{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-patruff-ollama-mcp-bridge","slug":"mcp-patruff-ollama-mcp-bridge","name":"ollama-mcp-bridge","type":"mcp","url":"https://github.com/patruff/ollama-mcp-bridge","page_url":"https://unfragile.ai/mcp-patruff-ollama-mcp-bridge","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_0","uri":"capability://tool.use.integration.dynamic.tool.discovery.and.registration.from.mcp.servers","name":"dynamic-tool-discovery-and-registration-from-mcp-servers","description":"Automatically discovers available tools from connected MCP servers by establishing stdio-based connections to MCP server processes, parsing their tool list responses, and registering tools with their schemas, descriptions, and input parameters into a DynamicToolRegistry. The bridge maintains a mapping between tool names and their originating MCP clients, enabling runtime tool availability without hardcoding tool definitions.","intents":["I want my local LLM to automatically access all tools exposed by my MCP servers without manual configuration","I need to add new MCP servers and have their tools immediately available to the LLM without code changes","I want to see what tools are available from my connected MCP servers at runtime"],"best_for":["developers building local-first AI agents with pluggable tool ecosystems","teams migrating from cloud-based LLM APIs to self-hosted Ollama with tool access","builders prototyping multi-tool workflows without tight coupling to specific tool implementations"],"limitations":["Tool discovery happens at bridge initialization — adding new MCP servers requires restart","No caching of tool schemas — each bridge restart re-queries all MCP servers","Assumes MCP servers are stable and respond to tool list requests within reasonable timeout","No conflict resolution if multiple MCP servers expose tools with identical names"],"requires":["Node.js 18+","MCP servers installed and accessible via command-line execution","bridge_config.json with mcpServers section defining command and args for each server","Ollama running locally on configured baseUrl (default http://localhost:11434)"],"input_types":["JSON configuration (bridge_config.json)","MCP server process responses (tool list, tool schemas)"],"output_types":["DynamicToolRegistry object with registered tools","Tool metadata (name, description, input schema, keywords)"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_1","uri":"capability://automation.workflow.mcp.server.process.lifecycle.management","name":"mcp-server-process-lifecycle-management","description":"Manages the full lifecycle of MCP server processes including spawning child processes via Node.js child_process with stdio piping, establishing bidirectional JSON-RPC communication channels, handling process errors and disconnections, and graceful shutdown. Each MCP server runs as an isolated subprocess with its own stdio streams connected to the MCPClient for message routing.","intents":["I need to run multiple MCP servers as separate processes and communicate with them reliably","I want to handle MCP server crashes or disconnections without crashing the entire bridge","I need to ensure MCP servers are properly cleaned up when the bridge shuts down"],"best_for":["developers building resilient local-first agent systems with multiple tool providers","teams running MCP servers with varying stability guarantees and resource requirements","builders needing isolation between tool execution contexts"],"limitations":["Process spawning adds ~50-200ms overhead per MCP server at initialization","No built-in process restart on failure — requires external orchestration for high availability","Stdio-based communication has no built-in backpressure handling for large tool responses","Windows compatibility depends on Node.js child_process behavior across platforms"],"requires":["Node.js 18+ with child_process module","MCP server executables accessible at paths specified in bridge_config.json","Proper file permissions for executing MCP server binaries","Sufficient system resources to spawn multiple child processes"],"input_types":["MCP server command and arguments from configuration","JSON-RPC messages from bridge to MCP servers"],"output_types":["Child process handles","JSON-RPC responses from MCP servers","Process error/exit events"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_10","uri":"capability://safety.moderation.error.handling.and.tool.failure.recovery","name":"error-handling-and-tool-failure-recovery","description":"Handles errors from MCP server tool calls by catching exceptions during tool execution, formatting error messages, and passing them back to the LLM as part of the conversation context. The LLM can then see the error and attempt alternative approaches or ask for clarification. Errors from MCP servers are converted to readable messages for the LLM.","intents":["I want the LLM to handle tool failures gracefully instead of crashing","I need the LLM to see tool errors and adapt its behavior","I want to understand why tools failed when debugging issues"],"best_for":["developers building resilient agentic systems","teams using unreliable external tools or services","builders implementing error recovery strategies in LLM workflows"],"limitations":["Error messages from MCP servers may be opaque — difficult to debug root causes","No automatic retry logic — LLM must decide whether to retry","Error handling depends on LLM's ability to understand and respond to errors","No structured error codes — errors are passed as text strings"],"requires":["Try-catch blocks around tool execution","Error message formatting logic","LLM capable of understanding error messages and adapting behavior"],"input_types":["tool execution exceptions","MCP server error responses"],"output_types":["formatted error messages (string)","error context for LLM"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_11","uri":"capability://text.generation.language.system.prompt.customization.with.tool.instructions","name":"system-prompt-customization-with-tool-instructions","description":"Allows