{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mcp-chatbot","slug":"mcp-chatbot","name":"MCP-Chatbot","type":"mcp","url":"https://github.com/3choff/mcp-chatbot","page_url":"https://unfragile.ai/mcp-chatbot","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-mcp-chatbot__cap_0","uri":"capability://tool.use.integration.dynamic.mcp.tool.discovery.and.registration","name":"dynamic-mcp-tool-discovery-and-registration","description":"Automatically discovers available tools from configured MCP servers via the stdio protocol, parses tool schemas, and registers them into the LLM's system prompt without manual tool definition. Uses the Server.list_tools() method to query each MCP server asynchronously, extracting tool metadata (name, description, input schema) and formatting it for LLM consumption via Tool.format_for_llm(). This enables zero-configuration tool integration where new tools become available immediately upon server startup.","intents":["I want to connect my LLM to external tools without manually defining each tool's schema","I need to support multiple MCP servers and have all their tools available in one chatbot","I want new tools to be discoverable automatically when I add a new MCP server"],"best_for":["developers building extensible AI agents with pluggable tool ecosystems","teams managing multiple MCP server instances and wanting unified tool access","rapid prototyping scenarios where tool definitions change frequently"],"limitations":["Tool discovery is synchronous at startup — adding new servers requires chatbot restart","No caching of tool schemas — each startup re-queries all servers, adding latency proportional to server count","Tool naming conflicts across servers are not automatically resolved; last-registered tool wins"],"requires":["Python 3.9+","MCP servers configured in servers_config.json with valid stdio command paths","Network/IPC connectivity to all configured MCP servers"],"input_types":["JSON configuration (servers_config.json)"],"output_types":["formatted tool schemas in system prompt","tool metadata objects"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-chatbot__cap_1","uri":"capability://tool.use.integration.openai.compatible.llm.provider.abstraction","name":"openai-compatible-llm-provider-abstraction","description":"Provides a unified LLMClient class that communicates with any LLM API following OpenAI's chat completion interface (configurable base URL, model name, API key). The client handles request formatting, response parsing, and error handling for tool-calling responses, allowing seamless swapping between OpenAI, Anthropic, Ollama, or any OpenAI-compatible endpoint without code changes. Configuration is loaded from environment variables, enabling provider switching via .env file updates.","intents":["I want to use my own LLM provider or self-hosted model instead of OpenAI","I need to switch between multiple LLM providers (OpenAI, Anthropic, local Ollama) without rewriting code","I want to run this chatbot with a cost-effective local LLM while maintaining the same interface"],"best_for":["developers wanting provider-agnostic LLM integration","organizations with on-premises LLM deployments or cost constraints","teams experimenting with multiple models and needing rapid provider switching"],"limitations":["Assumes OpenAI API format — providers with non-standard response schemas require custom LLMClient subclassing","No built-in retry logic for rate limiting or transient failures — relies on caller to implement backoff","Tool-calling response parsing assumes OpenAI's tool_calls format; non-standard formats require custom parsing"],"requires":["Python 3.9+","API key for chosen LLM provider (OpenAI, Anthropic, Ollama, etc.) in environment variables","LLM provider API endpoint URL (defaults to OpenAI if not configured)"],"input_types":["chat messages (user input, system prompt, tool results)","tool schemas"],"output_types":["LLM response text","tool call decisions (function name + arguments)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-chatbot__cap_10","uri":"capability://safety.moderation.environment.variable.based.credential.and.endpoint.configuration","name":"environment-variable-based-credential-and-endpoint-configuration","description":"Manages sensitive credentials (API keys, endpoints) via environment variables loaded from .env files, keeping secrets out of source code and configuration files. The Configuration class reads variables like OPENAI_API_KEY, LLM_BASE_URL, and provider-specific credentials from the environment, enabling secure credential injection without code changes. Supports .env file loading via python-dotenv or similar libraries.","intents":["I want to keep API keys and secrets out of my codebase and version control","I need to use different credentials for different deployment environments","I want to rotate credentials without redeploying the application"],"best_for":["production deployments where credential security is critical","teams using CI/CD pipelines with environment-specific secret injection","developers following security best practices for credential management"],"limitations":[".env files are still plain text — must be protected via file permissions and not committed to version control","No encryption of credentials at rest — relies on OS-level file permissions","Environment variable names are not validated — typos in variable names fail silently","No audit trail of credential access — difficult to track which processes accessed which secrets"],"requires":["Python 3.9+",".env file in the working directory or environment variables set via system/container configuration","python-dotenv library (optional, for .env file loading)"],"input_types":[".env file or environment