{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-deepresearch","slug":"deepresearch","name":"DeepResearch","type":"mcp","url":"https://github.com/OctagonAI/octagon-deep-research-mcp","page_url":"https://unfragile.ai/deepresearch","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-deepresearch__cap_0","uri":"capability://automation.workflow.parallel.research.orchestration","name":"parallel-research-orchestration","description":"Orchestrates unlimited concurrent research tasks across multiple LLM providers and search backends using an MCP-based task queue architecture. Distributes research queries to parallel workers that independently fetch, analyze, and synthesize information, then aggregates results through a coordination layer that deduplicates findings and merges insights from concurrent streams.","intents":["Run 10+ research queries simultaneously without blocking on individual results","Scale research depth across multiple topics in parallel without hitting API rate limits","Distribute research workload across heterogeneous LLM providers (OpenAI, Anthropic, local models) in parallel"],"best_for":["teams building research automation platforms needing horizontal scalability","enterprises conducting competitive intelligence across multiple domains simultaneously","LLM application developers requiring non-blocking research pipelines"],"limitations":["Parallel execution adds complexity to result ordering and deduplication logic","No built-in distributed state persistence — requires external message queue or database for fault tolerance","Concurrent API calls may trigger rate-limiting on downstream search/LLM services despite internal parallelization"],"requires":["MCP server runtime (Node.js 16+ or Python 3.8+)","API credentials for at least one LLM provider (OpenAI, Anthropic, or local Ollama instance)","Network connectivity to search backends (web search, knowledge bases, or custom APIs)"],"input_types":["research queries (natural language strings)","research parameters (depth level, source preferences, output format)"],"output_types":["structured research reports (JSON with sections, citations, confidence scores)","aggregated findings with source attribution","synthesis summaries combining insights from parallel research streams"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepresearch__cap_1","uri":"capability://data.processing.analysis.multi.source.information.synthesis","name":"multi-source-information-synthesis","description":"Aggregates and synthesizes information from heterogeneous sources (web search, knowledge bases, APIs, documents) by maintaining separate retrieval contexts per source and applying cross-source deduplication and conflict resolution. Uses a synthesis layer that identifies contradictions, weights sources by reliability, and produces unified findings with explicit source attribution and confidence scores.","intents":["Combine research findings from web search, internal knowledge bases, and custom APIs into a single coherent report","Detect and resolve contradictions between sources with explicit confidence scoring","Attribute claims to specific sources with citation links and reliability metadata"],"best_for":["research teams needing multi-source fact-checking and verification","enterprises integrating proprietary knowledge bases with public web search","compliance-heavy domains (legal, medical, financial) requiring auditable source attribution"],"limitations":["Synthesis quality depends on source diversity — homogeneous sources may produce redundant findings","Cross-source conflict resolution is heuristic-based and may miss nuanced domain-specific contradictions","No built-in source credibility scoring — relies on manual source configuration or external reputation APIs"],"requires":["MCP server with tool definitions for each source (web search, knowledge base, API endpoints)","Source configuration specifying retrieval patterns and response schemas","LLM with sufficient context window to process multi-source results (8k+ tokens recommended)"],"input_types":["research queries","source preferences and weights","conflict resolution strategies"],"output_types":["synthesized findings with source attribution","conflict reports highlighting contradictions","confidence-scored claims with evidence chains"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepresearch__cap_2","uri":"capability://planning.reasoning.adaptive.research.depth.control","name":"adaptive-research-depth-control","description":"Dynamically adjusts research depth and breadth based on query complexity and information sufficiency signals. Implements a feedback loop where the research agent evaluates whether current findings meet quality thresholds (coverage, confidence, source diversity) and either terminates early or expands search scope by querying additional sources, drilling deeper into specific topics, or reformulating queries.","intents":["Automatically stop research when sufficient information is gathered, avoiding wasted API calls","Expand research scope when initial results are insufficient or contradictory","Adapt search strategy based on query complexity (simple factual queries vs. nuanced analysis)"],"best_for":["cost-conscious teams needing to minimize API spend while maintaining research quality","applications with variable query complexity requiring dynamic resource allocation","research platforms serving diverse user needs from quick fact-checks to deep analysis"],"limitations":["Sufficiency evaluation is heuristic-based and may terminate prematurely on ambiguous queries","No built-in mechanism