{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-chucknorris","slug":"chucknorris","name":"ChuckNorris","type":"mcp","url":"https://github.com/pollinations/chucknorris-mcp","page_url":"https://unfragile.ai/chucknorris","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-chucknorris__cap_0","uri":"capability://tool.use.integration.enum.based.llm.specific.prompt.injection","name":"enum-based llm-specific prompt injection","description":"Dynamically selects and delivers jailbreak/enhancement prompts tailored to specific LLM models (OpenAI, Anthropic, Meta, etc.) using an enumerated model registry. The MCP server maintains a mapping of model identifiers to prompt variants, allowing clients to request prompts optimized for a target LLM's instruction-following patterns and vulnerabilities without hardcoding model-specific logic on the client side.","intents":["I need a jailbreak prompt specifically tuned for Claude's constitution-based alignment","I want to test how different LLMs respond to the same adversarial prompt variant","I need to dynamically select enhancement prompts based on which LLM my agent is calling","I want to maintain a centralized registry of prompt variants without duplicating them across multiple agents"],"best_for":["security researchers testing LLM robustness across model families","LLM application developers building multi-model orchestration systems","teams evaluating prompt injection vulnerabilities in production systems","researchers studying instruction-following behavior differences between LLM architectures"],"limitations":["Enum-based approach requires server-side updates to add new model variants — no runtime model discovery","Prompt effectiveness degrades as LLMs are fine-tuned or updated; enum mappings become stale without maintenance","No built-in versioning for prompt variants — difficult to track which prompt version was used in a given test","Limited to predefined model enum values; custom or private LLMs require server modification"],"requires":["MCP client implementation (Python, Node.js, or compatible runtime)","Network access to MCP server endpoint","Knowledge of target LLM model identifier to query correct enum variant"],"input_types":["model identifier string (enum value)","optional context parameters for prompt customization"],"output_types":["text (jailbreak/enhancement prompt string)","structured metadata (prompt version, model target, effectiveness notes)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chucknorris__cap_1","uri":"capability://tool.use.integration.dynamic.schema.adaptation.for.prompt.variants","name":"dynamic schema adaptation for prompt variants","description":"Implements a schema-based system that adapts the MCP tool schema based on available prompt variants and model enums, allowing the server to expose only valid prompt combinations and prevent invalid requests at the schema level. This pattern uses JSON Schema or similar constraint definitions to define which prompt types are available for which models, enforcing correctness through type validation rather than runtime error handling.","intents":["I want my MCP client to only see valid prompt options for the models I support","I need to add a new LLM model and automatically expose its prompt variants without changing client code","I want schema validation to catch invalid prompt-model combinations before sending requests","I need to document which prompts work with which models in a machine-readable format"],"best_for":["MCP server developers building extensible tool registries","teams implementing strict schema-driven API contracts","applications requiring compile-time or schema-validation-time safety checks","multi-tenant systems where different users have access to different prompt variants"],"limitations":["Schema changes require server restart or hot-reload mechanism — not suitable for real-time prompt injection without infrastructure support","Schema complexity grows linearly with number of model variants; managing hundreds of models becomes unwieldy","Clients must understand and respect schema constraints; no fallback for clients that ignore schema validation","Schema-based validation adds ~50-100ms overhead per request for complex constraint checking"],"requires":["MCP server implementation with schema generation capability","JSON Schema or equivalent constraint definition language","MCP client that respects and validates against provided schemas"],"input_types":["schema definition (JSON Schema format)","model enum values","prompt variant metadata"],"output_types":["adapted MCP tool schema","validated request/response contracts","schema documentation"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chucknorris__cap_2","uri":"capability://memory.knowledge.centralized.jailbreak.prompt.registry.with.versioning","name":"centralized jailbreak prompt registry with versioning","description":"Maintains a server-side registry of jailbreak and enhancement prompts organized by model family and version, allowing clients to query and retrieve prompts without embedding them in application code. The registry pattern enables atomic updates to all prompt variants, audit trails for prompt changes, and A/B testing of different prompt versions against the same model.","intents":["I want to update a jailbreak prompt and have all my agents immediately use the new version","I need to track which prompt version was used in a security test for compliance/audit purposes","I want to A/B test two different jailbreak approaches against the same model","I need to roll back a prompt variant that was causing unexpected behavior"],"best_for":["security teams running coordinated adversarial testing campaigns","research groups studying prompt injection effectiveness over time","production systems requiring audit trails for all prompt modifications","organizations managing multiple agents that need synchronized prompt updates"],"limitations":["No built-in persistence layer — registry state is in-memory unless explicitly persisted to database","Versioning requires manual version number management; no automatic semantic versioning","Registry queries have no built-in caching — high-frequency requests may cause performance degradation","No access control mechanism — all clients can access all prompts unless external auth layer is added"],"requires":["MCP server with persistent storage backend (database, file system, or equivalent)","Version control mechanism (manual or automated)","Client capability to query registry by model and version"],"input_types":["model identifier","version number or 'latest' tag","optional metadata filters (effectiveness, date