variables"],"output_types":["resolved credential values"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-chatbot__cap_2","uri":"capability://automation.workflow.mcp.server.lifecycle.management.with.stdio.protocol","name":"mcp-server-lifecycle-management-with-stdio-protocol","description":"Manages the full lifecycle of MCP server connections using the stdio protocol: spawning server processes, initializing the MCP session, discovering tools, executing tool calls with built-in retry mechanisms, and gracefully shutting down resources. The Server class wraps subprocess management and async I/O to handle bidirectional communication with MCP servers, including error recovery and resource cleanup. Supports multiple concurrent server connections via asyncio, enabling parallel tool execution across servers.","intents":["I need to reliably connect to and manage multiple MCP servers in a single chatbot process","I want automatic retry logic when tool execution fails due to transient errors","I need to ensure MCP server processes are properly cleaned up when the chatbot exits"],"best_for":["developers building production chatbots that depend on external tool servers","teams running multiple MCP servers and needing coordinated lifecycle management","scenarios requiring high availability with automatic error recovery"],"limitations":["Stdio protocol only — does not support HTTP or other transport protocols for MCP servers","Retry logic is hardcoded (no configurable backoff strategy) — limited to fixed retry counts","Server process crashes are not automatically detected or restarted; requires external process manager (systemd, supervisor)","No health-check mechanism to proactively detect hung or unresponsive servers"],"requires":["Python 3.9+","MCP server executables accessible via PATH or absolute file paths in servers_config.json","Proper file permissions to execute server binaries"],"input_types":["server configuration (command, args)","tool call requests"],"output_types":["tool execution results","error messages with retry status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-chatbot__cap_3","uri":"capability://planning.reasoning.multi.turn.conversation.with.tool.loop.orchestration","name":"multi-turn-conversation-with-tool-loop-orchestration","description":"Orchestrates a full agentic loop: accepts user input, sends it with system prompt and tool schemas to the LLM, parses tool-calling decisions from the LLM response, executes requested tools via MCP servers, and feeds tool results back into the conversation context for the LLM to reason over. The ChatSession class manages conversation history and iteratively calls the LLM until it produces a final response (no more tool calls). This enables multi-step reasoning where the LLM can call tools, observe results, and make follow-up decisions.","intents":["I want my chatbot to use tools intelligently, calling them only when needed and reasoning about results","I need multi-turn conversations where the LLM can call multiple tools in sequence and adapt based on results","I want to build agents that can break down complex tasks into tool calls and synthesize final answers"],"best_for":["developers building AI agents with reasoning capabilities","teams needing multi-step task automation (e.g., search → analyze → summarize)","applications requiring dynamic tool selection based on user intent"],"limitations":["No built-in loop termination safeguards — infinite tool-calling loops possible if LLM repeatedly calls tools without converging","Tool execution errors do not automatically trigger fallback strategies; errors are passed to LLM for handling","Conversation history is stored in memory only — no persistence across chatbot restarts","No cost tracking or token counting — developers must implement their own usage monitoring"],"requires":["Python 3.9+","Configured LLMClient with a model supporting tool calling (e.g., GPT-4, Claude 3.5)","At least one MCP server with available tools"],"input_types":["user text input","conversation history"],"output_types":["final LLM response text","tool call trace (for debugging)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-chatbot__cap_4","uri":"capability://automation.workflow.json.based.server.configuration.with.environment.variable.injection","name":"json-based-server-configuration-with-environment-variable-injection","description":"Loads MCP server configurations from a JSON file (servers_config.json) that specifies server command, arguments, and environment variables. The Configuration class merges JSON-defined settings with environment variables (e.g., API keys from .env), enabling secure credential management and environment-specific server setup without hardcoding secrets. Supports variable substitution in server commands and arguments, allowing dynamic path resolution and credential injection at runtime.","intents":["I want to configure multiple MCP servers without editing Python code","I need to inject API keys and secrets into server configurations securely via environment variables","I want different server configurations for development, staging, and production environments"],"best_for":["teams managing multiple deployment environments with different server configurations","developers wanting to separate configuration from code for easier maintenance","security-conscious teams needing to avoid hardcoding credentials"],"limitations":["JSON schema validation is not enforced — malformed configurations fail at runtime with unclear error messages","No support for configuration inheritance or templating — each server config must be fully specified","Environment variable substitution is simple string replacement — no type coercion or validation","No built-in config reload — configuration changes require