to detect when deeper research would yield diminishing returns","Requires careful tuning of depth thresholds — overly aggressive termination reduces quality, overly conservative increases latency"],"requires":["LLM capable of evaluating research sufficiency (Claude 3+, GPT-4, or equivalent)","Configurable depth parameters (max iterations, source count, token budget)","Feedback signals from downstream consumers (user satisfaction, query reformulation rates)"],"input_types":["research queries with optional complexity hints","depth preferences (quick, standard, deep)","quality thresholds (confidence scores, source diversity requirements)"],"output_types":["research reports with depth metadata","termination signals with sufficiency justification","expansion recommendations when depth is insufficient"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepresearch__cap_3","uri":"capability://tool.use.integration.mcp.based.tool.orchestration","name":"mcp-based-tool-orchestration","description":"Exposes research capabilities as MCP tools that can be called by any MCP-compatible client (Claude Desktop, custom agents, IDE extensions). Implements the MCP protocol for tool definition, argument validation, and result streaming, allowing seamless integration into existing LLM workflows without custom API clients. Supports both request-response and streaming result patterns for long-running research tasks.","intents":["Integrate deep research into Claude Desktop or other MCP clients without writing custom code","Call research tools from custom LLM agents using standard MCP function-calling patterns","Stream research results back to clients as they become available rather than waiting for completion"],"best_for":["developers building Claude Desktop plugins or extensions","teams using MCP as their LLM integration standard","applications needing to add research capabilities to existing LLM workflows with minimal refactoring"],"limitations":["MCP protocol overhead adds ~50-100ms per tool call compared to direct function invocation","Streaming results require client-side buffering and reassembly logic","Tool argument validation is schema-based and may not catch semantic errors (e.g., contradictory parameters)"],"requires":["MCP server runtime compatible with client (Node.js 16+ or Python 3.8+)","MCP-compatible client (Claude Desktop, custom agent with MCP support, or IDE extension)","Tool schema definitions in MCP format (JSON schema for arguments and results)"],"input_types":["MCP tool calls with typed arguments","streaming requests for long-running research","batch tool calls for multiple research queries"],"output_types":["MCP tool results (JSON-structured research reports)","streaming result chunks for progressive rendering","error responses with diagnostic information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepresearch__cap_4","uri":"capability://data.processing.analysis.research.result.caching.and.deduplication","name":"research-result-caching-and-deduplication","description":"Caches research results at multiple levels (query-level, source-level, finding-level) to avoid redundant API calls and computation. Implements semantic deduplication that identifies equivalent findings across parallel research streams and merges them with source attribution. Uses content hashing and semantic similarity matching to detect duplicate information even when phrased differently.","intents":["Avoid re-fetching the same information when similar queries are run","Merge duplicate findings from parallel research streams into single deduplicated results","Reduce API costs by caching source responses and reusing them across multiple research tasks"],"best_for":["research platforms with repeated queries or overlapping research topics","cost-sensitive applications where API spend is a primary concern","teams running high-volume research workloads with significant query overlap"],"limitations":["Cache invalidation is time-based or manual — no automatic detection of source updates","Semantic deduplication requires embedding model or LLM calls, adding latency and cost","Cache storage grows unbounded without pruning policies — requires external cache management"],"requires":["Cache backend (Redis, in-memory store, or database)","Embedding model for semantic similarity (optional but recommended for deduplication quality)","Cache key strategy and TTL configuration"],"input_types":["research queries","cache configuration (TTL, similarity threshold)","source responses"],"output_types":["cached research results with hit/miss metadata","deduplicated findings with source attribution","cache statistics (hit rate, storage size)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepresearch__cap_5","uri":"capability://tool.use.integration.configurable.search.backend.integration","name":"configurable-search-backend-integration","description":"Abstracts search backend selection through a pluggable interface that supports multiple search providers (web search APIs, knowledge bases, document stores, custom endpoints). Each backend is configured with retrieval patterns, response schemas, and reliability metadata. The research agent selects appropriate backends based on query type and source preferences, with fallback logic when primary sources are unavailable.","intents":["Switch between different search providers (Google, Bing, DuckDuckGo, custom APIs) without changing research logic","Combine web search with proprietary knowledge bases or document stores in a single research workflow","Configure source reliability and weighting to influence which backends are