customization of the system prompt via bridge_config.json, with support for dynamic tool-specific instruction injection when relevant tools are detected. The base system prompt is loaded from configuration, then tool-specific instructions are appended when the bridge detects that certain tools are needed for the user's request, enabling model-specific guidance for tool usage.","intents":["I want to customize how the LLM behaves and what instructions it follows","I need to provide tool-specific guidance to improve tool usage accuracy","I want different prompts for different tools or use cases"],"best_for":["developers fine-tuning LLM behavior for specific use cases","teams optimizing tool calling accuracy for their models","builders implementing model-specific prompt engineering strategies"],"limitations":["Tool-specific instructions are static — no dynamic generation based on context","No prompt versioning — changes require manual configuration updates","Instruction injection happens before LLM invocation — cannot adapt based on LLM's reasoning","No A/B testing framework for comparing different prompts"],"requires":["systemPrompt field in bridge_config.json","Tool-specific instruction strings in tool registry","LLM capable of following complex instructions"],"input_types":["base system prompt (string)","tool-specific instructions (string)","user message"],"output_types":["final system prompt with injected instructions (string)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_2","uri":"capability://planning.reasoning.intelligent.tool.detection.from.user.prompts","name":"intelligent-tool-detection-from-user-prompts","description":"Analyzes user messages to detect which tools from the registered tool registry are likely needed by matching keywords, tool descriptions, and semantic intent patterns. The DynamicToolRegistry maintains keyword mappings for each tool and the bridge uses these to identify relevant tools before sending the message to the LLM, enabling tool-specific instruction injection and optimized context window usage.","intents":["I want the LLM to automatically know which tools to use based on what the user asks","I need to inject tool-specific instructions only when relevant tools are detected","I want to optimize context usage by only including relevant tool schemas in the prompt"],"best_for":["developers building multi-tool LLM agents where tool selection matters for performance","teams using smaller LLMs (7B-13B parameters) where context efficiency is critical","builders implementing tool-specific prompt engineering strategies"],"limitations":["Keyword-based detection is brittle for synonyms or indirect tool references","No semantic understanding — relies on exact keyword matches rather than embedding-based similarity","Cannot detect tools needed for multi-step reasoning chains where intermediate steps require tools","Detection happens before LLM processing — may miss tools needed based on LLM's reasoning"],"requires":["DynamicToolRegistry populated with tool keywords","User message as text input","Tool descriptions and keywords properly configured in MCP server responses"],"input_types":["user message (string)","tool registry with keyword mappings"],"output_types":["array of detected tool names","tool-specific instructions (string)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_3","uri":"capability://text.generation.language.ollama.compatible.llm.client.with.tool.calling","name":"ollama-compatible-llm-client-with-tool-calling","description":"Wraps the Ollama API (OpenAI-compatible endpoint at baseUrl/v1/chat/completions) with a custom LLMClient that formats tool schemas as JSON in system prompts, sends messages with tool context, and parses tool-call responses from the LLM. Supports configurable temperature, max_tokens, and model selection, with built-in parsing of tool invocation patterns from LLM output.","intents":["I want to use any Ollama-compatible model with tool calling without modifying my LLM code","I need to pass tool schemas to the LLM in a format it understands and can act on","I want to parse tool calls from the LLM's text output and route them to the correct MCP server"],"best_for":["developers using open-source LLMs via Ollama (Llama 2, Mistral, DeepSeek, etc.)","teams avoiding cloud LLM APIs for privacy or cost reasons","builders experimenting with different model sizes and architectures"],"limitations":["Ollama API compatibility limited to models that support function-calling-like behavior through prompt engineering","No native function-calling support — tool invocations are parsed from text output, not structured JSON","Tool calling reliability depends on model instruction-following ability — smaller models may fail to invoke tools correctly","No built-in retry logic for failed tool calls or malformed LLM responses"],"requires":["Ollama running on configured baseUrl (default http://localhost:11434)","Model downloaded and available in Ollama (e.g., ollama pull deepseek-v2:16b)","Network connectivity to Ollama endpoint","API key set to 'ollama' (default, no authentication required for local Ollama)"],"input_types":["user message (string)","system prompt with tool schemas (string)","tool registry with tool definitions"],"output_types":["LLM response text","parsed tool calls with arguments","tool execution results"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_4","uri":"capability://planning.reasoning.multi.turn.conversation.with.tool.execution.loops","name":"multi-turn-conversation-with-tool-execution-loops","description":"Implements a message processing loop in MCPLLMBridge that handles multi-turn conversations where the LLM can invoke tools, receive results, and continue reasoning. The bridge detects tool calls in LLM responses, executes them via the appropriate MCP client, appends results to the conversation history, and re-invokes the LLM until it produces a