{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-assafelovic-gpt-researcher","slug":"mcp-assafelovic-gpt-researcher","name":"gpt-researcher","type":"agent","url":"https://github.com/assafelovic/gpt-researcher","page_url":"https://unfragile.ai/mcp-assafelovic-gpt-researcher","categories":["research-search"],"tags":["agent","ai","automation","deepresearch","llms","mcp","mcp-server","python","research","search","webscraping"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-assafelovic-gpt-researcher__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.orchestration.with.three.tier.strategy","name":"multi-provider llm orchestration with three-tier strategy","description":"Routes research tasks across 25+ LLM providers (OpenAI, Anthropic, Ollama, local models, etc.) using a three-tier fallback strategy: primary model for planning, secondary for execution, tertiary for fallback. Implements provider-agnostic abstraction layer that normalizes API differences, handles rate limiting, and manages context windows per model. Supports both cloud and local model deployment without code changes.","intents":["I want to run research agents without vendor lock-in to a single LLM provider","I need to switch between OpenAI, Claude, and local Ollama models based on cost or latency","I want to use cheaper models for execution while keeping expensive models for planning decisions"],"best_for":["teams building cost-optimized research pipelines","enterprises with multi-cloud LLM strategies","developers prototyping with local models before production deployment"],"limitations":["Three-tier strategy adds ~500ms latency overhead for fallback evaluation","Model-specific prompt tuning required for optimal results across different providers","Context window mismatches between providers may cause truncation without explicit handling"],"requires":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, etc.) OR local Ollama instance","Network access to provider APIs or local model server"],"input_types":["research query (text)","configuration specifying primary/secondary/tertiary models"],"output_types":["structured research report (markdown/JSON)","provider metadata (model used, tokens consumed)"],"categories":["tool-use-integration","llm-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_1","uri":"capability://planning.reasoning.query.decomposition.and.parallel.sub.query.execution","name":"query decomposition and parallel sub-query execution","description":"Automatically breaks down complex research queries into 5-10 focused sub-queries using the planner agent, then executes them in parallel across multiple concurrent tasks. Each sub-query is independently researched with its own context retrieval and source validation, then results are merged and deduplicated. Uses tree-based query planning to identify dependencies and optimize execution order.","intents":["I want to research a complex topic like 'AI regulation across EU, US, and China' without manually splitting it","I need faster research by parallelizing independent sub-queries instead of sequential processing","I want comprehensive coverage by ensuring all aspects of a topic are researched independently"],"best_for":["researchers tackling multi-faceted topics with 5+ distinct angles","teams needing research completed in minutes rather than hours","applications requiring exhaustive coverage of complex domains"],"limitations":["Decomposition quality depends on planner LLM capability; weak planners may miss important angles","Parallel execution increases total API calls by 5-10x vs sequential approach","Result merging requires deduplication logic that may miss subtle variations in sources"],"requires":["Python 3.9+","LLM provider with strong reasoning capability (GPT-4, Claude 3.5+)","Concurrent execution support (asyncio, thread pool)"],"input_types":["research query (text)","optional: max_sub_queries parameter (default 5-10)"],"output_types":["array of sub-query results","merged research report with source deduplication","execution timeline showing parallelization"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_10","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.implementation","name":"mcp (model context protocol) server implementation","description":"Exposes GPT Researcher as an MCP server, allowing Claude and other MCP-compatible clients to invoke research capabilities as tools. Implements MCP protocol with resource and tool definitions for research queries, configuration, and report retrieval. Clients can call research as a native tool within their workflows. Supports streaming responses for long-running research. Enables integration with Claude projects and other MCP-aware applications without custom API wrappers.","intents":["I want to use GPT Researcher as a tool within Claude or other MCP clients","I need to invoke research from within an AI agent workflow without custom integrations","I want research capabilities available as a native MCP resource in my AI application"],"best_for":["teams using Claude or other MCP-compatible AI clients","applications where research is one tool among many in an AI agent workflow","developers building AI applications that need research capabilities without custom API