{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_82deutschmark-chlorpromazine-mcp","slug":"82deutschmark-chlorpromazine-mcp","name":"Buzz Killington","type":"mcp","url":"https://82deutschmark.github.io/chlorpromazine-mcp/","page_url":"https://unfragile.ai/82deutschmark-chlorpromazine-mcp","categories":["mcp-servers","testing-quality"],"tags":["mcp","model-context-protocol","smithery:82deutschmark/chlorpromazine-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_82deutschmark-chlorpromazine-mcp__cap_0","uri":"capability://search.retrieval.mcp.integrated.documentation.search.with.semantic.indexing","name":"mcp-integrated documentation search with semantic indexing","description":"Provides semantic search across developer documentation through the Model Context Protocol, enabling LLM agents to retrieve fact-checked answers from trusted sources without hallucination. Implements a schema-based tool registry that exposes documentation queries as callable functions within the MCP protocol, allowing agents to invoke searches during reasoning chains and receive structured results with source attribution.","intents":["I want my LLM agent to search official documentation without making up answers","I need to ground code generation in verified API documentation and best practices","I want to reduce hallucinations by giving my agent access to trusted reference materials during coding tasks"],"best_for":["LLM agent developers building coding assistants","Teams implementing agentic workflows that require factual accuracy","Developers integrating Claude or other MCP-compatible models into IDE workflows"],"limitations":["Search quality depends on documentation indexing strategy — no details provided on indexing mechanism or update frequency","MCP protocol overhead adds latency per search invocation compared to direct API calls","Limited to documentation sources pre-indexed by the server — cannot dynamically add new documentation sources at runtime","No built-in caching layer specified, so repeated queries may incur redundant search costs"],"requires":["MCP-compatible client (Claude Desktop, or custom MCP client implementation)","Network connectivity to Buzz Killington MCP server","Understanding of MCP protocol and tool schema definition"],"input_types":["text query strings","structured search parameters (e.g., language, framework, topic filters)"],"output_types":["structured JSON with documentation snippets","source attribution and URLs","relevance scores or ranking metadata"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_82deutschmark-chlorpromazine-mcp__cap_1","uri":"capability://text.generation.language.structured.prompt.templates.for.code.generation.workflows","name":"structured prompt templates for code generation workflows","description":"Provides pre-built, fact-checked prompt templates optimized for code generation tasks, delivered through MCP as callable tools. Templates encode best practices, error patterns, and domain-specific guidance to improve LLM output quality without requiring manual prompt engineering. Agents invoke these templates as structured tools, passing context variables (language, framework, problem description) to generate contextually-appropriate prompts.","intents":["I want to use battle-tested prompts for code generation without writing them from scratch","I need my agent to automatically select the right prompt template based on the coding task","I want to ensure consistent, high-quality prompts across multiple coding sessions and team members"],"best_for":["Solo developers building LLM-assisted coding tools","Teams standardizing on prompt patterns for code generation","Agentic systems that need to dynamically select prompts based on task type"],"limitations":["Template library scope unknown — unclear which languages, frameworks, and problem domains are covered","No versioning or update mechanism described — templates may become stale as language/framework best practices evolve","Templates are static — no dynamic adaptation based on codebase-specific patterns or team conventions","Requires MCP client to invoke — cannot be used directly in non-MCP LLM workflows"],"requires":["MCP-compatible LLM client","Buzz Killington MCP server running and accessible","Understanding of template variable substitution and context passing"],"input_types":["task type identifier (e.g., 'refactor', 'debug', 'test-generation')","programming language and framework","problem description or code snippet","context variables (e.g., codebase style, constraints)"],"output_types":["formatted prompt string","structured prompt with role/system/user message separation","metadata about template (version, applicable languages, known limitations)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_82deutschmark-chlorpromazine-mcp__cap_2","uri":"capability://safety.moderation.fact.checking.and.source.attribution.for.code.related.queries","name":"fact-checking and source attribution for code-related queries","description":"Validates code generation outputs and developer queries against trusted documentation sources, returning confidence scores and source citations. Implements a verification pipeline that cross-references generated code snippets, API usage patterns, and best practices against indexed documentation, surfacing potential inaccuracies or deprecated patterns. Results include source URLs and documentation excerpts to support human review.","intents":["I want to verify that generated code follows current API specifications