chatbot restart"],"requires":["Python 3.9+","servers_config.json file in the working directory or specified path",".env file with required environment variables (optional but recommended)"],"input_types":["JSON configuration file","environment variables"],"output_types":["parsed server configurations","resolved environment variables"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-chatbot__cap_5","uri":"capability://data.processing.analysis.tool.schema.formatting.for.llm.consumption","name":"tool-schema-formatting-for-llm-consumption","description":"Converts MCP tool metadata (name, description, input schema) into a structured format that LLMs can understand and reason about. The Tool.format_for_llm() method serializes tool schemas into a standardized text or JSON representation that is injected into the system prompt, enabling the LLM to recognize available tools and generate valid tool-calling requests. Handles schema validation and formatting to ensure LLM-compatible output.","intents":["I want the LLM to understand what tools are available and how to call them correctly","I need tool schemas formatted in a way that my LLM provider can parse and act on","I want to ensure tool descriptions are clear enough for the LLM to make intelligent tool-selection decisions"],"best_for":["developers integrating MCP tools with LLMs for the first time","teams needing to customize tool descriptions for better LLM understanding","scenarios where tool schema clarity directly impacts agent decision quality"],"limitations":["Schema formatting is fixed to a single format — no customization for different LLM providers' preferred formats","Complex nested schemas may not be clearly represented in the formatted output","No validation that formatted schemas are actually parseable by the target LLM","Tool descriptions are limited to what the MCP server provides — no augmentation or clarification"],"requires":["Python 3.9+","Tool metadata from MCP server discovery (name, description, input schema)"],"input_types":["tool metadata objects"],"output_types":["formatted tool schema strings","system prompt fragments"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-chatbot__cap_6","uri":"capability://automation.workflow.asynchronous.concurrent.tool.execution.across.servers","name":"asynchronous-concurrent-tool-execution-across-servers","description":"Executes tool calls concurrently across multiple MCP servers using Python's asyncio framework. When the LLM requests multiple tools, the system spawns async tasks for each tool execution, allowing parallel I/O and reducing total latency. The Server class uses async/await patterns for all I/O operations (server communication, tool execution), enabling efficient handling of multiple concurrent requests without blocking.","intents":["I want to execute multiple tools in parallel to reduce total response time","I need to call tools on different servers simultaneously without waiting for each to complete","I want to build responsive chatbots that don't block on slow tool servers"],"best_for":["applications with multiple tool servers where latency is critical","scenarios where tools have variable execution times and parallelization provides significant speedup","teams building high-throughput chatbot services"],"limitations":["Async execution adds complexity to error handling — tool failures in one task don't automatically cancel others","No built-in timeout mechanism for hanging tool calls — long-running tools can stall the entire conversation","Concurrent execution is limited by the number of available file descriptors and process limits","Debugging async code is harder than synchronous equivalents — stack traces are less intuitive"],"requires":["Python 3.9+","Understanding of asyncio patterns (async/await syntax)"],"input_types":["multiple tool call requests"],"output_types":["tool execution results (in execution order, not request order)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-chatbot__cap_7","uri":"capability://automation.workflow.tool.execution.with.built.in.retry.and.error.recovery","name":"tool-execution-with-built-in-retry-and-error-recovery","description":"Executes tool calls with automatic retry logic on failure, allowing transient errors (network timeouts, temporary server unavailability) to be recovered without user intervention. The Server.call_tool() method includes configurable retry counts and handles specific error types, re-attempting failed tool calls before surfacing errors to the LLM. Errors are formatted as tool results and fed back to the LLM for reasoning, enabling the agent to adapt to tool failures.","intents":["I want my chatbot to be resilient to temporary tool server failures","I need automatic retry logic for flaky network connections or overloaded servers","I want the LLM to be aware of tool failures and potentially try alternative approaches"],"best_for":["production deployments where tool server availability is not guaranteed","teams with unreliable network infrastructure or overloaded tool servers","applications requiring high resilience to transient failures"],"limitations":["Retry logic is hardcoded with fixed retry counts — no exponential backoff or jitter to avoid thundering herd","Only retries on specific error types — permanent failures (invalid tool calls, authentication errors) fail immediately","No circuit breaker pattern — repeated failures to a server don't trigger fallback mechanisms","Retry delays are not configurable — developers cannot tune retry behavior for their specific servers"],"requires":["Python 3.9+","MCP servers that are occasionally unavailable or slow (retry logic is only useful if failures are transient)"],"input_types":["tool call requests"],"output_types":["tool execution results