code"],"limitations":["MCP protocol overhead adds ~100-200ms per request","Streaming responses may not work reliably with all MCP clients","MCP server requires separate process; adds deployment complexity","Limited to MCP-compatible clients; not usable with standard REST API clients"],"requires":["Python 3.9+","MCP-compatible client (Claude, etc.)","MCP server implementation (included in GPT Researcher)"],"input_types":["MCP tool call with research query and parameters","MCP resource request for configuration or report retrieval"],"output_types":["MCP tool response with research results","MCP resource content (configuration, report)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_11","uri":"capability://automation.workflow.configuration.management.with.environment.variable.and.file.based.setup","name":"configuration management with environment variable and file-based setup","description":"Provides flexible configuration system supporting environment variables, YAML/JSON config files, and programmatic Config class. Centralizes all settings: LLM providers, retrievers, report modes, domain filters, vector stores, etc. Implements configuration validation and defaults. Supports per-environment configurations (dev, staging, production) via config file selection. Environment variables override file-based configs. Enables easy switching between configurations without code changes.","intents":["I want to configure GPT Researcher for different environments (dev, prod) without code changes","I need to switch between LLM providers and retrievers via configuration","I want to manage sensitive credentials (API keys) via environment variables"],"best_for":["teams deploying GPT Researcher across multiple environments","applications requiring flexible configuration without code changes","organizations with strict credential management policies"],"limitations":["Configuration validation is basic; complex validation rules require custom code","Environment variable naming conventions can be confusing with many settings","File-based configs require careful YAML/JSON syntax; errors may not be caught until runtime","No built-in config versioning or rollback mechanism"],"requires":["Python 3.9+","Optional: YAML/JSON config files","Optional: environment variable setup in deployment environment"],"input_types":["environment variables (e.g., OPENAI_API_KEY)","config file (YAML or JSON)","programmatic Config class instantiation"],"output_types":["Config object with validated settings","configuration metadata (source, overrides)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_12","uri":"capability://safety.moderation.domain.filtering.and.source.validation.with.customizable.rules","name":"domain filtering and source validation with customizable rules","description":"Implements domain-based filtering allowing researchers to include/exclude specific domains from research. Supports whitelist mode (only specified domains) and blacklist mode (exclude specified domains). Validates sources against domain rules before inclusion in reports. Provides built-in domain categories (academic, news, government, etc.) for quick filtering. Enables custom domain rules per research query. Includes domain credibility scoring based on historical performance.","intents":["I want to research only from academic sources for a scientific topic","I need to exclude unreliable news sources or social media from research","I want to restrict research to government or official sources for policy research"],"best_for":["research teams with strict source requirements (academic, government, etc.)","applications where source credibility is critical","workflows requiring transparent source filtering for compliance"],"limitations":["Domain-only filtering may miss credible sources from unexpected domains","Whitelist mode may be too restrictive and miss important sources","Domain categories are predefined; custom categories require configuration","Credibility scoring is heuristic-based and may not reflect actual source quality"],"requires":["Python 3.9+","Optional: custom domain configuration"],"input_types":["array of Document objects with domain information","domain_filter configuration (whitelist/blacklist/categories)","optional: custom domain rules"],"output_types":["filtered Document array","filtering metadata (domains included/excluded, rules applied)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_13","uri":"capability://text.generation.language.research.report.export.in.multiple.formats.markdown.pdf.json","name":"research report export in multiple formats (markdown, pdf, json)","description":"Exports completed research reports in multiple formats: markdown (with inline citations), PDF (formatted with images and styling), and JSON (structured data with metadata). Markdown export preserves source links and citations. PDF export includes table of contents, page numbers, and embedded images. JSON export provides structured access to report sections, sources, and metadata. Supports custom export templates for branded PDF output. Implements format-specific optimizations (e.g., markdown for version control, PDF for sharing).","intents":["I want to export research as markdown for version control and collaboration","I