final response without tool calls. Maintains full conversation context across turns.","intents":["I want the LLM to use tools iteratively to solve complex problems that require multiple steps","I need the LLM to see tool execution results and adapt its reasoning based on those results","I want to maintain conversation history across multiple tool calls and LLM invocations"],"best_for":["developers building agentic workflows where tools inform subsequent reasoning","teams implementing research or analysis tasks requiring iterative tool use","builders creating interactive assistants that refine answers based on tool feedback"],"limitations":["Each tool execution loop adds latency — typical 2-5 second overhead per loop depending on tool and LLM response time","No built-in loop termination detection — relies on LLM eventually producing non-tool-calling response","Token usage grows with each turn — long conversations may exceed model context windows","No explicit error handling for tool failures — tool errors are passed to LLM as text, which may confuse smaller models"],"requires":["MCPLLMBridge initialized with LLMClient and MCP clients","Tool registry populated with available tools","Ollama model capable of following tool-calling instructions","Sufficient context window to handle conversation history plus tool results"],"input_types":["user message (string)","conversation history (array of messages)","tool registry"],"output_types":["final LLM response (string)","conversation history with all tool calls and results","tool execution logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_5","uri":"capability://tool.use.integration.json.rpc.based.mcp.protocol.implementation","name":"json-rpc-based-mcp-protocol-implementation","description":"Implements the Model Context Protocol using JSON-RPC 2.0 over stdio, with MCPClient handling message serialization, request/response correlation via message IDs, and error handling. Supports MCP methods like tools/list, tools/call, and resource operations through a standardized JSON-RPC request/response pattern with proper error codes and result handling.","intents":["I need to communicate with MCP servers using the standard MCP protocol","I want to call tools on MCP servers and handle their responses reliably","I need to handle MCP protocol errors and edge cases properly"],"best_for":["developers integrating with MCP-compliant tool servers","teams building MCP client implementations","builders needing standards-based tool protocol support"],"limitations":["JSON-RPC over stdio has no built-in backpressure — large tool responses may cause buffer issues","No multiplexing — requests are processed sequentially, not in parallel","Timeout handling is basic — no configurable request timeouts per operation","Error messages from MCP servers may be opaque and difficult to debug"],"requires":["MCP servers implementing JSON-RPC 2.0 protocol","Node.js with proper stdio stream handling","Understanding of MCP protocol specification"],"input_types":["JSON-RPC requests (method, params, id)","MCP server responses"],"output_types":["JSON-RPC responses with results or errors","tool execution results"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_6","uri":"capability://automation.workflow.configuration.driven.bridge.initialization","name":"configuration-driven-bridge-initialization","description":"Loads bridge configuration from bridge_config.json specifying LLM settings (model, baseUrl, temperature, maxTokens), MCP server definitions (command, args), and system prompt. The bridge parses this configuration at startup and uses it to initialize LLMClient with Ollama parameters and spawn MCP server processes with their configured commands and arguments, enabling zero-code configuration of the entire system.","intents":["I want to configure which LLM model and MCP servers to use without modifying code","I need to change LLM parameters like temperature and max tokens via configuration","I want to add or remove MCP servers by editing a JSON file"],"best_for":["non-technical users deploying pre-built bridge instances","teams managing multiple bridge configurations for different use cases","developers prototyping different LLM and tool combinations"],"limitations":["No schema validation — invalid configuration may cause cryptic runtime errors","No environment variable substitution — all paths and URLs must be hardcoded","Configuration changes require bridge restart — no hot-reload support","No configuration versioning or migration path for breaking changes"],"requires":["bridge_config.json in the working directory","Valid JSON syntax in configuration file","Paths to MCP server binaries must be absolute or relative to working directory","Ollama baseUrl must be accessible and running"],"input_types":["bridge_config.json (JSON)"],"output_types":["LLMClient configuration object","MCP server process definitions","system prompt string"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_7","uri":"capability://automation.workflow.command.line.interface.with.interactive.tool.listing","name":"command-line-interface-with-interactive-tool-listing","description":"Provides a command-line REPL interface in main.ts that accepts user messages, displays LLM responses, and supports special commands like 'list-tools' to enumerate available tools with their descriptions and 'quit' to exit. The CLI reads from stdin, processes messages through the bridge, and formats output for terminal display.","intents":["I want to interact with the LLM and its tools via a simple command-line interface","I need to see what tools are available before using them","I want a quick way to test the bridge without building a custom UI"],"best_for":["developers testing bridge functionality locally","non-technical users interacting with the LLM via terminal","teams prototyping before building custom interfaces"],"limitations":["No multi-line input support — each line is treated as a separate message","No command history or readline features — basic stdin/stdout only","Tool listing shows raw schema — not user-friendly for non-developers","No session persistence — conversation history is lost on exit"],"requires":["Node.js runtime","Terminal/console for stdin/stdout","Bridge initialized and ready to process messages"],"input_types":["user text input from stdin","special commands (list-tools, quit)"],"output_types":["LLM responses to stdout","tool listings","status messages"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_8","uri":"capability://text.generation.language.tool.schema.to.prompt.injection","name":"tool-schema-to-prompt-injection","description":"Converts tool schemas from the DynamicToolRegistry into JSON-formatted tool descriptions that are injected into the LLM system prompt. The bridge constructs a tools section in the prompt listing each tool's name, description, and input schema in a format the LLM can parse and understand, enabling the model to decide when and how to invoke tools based on the schema information.","intents":["I want the LLM to understand what tools are available and how to use them","I need to format tool schemas in a way that works with my LLM model","I want to provide tool-specific instructions to guide the LLM's tool usage"],"best_for":["developers using open-source LLMs that require explicit tool schema information","teams fine-tuning prompt engineering for tool calling","builders experimenting with different schema formats for different models"],"limitations":["Schema injection increases prompt length — may reduce available context for user messages","No schema optimization — all tools are included even if not relevant to the user's request","LLM understanding of schemas depends on model training — smaller models may struggle","No validation that the LLM actually understands the schema format"],"requires":["Tool registry with properly formatted schemas","LLM capable of parsing JSON in prompts","Sufficient context window to accommodate tool schemas plus conversation"],"input_types":["tool registry with tool definitions","tool schemas (JSON objects)"],"output_types":["formatted tool descriptions (string)","system prompt with tool section (string)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-patruff-ollama-mcp-bridge__cap_9","uri":"capability://tool.use.integration.mcp.server.tool.call.routing.and.execution","name":"mcp-server-tool-call-routing-and-execution","description":"Routes tool calls from the LLM to the correct MCP server by looking up the tool name in the tool registry to find the associated MCP client, then invoking the tool via that client's tools/call JSON-RPC method with the provided arguments. Handles tool execution errors and returns results back to the LLM for further processing.","intents":["I need to execute tools on MCP servers when the LLM requests them","I want to route tool calls to the correct MCP server without manual mapping","I need to handle tool execution failures gracefully"],"best_for":["developers building multi-tool LLM systems with multiple MCP servers","teams needing reliable tool execution with error handling","builders implementing agentic workflows with tool dependencies"],"limitations":["No built-in retry logic — tool failures are passed to LLM as-is","Tool execution is synchronous — blocks LLM processing until tool completes","No timeout enforcement — long-running tools may hang the bridge","Tool argument validation is minimal — relies on MCP server to validate inputs"],"requires":["Tool registry with tool-to-MCP-client mappings","MCP clients initialized and connected to their servers","Tool name and arguments from LLM output"],"input_types":["tool name (string)","tool arguments (object)","tool registry"],"output_types":["tool execution result (any)","error messages if tool fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 18+","MCP servers installed and accessible via command-line execution","bridge_config.json with mcpServers section defining command and args for each server","Ollama running locally on configured baseUrl (default http://localhost:11434)","Node.js 18+ with child_process module","MCP server executables accessible at paths specified in bridge_config.json","Proper file permissions for executing MCP server binaries","Sufficient system resources to spawn multiple child processes","Try-catch blocks around tool execution","Error message formatting logic"],"failure_modes":["Tool discovery happens at bridge initialization — adding new MCP servers requires restart","No caching of tool schemas — each bridge restart re-queries all MCP servers","Assumes MCP servers are stable and respond to tool list requests within reasonable timeout","No conflict resolution if multiple MCP servers expose tools with identical names","Process spawning adds ~50-200ms overhead per MCP server at initialization","No built-in process restart on failure — requires external orchestration for high availability","Stdio-based communication has no built-in backpressure handling for large tool responses","Windows compatibility depends on Node.js child_process behavior across platforms","Error messages from MCP servers may be opaque — difficult to debug root causes","No automatic retry logic — LLM must decide whether to retry","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.42505422267527504,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2025-04-20T07:26:36Z"},"community":{"stars":972,"forks":113,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-patruff-ollama-mcp-bridge","compare_url":"https://unfragile.ai/compare?artifact=mcp-patruff-ollama-mcp-bridge"}},"signature":"pHYgWngjKu7GBUUwd+XtJYuVHt9JNqRqvje5i68c7axU3L0vQzCzc74r/g94faCzUS40kUVfv/uYDAzByM1GDA==","signedAt":"2026-06-19T21:12:17.551Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-patruff-ollama-mcp-bridge","artifact":"https://unfragile.ai/mcp-patruff-ollama-mcp-bridge","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-patruff-ollama-mcp-bridge","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}