range, author)"],"output_types":["prompt text","version metadata (creation date, author, effectiveness metrics)","list of available versions for a model"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chucknorris__cap_3","uri":"capability://tool.use.integration.mcp.protocol.gateway.for.prompt.delivery","name":"mcp protocol gateway for prompt delivery","description":"Implements an MCP server that exposes prompt retrieval as callable tools, allowing any MCP-compatible client (LLM agents, orchestration frameworks, testing tools) to request prompts via the Model Context Protocol. The gateway translates prompt queries into MCP tool calls with structured arguments, enabling seamless integration with MCP-based agent architectures without custom HTTP endpoints or SDK dependencies.","intents":["I want my LLM agent to request jailbreak prompts dynamically without hardcoding them","I need to integrate prompt delivery into an existing MCP-based orchestration system","I want to use standard MCP tooling to manage and version prompts across my infrastructure","I need to expose prompt variants to multiple different client types (Python agents, Node.js tools, etc.) through a single protocol"],"best_for":["teams building MCP-native agent architectures","organizations standardizing on MCP for tool integration","multi-language environments where a protocol-based approach is simpler than SDK management","research projects studying LLM behavior through standardized tool interfaces"],"limitations":["MCP protocol overhead adds ~100-200ms latency per request compared to direct function calls","Requires MCP client implementation — not suitable for environments without MCP support","Tool schema complexity grows with number of prompt variants; large registries may exceed MCP message size limits","No built-in request batching — multiple prompt queries require multiple round-trips"],"requires":["MCP server runtime (Python, Node.js, or compatible)","MCP client implementation in target language","Network connectivity between client and server"],"input_types":["MCP tool call with model identifier argument","optional version/variant parameters"],"output_types":["MCP tool result containing prompt text","structured metadata in result object"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chucknorris__cap_4","uri":"capability://data.processing.analysis.model.family.aware.prompt.selection","name":"model-family-aware prompt selection","description":"Implements logic to categorize LLM models into families (OpenAI GPT, Anthropic Claude, Meta Llama, etc.) and select appropriate prompt variants based on family characteristics rather than exact model version. This abstraction allows prompts to remain effective across minor model updates within a family and reduces the number of distinct prompt variants that must be maintained.","intents":["I want a single jailbreak prompt that works across GPT-4, GPT-4 Turbo, and GPT-4o without maintaining separate variants","I need to test a prompt against an entire model family to understand its robustness","I want to automatically select the best prompt for a model I haven't explicitly configured","I need to handle new model releases without immediately updating my prompt registry"],"best_for":["teams managing prompts for rapidly-evolving model landscapes","research studying prompt effectiveness across model families","production systems that need to support new model releases without immediate reconfiguration","organizations with limited resources for maintaining per-model prompt variants"],"limitations":["Family-level abstraction may miss model-specific vulnerabilities or instruction-following differences","Requires manual curation of model-to-family mappings; new model families require explicit configuration","Prompt effectiveness may degrade when applied to edge-case models within a family","No automatic detection of when a model has diverged enough from its family to require a new variant"],"requires":["Model family taxonomy definition (hardcoded or externally sourced)","Mapping of model identifiers to family categories","Prompt variants defined at family level rather than per-model"],"input_types":["model identifier (exact version or family name)","optional fallback behavior specification"],"output_types":["model family classification","selected prompt variant for family","confidence score for selection"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (Python, Node.js, or compatible runtime)","Network access to MCP server endpoint","Knowledge of target LLM model identifier to query correct enum variant","MCP server implementation with schema generation capability","JSON Schema or equivalent constraint definition language","MCP client that respects and validates against provided schemas","MCP server with persistent storage backend (database, file system, or equivalent)","Version control mechanism (manual or automated)","Client capability to query registry by model and version","MCP server runtime (Python, Node.js, or compatible)"],"failure_modes":["Enum-based approach requires server-side updates to add new model variants — no runtime model discovery","Prompt effectiveness degrades as LLMs are fine-tuned or updated; enum mappings become stale without maintenance","No built-in versioning for prompt variants — difficult to track which prompt version was used in a given test","Limited to predefined model enum values; custom or private LLMs require server modification","Schema changes require server restart or hot-reload mechanism — not suitable for real-time prompt injection without infrastructure support","Schema complexity grows linearly with number of model variants; managing hundreds of models becomes unwieldy","Clients must understand and respect schema constraints; no fallback for clients that ignore schema validation","Schema-based validation adds ~50-100ms overhead per request for complex constraint checking","No built-in persistence layer — registry state is in-memory unless explicitly persisted to database","Versioning requires manual version number management; no automatic semantic versioning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:19.837Z","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=chucknorris","compare_url":"https://unfragile.ai/compare?artifact=chucknorris"}},"signature":"uo+JITs0cI/DDsBClGHQOe9Hj10C2xdyv7i7xvLMl1YIAwTqpEALtk3F/opgnGuDOFufyUgI86qdyFvSRi/NAw==","signedAt":"2026-06-15T19:38:50.622Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chucknorris","artifact":"https://unfragile.ai/chucknorris","verify":"https://unfragile.ai/api/v1/verify?slug=chucknorris","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"}}