need to generate PDF reports for client delivery with professional formatting","I want structured JSON export for programmatic processing or database storage"],"best_for":["teams collaborating on research with version control requirements","applications delivering research to external audiences (clients, publications)","workflows requiring structured data export for downstream processing"],"limitations":["PDF generation requires external library (weasyprint, reportlab); adds dependency","Custom PDF templates require HTML/CSS knowledge; complex styling may fail","Large reports with many images may produce large PDF files","JSON export may not preserve all formatting from original report"],"requires":["Python 3.9+","Optional: weasyprint or reportlab for PDF generation","Optional: custom PDF templates (HTML/CSS)"],"input_types":["research report object","export_format: 'markdown' | 'pdf' | 'json'","optional: pdf_template for custom styling"],"output_types":["markdown string with citations","PDF bytes (file)","JSON object with structured report data"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_14","uri":"capability://memory.knowledge.research.history.and.session.management.with.state.persistence","name":"research history and session management with state persistence","description":"Maintains research history across sessions, storing completed research queries, reports, and metadata. Implements session management with unique session IDs for tracking research progress. Supports state persistence to database or file system. Enables users to retrieve previous research, compare reports, and build on prior work. Implements automatic cleanup of old sessions. Provides search and filtering across research history. Supports export of research history for audit trails.","intents":["I want to retrieve previous research without re-running the same query","I need to compare research reports from different time periods","I want to maintain an audit trail of research conducted for compliance"],"best_for":["teams conducting ongoing research with knowledge accumulation","applications requiring audit trails for compliance","workflows where research history informs future research"],"limitations":["State persistence requires database or file system; adds operational complexity","Large research histories may impact query performance","Session cleanup requires careful configuration to avoid data loss","No built-in deduplication; similar research may be stored multiple times"],"requires":["Python 3.9+","Optional: database (SQLite, PostgreSQL) or file system for persistence"],"input_types":["research query (text)","optional: session_id for retrieving previous research"],"output_types":["research report","session metadata (timestamp, query, report type)","research history (array of previous sessions)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_2","uri":"capability://text.generation.language.multi.mode.research.report.generation.standard.detailed.deep","name":"multi-mode research report generation (standard, detailed, deep)","description":"Generates research reports in three configurable modes: Standard (quick overview with 3-5 sources), Detailed (comprehensive analysis with 10-15 sources and citations), and Deep (exhaustive research with 20+ sources, fact-checking, and multi-agent review). Each mode uses different prompt templates, source count targets, and validation strategies. Deep mode triggers multi-agent workflow with ChiefEditorAgent orchestrating specialized agents for research, review, and revision.","intents":["I want a quick 2-minute research summary for a meeting","I need a comprehensive report with full citations for a whitepaper or decision document","I require exhaustive, fact-checked research with multiple perspectives for critical decisions"],"best_for":["teams with varying research depth requirements across different use cases","organizations balancing speed vs accuracy (quick summaries for internal, deep reports for external)","research-heavy workflows where report quality directly impacts downstream decisions"],"limitations":["Deep mode can take 10-30 minutes and consume 50+ API calls; not suitable for real-time applications","Standard mode may miss important nuances due to limited source count","Multi-agent review in Deep mode adds ~2-5 minute latency overhead"],"requires":["Python 3.9+","LLM provider with strong writing capability","For Deep mode: multi-agent orchestration support (AG2 or custom)"],"input_types":["research query (text)","report_type parameter: 'standard' | 'detailed' | 'deep'"],"output_types":["markdown report with inline citations","JSON structure with sources, sections, metadata","for Deep mode: review comments and revision history"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_3","uri":"capability://search.retrieval.web.scraping.and.document.loading.with.multi.source.retrieval","name":"web scraping and document loading with multi-source retrieval","description":"Retrieves research data from multiple sources: web search (Google, Bing, DuckDuckGo), web scraping with browser automation (Playwright/Selenium), document loading (PDF, DOCX, TXT), and cloud storage (S3, Google Drive). Implements source validation, domain