or error messages after retry exhaustion"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-chatbot__cap_8","uri":"capability://text.generation.language.interactive.cli.chat.interface.with.streaming.responses","name":"interactive-cli-chat-interface-with-streaming-responses","description":"Provides a command-line interface for interactive multi-turn conversations with the chatbot. Users type messages at a prompt, and the system displays LLM responses and tool execution traces in real-time. The main() function orchestrates the chat loop, handling user input, calling the ChatSession, and formatting output for readability. Supports streaming responses (if the LLM provider supports it) to provide real-time feedback to users.","intents":["I want to test my MCP chatbot interactively without building a web interface","I need to see tool execution traces and debug what the LLM is doing","I want a simple CLI tool for rapid prototyping and experimentation"],"best_for":["developers prototyping and testing MCP integrations","teams building CLI-based tools and agents","debugging scenarios where visibility into tool execution is critical"],"limitations":["CLI interface is text-only — no rich formatting, colors, or interactive UI elements","No conversation persistence — chat history is lost when the process exits","No support for file uploads or complex input types — limited to text input","Streaming responses require LLM provider support — falls back to non-streaming if unavailable"],"requires":["Python 3.9+","Terminal/console environment with standard input/output"],"input_types":["user text input from stdin"],"output_types":["formatted text responses to stdout","tool execution traces"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-chatbot__cap_9","uri":"capability://text.generation.language.system.prompt.injection.with.tool.schema.embedding","name":"system-prompt-injection-with-tool-schema-embedding","description":"Constructs a system prompt that includes tool schemas and usage instructions, injected into every LLM request to guide the model's behavior. The system prompt is built dynamically from discovered tools, formatted via Tool.format_for_llm(), and combined with static instructions about how to call tools and when to use them. This enables the LLM to understand its capabilities without requiring fine-tuning or external knowledge.","intents":["I want to tell the LLM about available tools without modifying the model itself","I need the LLM to understand when and how to call tools based on user requests","I want to customize the LLM's behavior and tool-calling strategy via prompting"],"best_for":["developers using off-the-shelf LLMs that don't have native tool-calling support","teams wanting to customize agent behavior through prompt engineering","scenarios where tool availability changes frequently and needs to be reflected in prompts"],"limitations":["System prompt size grows with the number of tools — very large tool sets may exceed context window limits","Tool schema clarity depends on LLM's ability to parse and understand the formatted schemas","No feedback loop to optimize prompts based on tool-calling success rates","Prompt injection attacks are possible if user input is not sanitized before being added to the conversation"],"requires":["Python 3.9+","LLM with sufficient context window to accommodate system prompt + tools + conversation history"],"input_types":["tool schemas","static prompt instructions"],"output_types":["complete system prompt string"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","MCP servers configured in servers_config.json with valid stdio command paths","Network/IPC connectivity to all configured MCP servers","API key for chosen LLM provider (OpenAI, Anthropic, Ollama, etc.) in environment variables","LLM provider API endpoint URL (defaults to OpenAI if not configured)",".env file in the working directory or environment variables set via system/container configuration","python-dotenv library (optional, for .env file loading)","MCP server executables accessible via PATH or absolute file paths in servers_config.json","Proper file permissions to execute server binaries","Configured LLMClient with a model supporting tool calling (e.g., GPT-4, Claude 3.5)"],"failure_modes":["Tool discovery is synchronous at startup — adding new servers requires chatbot restart","No caching of tool schemas — each startup re-queries all servers, adding latency proportional to server count","Tool naming conflicts across servers are not automatically resolved; last-registered tool wins","Assumes OpenAI API format — providers with non-standard response schemas require custom LLMClient subclassing","No built-in retry logic for rate limiting or transient failures — relies on caller to implement backoff","Tool-calling response parsing assumes OpenAI's tool_calls format; non-standard formats require custom parsing",".env files are still plain text — must be protected via file permissions and not committed to version control","No encryption of credentials at rest — relies on OS-level file permissions","Environment variable names are not validated — typos in variable names fail silently","No audit trail of credential access — difficult to track which processes accessed which secrets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-chatbot","compare_url":"https://unfragile.ai/compare?artifact=mcp-chatbot"}},"signature":"cwM/Gll1+e3s9FxTlbeaeRWs1Z3+TXXr38WBGeb+lQUbvz9DBjVrSxKI8u/CArXLfSpk8ZFuQiA19J9sY3SDAA==","signedAt":"2026-06-19T17:50:16.734Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-chatbot","artifact":"https://unfragile.ai/mcp-chatbot","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-chatbot","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"}}