before using it","I need to know which documentation sources support a particular coding pattern or recommendation","I want to catch deprecated or incorrect patterns in AI-generated code before they reach production"],"best_for":["Teams requiring high code quality and compliance with documented standards","Developers working with rapidly-evolving APIs or frameworks where deprecation is common","Regulated environments where code provenance and correctness must be auditable"],"limitations":["Fact-checking accuracy depends on documentation coverage — gaps in indexed sources will result in false negatives","No details on confidence scoring methodology — unclear how certainty is calculated or what threshold indicates reliable verification","Verification latency unknown — fact-checking may add significant delay to code generation workflows","Limited to documented patterns — novel or cutting-edge approaches may not be verifiable against existing documentation"],"requires":["MCP-compatible client","Buzz Killington server with indexed documentation sources","Code snippet or query to verify"],"input_types":["code snippets","API usage patterns","best practice descriptions","natural language queries about code correctness"],"output_types":["verification result (verified, unverified, contradicted)","confidence score","source citations with URLs","relevant documentation excerpts","explanation of verification reasoning"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_82deutschmark-chlorpromazine-mcp__cap_3","uri":"capability://planning.reasoning.deep.problem.analysis.with.documentation.grounded.reasoning","name":"deep problem analysis with documentation-grounded reasoning","description":"Analyzes coding problems by decomposing them into sub-problems and retrieving relevant documentation for each component, enabling agents to reason through complex issues with fact-checked context. Implements a multi-step analysis pipeline that identifies problem categories, retrieves applicable documentation, and synthesizes solutions grounded in trusted sources. Results include problem decomposition, relevant documentation sections, and reasoning traces.","intents":["I want my agent to break down complex coding problems and find relevant documentation for each part","I need to understand why a particular approach is recommended by seeing the documentation that supports it","I want to solve problems systematically by grounding reasoning in verified sources rather than LLM intuition"],"best_for":["Developers debugging complex issues across multiple systems or frameworks","Teams building agentic debugging assistants that need to explain their reasoning","Educational contexts where understanding the 'why' behind solutions is important"],"limitations":["Problem decomposition strategy not specified — unclear how problems are categorized or split into sub-problems","Documentation retrieval may miss relevant sources if problem framing doesn't match indexed documentation structure","Analysis depth depends on documentation completeness — sparse documentation for a problem domain will limit reasoning quality","No feedback mechanism described — cannot improve analysis based on user validation of results"],"requires":["MCP-compatible client","Buzz Killington server with comprehensive documentation indexing","Problem description or error message to analyze"],"input_types":["error messages or stack traces","problem descriptions in natural language","code snippets demonstrating the issue","context about frameworks, libraries, or environments involved"],"output_types":["problem decomposition (identified sub-problems)","relevant documentation sections for each sub-problem","reasoning trace showing how documentation informed analysis","recommended solution approaches with source citations","confidence assessment for each recommendation"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_82deutschmark-chlorpromazine-mcp__cap_4","uri":"capability://search.retrieval.multi.language.and.framework.specific.documentation.routing","name":"multi-language and framework-specific documentation routing","description":"Routes documentation queries to language and framework-specific indices, ensuring agents retrieve documentation relevant to their current development context. Implements context-aware routing that identifies the programming language, framework, and domain from query context or explicit parameters, then queries the appropriate documentation subset. Supports polyglot development workflows where agents work across multiple languages and frameworks.","intents":["I want my agent to automatically search Python documentation when working on Python code and JavaScript docs when switching to Node.js","I need to ensure documentation searches return results for the specific framework version I'm using","I want to avoid irrelevant documentation results from other languages or frameworks cluttering search results"],"best_for":["Polyglot development teams working across multiple languages and frameworks","Developers switching between projects with different tech stacks","Agentic systems that need to maintain context across multiple programming languages"],"limitations":["Supported languages and frameworks not specified — unclear which combinations are available","Context detection mechanism unknown — unclear how language/framework is inferred from queries or if it requires explicit specification","Version-specific routing not detailed — may not support multiple versions