filtering, and deduplication. Each retriever is pluggable; custom retrievers can be added by implementing a standard interface. Handles JavaScript-heavy sites via headless browser execution.","intents":["I want to research a topic by automatically scraping relevant web pages and PDFs","I need to filter results by domain (e.g., only academic sources or exclude certain domains)","I want to load research data from cloud storage or local documents alongside web search"],"best_for":["research teams needing multi-source data aggregation","applications requiring domain-specific source filtering","workflows combining web research with proprietary document analysis"],"limitations":["Web scraping may violate robots.txt or terms of service; requires careful domain filtering","JavaScript-heavy sites require headless browser execution, adding 2-5 second latency per page","PDF extraction quality varies by document structure; complex layouts may fail","Rate limiting from search providers may throttle requests during high-volume research"],"requires":["Python 3.9+","Playwright or Selenium for browser automation","Search API keys (Google Custom Search, Bing, DuckDuckGo)","Optional: cloud storage credentials (AWS S3, Google Drive)"],"input_types":["research query (text)","optional: domain_filter (list of allowed/blocked domains)","optional: source_types (web, pdf, docx, cloud)"],"output_types":["array of Document objects with content, source URL, metadata","source validation results (relevance score, domain check)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_4","uri":"capability://memory.knowledge.context.management.and.token.aware.compression","name":"context management and token-aware compression","description":"Manages research context across multiple sources using a context manager skill that compresses and prioritizes information to fit within LLM context windows. Implements sliding window compression, source ranking by relevance, and automatic truncation strategies. Tracks token usage per model and adjusts compression aggressively for smaller context windows (e.g., Ollama local models). Deduplicates overlapping information across sources before compression.","intents":["I want to research with local models that have small context windows (4K-8K tokens)","I need to include 20+ sources in a report without exceeding token limits","I want to prioritize the most relevant sources when context is limited"],"best_for":["teams using local or smaller LLMs with limited context windows","research workflows with many sources that must fit in a single prompt","cost-sensitive applications where token usage directly impacts expenses"],"limitations":["Aggressive compression may lose nuance or important details from sources","Token counting is approximate; actual usage may exceed estimates by 5-10%","Deduplication logic may incorrectly merge similar but distinct information","Sliding window approach may miss connections between distant sources"],"requires":["Python 3.9+","LLM provider with known token counting (OpenAI, Anthropic)","Source ranking/relevance scoring mechanism"],"input_types":["array of Document objects with content and metadata","target_tokens parameter (e.g., 8000 for 8K context window)","model_name for token counting"],"output_types":["compressed context string","compression metadata (original tokens, compressed tokens, sources included)","source ranking with relevance scores"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_5","uri":"capability://planning.reasoning.multi.agent.orchestration.with.chiefeditoragent","name":"multi-agent orchestration with chiefeditoragent","description":"Orchestrates specialized research agents (ResearcherAgent, WriterAgent, ReviewerAgent, CuratorAgent) through a ChiefEditorAgent that assigns tasks, manages state, and coordinates review-revision workflows. Each agent has specific skills: ResearcherAgent gathers sources, WriterAgent synthesizes reports, ReviewerAgent validates facts, CuratorAgent filters sources. Implements task dependency tracking, state persistence, and inter-agent communication via message passing. Supports both sequential and parallel agent execution patterns.","intents":["I want multiple specialized agents working together on research (one gathering sources, one writing, one reviewing)","I need fact-checking and revision workflows where reviewers can send feedback back to writers","I want to scale research quality by having specialized agents focus on their strengths"],"best_for":["teams building complex research workflows with multiple specialized roles","applications requiring fact-checking and multi-round revision","organizations where research quality is critical and worth the latency cost"],"limitations":["Multi-agent orchestration adds 5-15 minute latency vs single-agent research","State management across agents requires careful synchronization; bugs can cause inconsistent results","Inter-agent communication overhead increases API calls by 3-5x","Requires careful prompt engineering for each agent role to avoid conflicts"],"requires":["Python 3.9+","LLM provider with strong reasoning (GPT-4, Claude 3.5+)","State management system (in-memory or persistent store)","Optional: AG2 framework for advanced multi-agent