queried first"],"best_for":["enterprises with heterogeneous data sources (public web, internal knowledge bases, third-party APIs)","applications needing to switch search providers for cost or compliance reasons","research platforms serving multiple use cases with different source requirements"],"limitations":["Backend abstraction adds complexity to result parsing and normalization","Response schema mismatches between backends may cause silent failures or data loss","No built-in load balancing across backends — requires manual configuration of fallback order"],"requires":["Backend configuration (API endpoints, authentication, response schemas)","Backend adapter implementations for each source type","Query routing logic to select appropriate backends"],"input_types":["backend configurations (endpoints, auth, schemas)","source preferences and weights","query routing rules"],"output_types":["normalized results from heterogeneous backends","backend selection metadata","fallback/retry information"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepresearch__cap_6","uri":"capability://data.processing.analysis.research.quality.scoring.and.validation","name":"research-quality-scoring-and-validation","description":"Evaluates research quality across multiple dimensions (source credibility, information freshness, finding confidence, coverage breadth) and produces quality scores that guide further research or termination decisions. Implements validation rules that check for contradictions, missing evidence, and insufficient source diversity. Produces quality reports that explain which dimensions are weak and what additional research would improve quality.","intents":["Automatically assess whether research findings meet quality standards before returning to user","Identify weak areas in research (e.g., insufficient source diversity) and recommend additional queries","Provide transparency into research quality through detailed scoring and validation reports"],"best_for":["compliance-heavy domains (legal, medical, financial) requiring auditable quality assurance","research platforms serving quality-conscious users who need transparency into findings","applications where research quality directly impacts downstream decisions"],"limitations":["Quality scoring is heuristic-based and domain-specific — generic scores may not reflect domain requirements","Validation rules require manual configuration per domain or use case","No built-in mechanism to detect when quality improvements have diminishing returns"],"requires":["Quality scoring configuration (weights for credibility, freshness, confidence, coverage)","Validation rules (contradiction detection, evidence requirements, source diversity thresholds)","LLM for evaluating subjective quality dimensions"],"input_types":["research findings with source metadata","quality configuration and thresholds","domain-specific validation rules"],"output_types":["quality scores (overall and per-dimension)","validation reports with identified issues","recommendations for improving quality"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepresearch__cap_7","uri":"capability://planning.reasoning.context.aware.query.reformulation","name":"context-aware-query-reformulation","description":"Automatically reformulates research queries based on initial results to improve coverage, resolve ambiguities, or explore related topics. Analyzes initial findings to identify gaps (missing perspectives, unexplored angles, unanswered sub-questions) and generates follow-up queries that address those gaps. Uses semantic similarity to avoid redundant reformulations and tracks query history to prevent infinite loops.","intents":["Automatically expand research scope when initial results are incomplete or one-sided","Resolve ambiguous queries by generating clarifying sub-queries","Explore related topics and perspectives without explicit user guidance"],"best_for":["research agents that need to autonomously improve coverage without user interaction","applications handling ambiguous or complex queries that benefit from multi-angle exploration","platforms where research depth is a key differentiator"],"limitations":["Query reformulation can lead to infinite loops if not carefully controlled with iteration limits","Reformulated queries may drift from original intent if semantic similarity checks are too loose","No built-in mechanism to detect when additional reformulations have diminishing returns"],"requires":["LLM capable of analyzing findings and generating follow-up queries","Semantic similarity model for deduplication (embeddings or LLM-based similarity)","Query history tracking and loop detection logic"],"input_types":["initial research query","initial research findings","reformulation configuration (max iterations, similarity threshold)"],"output_types":["reformulated queries with justification","expanded research findings from follow-up queries","query history and reformulation decisions"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepresearch__cap_8","uri":"capability://text.generation.language.structured.research.report.generation","name":"structured-research-report-generation","description":"Transforms raw research findings into structured reports with configurable schemas (sections, hierarchies, formatting). Supports multiple output formats (JSON, Markdown, HTML) and can generate reports optimized for different audiences (executives, technical teams, compliance reviewers). Includes automatic table-of-contents generation, citation formatting, and evidence linking.","intents":["Generate professional