of the same framework simultaneously","Documentation coverage varies by language/framework — some combinations may have sparse or outdated indices"],"requires":["MCP-compatible client","Buzz Killington server with multi-language documentation indices","Language and framework context (explicit or inferred from code)"],"input_types":["documentation queries with language/framework context","code snippets (for language detection)","explicit language and framework identifiers","version specifications"],"output_types":["language/framework-specific documentation results","routing metadata (which index was queried)","version information for returned documentation","cross-reference links to related documentation in other languages/frameworks"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_82deutschmark-chlorpromazine-mcp__cap_5","uri":"capability://tool.use.integration.agentic.tool.composition.for.multi.step.coding.workflows","name":"agentic tool composition for multi-step coding workflows","description":"Enables agents to compose multiple MCP tools (documentation search, fact-checking, prompt templates, problem analysis) into coordinated workflows for complex coding tasks. Implements tool chaining through MCP's function-calling interface, allowing agents to invoke tools sequentially or in parallel, pass results between tools, and maintain state across steps. Supports conditional branching based on tool results and error handling for failed tool invocations.","intents":["I want my agent to search documentation, fact-check the results, and generate code using templates in a single workflow","I need my agent to analyze a problem, retrieve relevant docs, and then verify the proposed solution","I want to build complex coding workflows that combine multiple tools without writing custom orchestration code"],"best_for":["Developers building sophisticated agentic coding assistants","Teams implementing multi-step code generation and verification pipelines","Systems requiring complex reasoning that spans multiple documentation and code generation steps"],"limitations":["Tool composition semantics not detailed — unclear how tool outputs are passed to subsequent tools or how state is managed","Error handling and retry logic not specified — unclear how failures in one tool affect downstream steps","Latency accumulates across tool invocations — no optimization for parallel tool execution described","Requires MCP client with tool composition support — not all MCP clients may support complex chaining"],"requires":["MCP-compatible client with tool composition/chaining support","Buzz Killington MCP server with multiple tools exposed","Understanding of MCP tool calling semantics and result passing"],"input_types":["initial task description or code snippet","tool invocation sequences (implicit or explicit)","parameters for each tool in the chain"],"output_types":["final result from the composed workflow","intermediate results from each tool step","execution trace showing tool invocation order and results","error logs if any tool fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"moderate","permissions":["MCP-compatible client (Claude Desktop, or custom MCP client implementation)","Network connectivity to Buzz Killington MCP server","Understanding of MCP protocol and tool schema definition","MCP-compatible LLM client","Buzz Killington MCP server running and accessible","Understanding of template variable substitution and context passing","MCP-compatible client","Buzz Killington server with indexed documentation sources","Code snippet or query to verify","Buzz Killington server with comprehensive documentation indexing"],"failure_modes":["Search quality depends on documentation indexing strategy — no details provided on indexing mechanism or update frequency","MCP protocol overhead adds latency per search invocation compared to direct API calls","Limited to documentation sources pre-indexed by the server — cannot dynamically add new documentation sources at runtime","No built-in caching layer specified, so repeated queries may incur redundant search costs","Template library scope unknown — unclear which languages, frameworks, and problem domains are covered","No versioning or update mechanism described — templates may become stale as language/framework best practices evolve","Templates are static — no dynamic adaptation based on codebase-specific patterns or team conventions","Requires MCP client to invoke — cannot be used directly in non-MCP LLM workflows","Fact-checking accuracy depends on documentation coverage — gaps in indexed sources will result in false negatives","No details on confidence scoring methodology — unclear how certainty is calculated or what threshold indicates reliable verification","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.49000000000000005,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:19:34.639Z","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=82deutschmark-chlorpromazine-mcp","compare_url":"https://unfragile.ai/compare?artifact=82deutschmark-chlorpromazine-mcp"}},"signature":"Y2STXEK+lqc6eoIXrh3ACuAwpGxgcCQVQGiiBj91tdvto4XFrCiwNFJZlEXBmpJNZ+Jo+vdKE7ZjC3XIcFitCw==","signedAt":"2026-06-21T07:25:28.992Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/82deutschmark-chlorpromazine-mcp","artifact":"https://unfragile.ai/82deutschmark-chlorpromazine-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=82deutschmark-chlorpromazine-mcp","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"}}