patterns"],"input_types":["research query (text)","multi_agent_config with agent roles and task definitions","optional: max_iterations for review-revision cycles"],"output_types":["final research report","agent execution trace with task assignments","revision history showing feedback and changes"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_6","uri":"capability://data.processing.analysis.source.curation.and.validation.with.relevance.scoring","name":"source curation and validation with relevance scoring","description":"Validates and ranks research sources using a CuratorAgent that implements relevance scoring, source credibility assessment, and duplicate detection. Scores sources based on domain authority, content relevance to query, recency, and citation count. Filters out low-quality sources, spam, and duplicates before inclusion in reports. Implements domain-specific credibility rules (e.g., academic sources ranked higher for scientific queries). Provides source metadata including relevance scores and validation reasons.","intents":["I want to automatically filter out low-quality or spam sources from research results","I need sources ranked by credibility and relevance, not just search ranking","I want domain-specific source filtering (e.g., prefer academic sources for scientific research)"],"best_for":["research teams where source quality directly impacts report credibility","applications requiring transparent source validation for compliance","workflows combining web search with proprietary or academic sources"],"limitations":["Credibility assessment is heuristic-based; may incorrectly rank emerging sources or new domains","Duplicate detection uses content similarity; may miss paraphrased duplicates","Domain-specific rules require manual configuration per domain","Relevance scoring adds ~1-2 second latency per source"],"requires":["Python 3.9+","LLM provider for relevance assessment","Optional: domain credibility database or configuration"],"input_types":["array of Document objects from retrievers","research query (text) for relevance scoring","optional: domain_rules configuration"],"output_types":["filtered and ranked Document array","relevance scores and validation metadata per source","rejection reasons for filtered sources"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_7","uri":"capability://tool.use.integration.websocket.based.real.time.research.streaming.with.fastapi.backend","name":"websocket-based real-time research streaming with fastapi backend","description":"Exposes research capabilities via FastAPI backend with WebSocket support for real-time streaming of research progress. Clients connect via WebSocket and receive live updates as research progresses: query decomposition, sub-query execution, source retrieval, and report generation. Implements message-based protocol with event types (query_decomposed, sources_found, report_section_generated, etc.). Supports concurrent research sessions with state isolation. Includes REST API for batch research and configuration management.","intents":["I want to show users real-time research progress in a web UI as it happens","I need a backend API that supports both real-time streaming and batch research","I want to build interactive research tools where users can see sources and progress live"],"best_for":["web applications requiring real-time research progress visualization","teams building interactive research UIs with live updates","applications where user engagement benefits from seeing research in progress"],"limitations":["WebSocket connections require persistent network; unreliable networks may drop updates","State isolation per session adds memory overhead; high concurrency may require horizontal scaling","Message serialization/deserialization adds ~50-100ms latency per update","Browser-based clients may have connection limits (typically 6-10 concurrent WebSockets)"],"requires":["Python 3.9+","FastAPI 0.95+","WebSocket-capable client (browser with native WebSocket or library)","Optional: Redis for session state in distributed deployments"],"input_types":["research query (text) via WebSocket message","configuration parameters (report_type, max_sources, etc.)"],"output_types":["WebSocket messages with event type and payload","final research report as last message","REST API responses (JSON) for batch requests"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_8","uri":"capability://image.visual.image.generation.for.research.reports.with.dall.e.integration","name":"image generation for research reports with dall-e integration","description":"Automatically generates relevant images for research reports using DALL-E 3 integration. Analyzes report sections and generates descriptive prompts for images that illustrate key concepts. Embeds generated images into markdown reports with captions. Supports image caching to avoid regenerating identical images. Implements fallback to stock image APIs if DALL-E fails. Configurable per-section image generation (e.g., only generate for introduction and conclusion).","intents":["I want research reports to include relevant illustrations without manual image sourcing","I need to make reports more visually engaging for presentations or publications","I