?
Allows integration with Ollama, enabling use of Ollama models through the MCP interface. Provides capabilities to list models, get model details, and ask questions to Ollama models.
Why this server?
Provides integration with Ollama's LLM server, allowing interactive chat with Ollama models while using the Bybit tools to access cryptocurrency data.
Why this server?
Provides integration with Ollama to run open source models locally, supporting models like Phi-3.
Why this server?
Supports integration with Ollama for local execution of Large Language Models, providing an alternative to cloud-based AI providers.
Why this server?
Supports Ollama as an LLM provider through API key integration
Why this server?
Uses Ollama models to generate text embeddings from Parquet file columns
Why this server?
Allows interaction with locally-hosted Ollama models through a consistent API, supporting models like Llama 3.1.
Why this server?
Provides access to Deepseek reasoning content through a local Ollama server
Why this server?
Provides complete integration with Ollama, allowing users to pull, push, list, create, copy, and run local LLM models. Includes model management, execution of models with customizable prompts, and an OpenAI-compatible chat completion API.
Why this server?
Integration with Ollama's open-source AI models to create blockchain agents for Starknet operations.
Why this server?
Planned future integration to enable running embedding models locally for complete privacy and control
Why this server?
Integrates with Ollama LLMs to enable natural language hardware control through OllamaHardwareAgent
Why this server?
Provides integration with free/open-weights models through Ollama, enabling code analysis and editing without proprietary LLM APIs
Why this server?
Enables integration with Ollama's local models to run MCTS analysis, allowing model selection, comparison between different Ollama models, and storing results organized by model name.
Why this server?
Provides support for local LLM capabilities, allowing the server to use locally hosted models for embeddings and text generation rather than cloud-based solutions.
Why this server?
Integrates with Ollama to allow running local LLMs as an alternative to cloud-based models for processing API interactions.
Why this server?
Leverages Ollama's local AI models (nomic-embed-text, phi4, clip) for document processing, metadata extraction, and vector embeddings of construction documents.
Why this server?
Integrates with Ollama AI models for enhanced code analysis capabilities
Why this server?
Enables using local Ollama models for security validation in the MCP gateway through the OLLAMA_HOST configuration
Why this server?
Provides access to Ollama's local LLM models through a Model Context Protocol server, allowing listing, pulling, and chatting with Ollama models
Why this server?
Offers alternative LLM provider integration for task management functions, allowing use of locally deployed Ollama models for PRD parsing and task suggestions
Why this server?
Allows using Ollama's local language models as an alternative provider for generating embeddings and handling memory operations
Why this server?
Provides local embeddings generation using Ollama's nomic-embed-text model as an alternative to cloud-based embedding services.
Why this server?
Integrates with Ollama for local embedding models, supporting document embedding and semantic search functionality.
Why this server?
Uses Ollama with nomic-embed-text to generate vector embeddings for documents, enabling semantic search capabilities in Solr collections.
Why this server?
Enables communication between Unity and local Large Language Models (LLMs) running through Ollama, allowing developers to automate Unity workflows, manipulate assets, and control the Unity Editor programmatically without cloud-based LLMs.
Why this server?
Mentioned as a planned feature for local embeddings generation as an alternative to OpenAI embeddings
Why this server?
Supports integration with Ollama through MCPHost as a free alternative to Claude, enabling LLMs to interact with the MCP server
Why this server?
Provides a standardized interface for interacting with Ollama's API, supporting model listing, chat functionality, text generation, embedding generation, and querying running models and model details.
Why this server?
Provides integration with Ollama's local LLM hosting service, supporting customizable context settings and model parameters for browser automation
Why this server?
Optionally connects to an Ollama server for prompt generation using LLMs hosted on Ollama
Why this server?
Uses Ollama's embedding models (particularly nomic-embed-text) for creating vector embeddings for documentation search
Why this server?
Used for the default summarization and embedding models required by the server, specifically the snowflake-arctic-embed2 and llama3.1:8b models.
Why this server?
Provides free embeddings for vector representation of documents
Why this server?
Provides integration with Ollama using the Mistral model, allowing AI agents to interact with and leverage the model's capabilities through the MCP protocol
Why this server?
Utilizes Ollama server for embedding generation to support the similarity search functionality.
Why this server?
Mentioned as a potential OpenAI API-compatible endpoint that can be used with the MCP server through the openai_compatible provider setting.
Why this server?
Uses locally running Ollama models to process natural language commands, with the ability to switch between different models like llama3.2 or Gemma3, and query available models from the Ollama server.
Why this server?
Enables integration with local large language models like Mistral, allowing the MCP server to process conversational AI requests without relying on cloud services.
Why this server?
Leverages Ollama's embedding model (nomic-embed-text) to create custom embedding functions for converting text into vector representations that can be searched.
Why this server?
Enables running LLaMA 3.2 3B locally, allowing the MCP to integrate Yahoo Finance data with LLaMA's capabilities
Why this server?
Provides services for generating embeddings and text with Ollama, allowing AI-powered applications to perform embedding generation and text generation operations locally.
Why this server?
Provides integration with Ollama for AI-powered code reviews using local models, allowing the MCP server to utilize Ollama's capabilities to generate expert code reviews based on different programming principles.
Why this server?
Supports exporting fine-tuned models to Ollama format for local deployment and inference.
Why this server?
Generates vector embeddings for emails using models like nomic-embed-text for enhanced semantic search capabilities
Why this server?
Integrates with Ollama as a local LLM provider for context-aware querying. Allows users to send prompts to Ollama models with context from local files.
Why this server?
Enables seamless communication with local Ollama LLM instances, providing capabilities for task decomposition, result evaluation, and direct model execution with configurable parameters.
Why this server?
Uses Ollama for efficient embedding generation, requiring it to be installed and running for vector operations
Why this server?
Provides integration with Ollama for local AI model usage and processing
Why this server?
Uses Ollama as a Large Language Model provider to determine user intent and route requests
Why this server?
Uses Ollama as the default embedding provider for local embeddings generation, supporting semantic documentation search and vector storage.
Why this server?
Integrates with Ollama to use the Deepseek model for AI capabilities through the MCP protocol
Why this server?
Allows querying Ollama models directly from Claude with performance tracking, supporting selection of different models and providing context for queries.
Why this server?
Leverages Ollama's LLM capabilities to interpret natural language questions, generate SQL queries, and provide AI-powered responses based on database results.
Why this server?
Allows communication with locally available Ollama models (like llama2, codellama) while maintaining persistent conversation history.