research reports in multiple formats from raw findings","Customize report structure and content for different audiences","Ensure consistent formatting and citation standards across research outputs"],"best_for":["research platforms serving diverse audiences with different report requirements","enterprises needing standardized research documentation for compliance or knowledge management","applications where report quality and professionalism are key user experience factors"],"limitations":["Report generation adds latency — complex reports with many sections may take 10-30 seconds to generate","Schema customization requires manual configuration per report type","No built-in support for custom visualizations or embedded media"],"requires":["Report schema definitions (sections, hierarchies, formatting rules)","LLM for content organization and summarization","Output format handlers (Markdown, HTML, JSON generators)"],"input_types":["research findings with source attribution","report schema and configuration","audience preferences and formatting rules"],"output_types":["structured reports (JSON, Markdown, HTML)","formatted citations and evidence links","table-of-contents and navigation metadata"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepresearch__cap_9","uri":"capability://automation.workflow.research.task.batching.and.scheduling","name":"research-task-batching-and-scheduling","description":"Groups multiple research queries into batches and schedules execution based on resource availability, cost constraints, and priority levels. Implements backpressure logic to prevent overwhelming downstream services and supports both immediate and deferred execution modes. Tracks task status and provides progress updates for long-running research batches.","intents":["Submit multiple research queries at once and process them efficiently without overwhelming APIs","Prioritize urgent research tasks while deferring lower-priority queries to off-peak hours","Monitor progress of large research batches and get notified when results are ready"],"best_for":["platforms handling high-volume research requests with variable priority","cost-conscious applications needing to batch queries for rate-limit optimization","teams running scheduled research tasks (daily competitive intelligence, market monitoring)"],"limitations":["Batching adds latency for individual queries — immediate results are slower than unbatched execution","Scheduling logic requires external job queue or scheduler (e.g., Redis, Celery)","No built-in support for cross-batch dependencies or conditional execution"],"requires":["Job queue or scheduler (Redis, Celery, or equivalent)","Task status tracking and persistence","Resource availability monitoring and backpressure logic"],"input_types":["research queries with priority levels","batch configuration (size, scheduling rules)","resource constraints (API rate limits, cost budgets)"],"output_types":["batch status and progress updates","task execution results with timing metadata","resource utilization reports"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["MCP server runtime (Node.js 16+ or Python 3.8+)","API credentials for at least one LLM provider (OpenAI, Anthropic, or local Ollama instance)","Network connectivity to search backends (web search, knowledge bases, or custom APIs)","MCP server with tool definitions for each source (web search, knowledge base, API endpoints)","Source configuration specifying retrieval patterns and response schemas","LLM with sufficient context window to process multi-source results (8k+ tokens recommended)","LLM capable of evaluating research sufficiency (Claude 3+, GPT-4, or equivalent)","Configurable depth parameters (max iterations, source count, token budget)","Feedback signals from downstream consumers (user satisfaction, query reformulation rates)","MCP server runtime compatible with client (Node.js 16+ or Python 3.8+)"],"failure_modes":["Parallel execution adds complexity to result ordering and deduplication logic","No built-in distributed state persistence — requires external message queue or database for fault tolerance","Concurrent API calls may trigger rate-limiting on downstream search/LLM services despite internal parallelization","Synthesis quality depends on source diversity — homogeneous sources may produce redundant findings","Cross-source conflict resolution is heuristic-based and may miss nuanced domain-specific contradictions","No built-in source credibility scoring — relies on manual source configuration or external reputation APIs","Sufficiency evaluation is heuristic-based and may terminate prematurely on ambiguous queries","No built-in mechanism to detect when deeper research would yield diminishing returns","Requires careful tuning of depth thresholds — overly aggressive termination reduces quality, overly conservative increases latency","MCP protocol overhead adds ~50-100ms per tool call compared to direct function invocation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"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":"2026-06-17T09:51:03.037Z","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=deepresearch","compare_url":"https://unfragile.ai/compare?artifact=deepresearch"}},"signature":"4uOV/eetUy2TC/lGV8phoUS+kfmf3WxTEkw/ZGqir8jYMgsiX3ZVWRpaHH/NvYy0XGeUT1sHj0YxYwTunTVJDg==","signedAt":"2026-06-20T12:08:44.607Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepresearch","artifact":"https://unfragile.ai/deepresearch","verify":"https://unfragile.ai/api/v1/verify?slug=deepresearch","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"}}