want to automatically generate diagrams or concept illustrations from text descriptions"],"best_for":["teams generating research reports for external audiences (clients, publications)","applications where visual engagement improves user experience","workflows where report quality and presentation matter as much as content"],"limitations":["DALL-E image generation costs $0.04-0.20 per image; adds significant cost for large reports","Generated images may not match report content perfectly; requires human review for critical reports","Image generation adds 5-10 seconds per image to report generation time","Caching requires persistent storage; distributed deployments need shared cache"],"requires":["Python 3.9+","OpenAI API key with DALL-E 3 access","Optional: image caching backend (S3, local filesystem)"],"input_types":["research report (markdown or structured format)","optional: image_config with section selection and style preferences"],"output_types":["markdown report with embedded images","image metadata (URL, prompt, generation time, cost)","image cache for reuse"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-assafelovic-gpt-researcher__cap_9","uri":"capability://memory.knowledge.vector.store.integration.for.semantic.search.and.rag","name":"vector store integration for semantic search and rag","description":"Integrates with vector databases (Pinecone, Weaviate, Chroma, Milvus) for semantic search and retrieval-augmented generation. Embeds research sources using sentence transformers or OpenAI embeddings, stores in vector DB, and retrieves semantically similar documents for context. Supports hybrid search combining vector similarity with keyword matching. Implements embedding caching to avoid recomputing embeddings for identical sources. Enables long-term knowledge accumulation across research sessions.","intents":["I want to search research sources semantically, not just by keywords","I need to build a knowledge base of research across multiple sessions and queries","I want to combine semantic search with keyword filtering for precise retrieval"],"best_for":["teams conducting ongoing research with knowledge accumulation requirements","applications requiring semantic understanding of research content","workflows where keyword search is insufficient (e.g., concept-based research)"],"limitations":["Embedding generation adds 1-2 seconds per source; scales poorly with large source counts","Vector DB setup and maintenance adds operational complexity","Semantic search quality depends on embedding model; weak models may miss relevant sources","Hybrid search requires tuning weight between vector and keyword components"],"requires":["Python 3.9+","Vector database (Pinecone, Weaviate, Chroma, Milvus, etc.)","Embedding model (sentence-transformers or OpenAI embeddings)","Optional: embedding caching backend"],"input_types":["array of Document objects to embed and store","search query (text) for semantic retrieval","optional: hybrid_search_weights for vector/keyword balance"],"output_types":["array of semantically similar Document objects","similarity scores per document","embedding metadata (model, dimension, timestamp)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, etc.) OR local Ollama instance","Network access to provider APIs or local model server","LLM provider with strong reasoning capability (GPT-4, Claude 3.5+)","Concurrent execution support (asyncio, thread pool)","MCP-compatible client (Claude, etc.)","MCP server implementation (included in GPT Researcher)","Optional: YAML/JSON config files","Optional: environment variable setup in deployment environment","Optional: custom domain configuration"],"failure_modes":["Three-tier strategy adds ~500ms latency overhead for fallback evaluation","Model-specific prompt tuning required for optimal results across different providers","Context window mismatches between providers may cause truncation without explicit handling","Decomposition quality depends on planner LLM capability; weak planners may miss important angles","Parallel execution increases total API calls by 5-10x vs sequential approach","Result merging requires deduplication logic that may miss subtle variations in sources","MCP protocol overhead adds ~100-200ms per request","Streaming responses may not work reliably with all MCP clients","MCP server requires separate process; adds deployment complexity","Limited to MCP-compatible clients; not usable with standard REST API clients","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7722910995464346,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-04-16T17:41:04Z"},"community":{"stars":26842,"forks":3594,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-assafelovic-gpt-researcher","compare_url":"https://unfragile.ai/compare?artifact=mcp-assafelovic-gpt-researcher"}},"signature":"2HrKdKgIwlWg719jP8yg3F1Fle2mVJlmq8iJgnBswbrC7dIdZ59M6zN+GUkdtX3LfqQNo9wBfMlgkIfD0TdrCQ==","signedAt":"2026-06-22T06:00:34.330Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-assafelovic-gpt-researcher","artifact":"https://unfragile.ai/mcp-assafelovic-gpt-researcher","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-assafelovic-gpt-researcher","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"}}