MCP Radar
MCP 서버

Tresslers Intelligence MCP Server

io.github.classofdoom/tresslers-intelligence
지식 및 문서 과학 및 엔지니어링 검색 및 리서치 공개 · 연결 가능 MCP 2026-07-28

이 MCP로 할 수 있는 일

Searches and retrieves intelligence dossiers and knowledge-graph context, provides model and prediction records, and performs restricted in-silico codon optimization.

기능

도구

11 도구
ask_intelligence_rag
Ask a natural-language question and receive structured intelligence context retrieved from Tresslers Group dossiers via RAG (Retrieval Augmented Generation). Returns relevant document chunks, source citations, conviction metadata, and graph neighborhood data. The calling LLM should synthesize the returned context into a coherent answer.
입력 스키마 
{'type': 'object', 'required': ['question'], 'properties': {'question': {'type': 'string', 'description': "Natural language question (e.g., 'What are the key geopolitical risks in sovereign AI policy?')."}, 'max_sources': {'type': 'number', 'description': 'Maximum number of source dossier chunks to retrieve (default 5, max 8).'}}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'Structured RAG response object with chunks, source citations, conviction metadata, and graph neighborhood.'}, 'type': {'type': 'string'}}}}}}
get_alpha_gap
Retrieves prediction market Alpha Gaps comparing Tresslers Group Bayesian model odds against Polymarket order books, along with Brier scores, calibration alpha (+3,480 bps benchmark), and cryptographic SHA-256 pre-commitments.
입력 스키마 
{'type': 'object', 'properties': {'limit': {'type': 'number', 'description': 'Maximum number of Alpha Gap records to return.'}, 'status': {'enum': ['ALL', 'RESOLVED', 'PENDING', 'CONTRACT_SETTLED', 'OUTCOME_TRACKED'], 'type': 'string', 'description': "Filter by status or settlement type (e.g., 'CONTRACT_SETTLED', 'OUTCOME_TRACKED', 'RESOLVED', 'PENDING', 'ALL')."}, 'category': {'type': 'string', 'description': 'Optional filter by strategic intelligence domain.'}, 'min_divergence': {'type': 'number', 'description': 'Minimum absolute divergence gap in percentage points (e.g. 20 for >= 20% gap).'}, 'settlement_type': {'enum': ['all', 'contract_settled', 'outcome_tracked', 'unscored_archive'], 'type': 'string', 'description': "Filter by settlement tier: 'contract_settled', 'outcome_tracked', 'unscored_archive', or 'all'."}}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'JSON representation of prediction market alpha gaps, model odds, Polymarket odds, and Brier calibration scores.'}, 'type': {'type': 'string'}}}}}}
get_dossier_delta
Queries intelligence updates and conviction deltas attested since a specific UTC timestamp.
입력 스키마 
{'type': 'object', 'required': ['since_timestamp'], 'properties': {'since_timestamp': {'type': 'string', 'description': "ISO UTC timestamp (e.g., '2026-05-01T00:00:00Z')."}}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'JSON object listing dossier updates and conviction deltas since timestamp.'}, 'type': {'type': 'string'}}}}}}
get_model_record
Queries the public calibration and backtest ledger of Tresslers Group sovereign intelligence models. Supports filtering by settlement type ('contract_settled' for live prediction markets, 'outcome_tracked' for internal foresight models, 'unscored_archive' for pre-commitment history, or 'all'). Returns Brier calibration scores, transparent failure analysis, and verifiable primary source URLs.
입력 스키마 
{'type': 'object', 'required': [], 'properties': {'status': {'enum': ['ALL', 'RESOLVED', 'PENDING'], 'type': 'string', 'description': "Filter by resolution status (default: 'ALL')."}, 'category': {'type': 'string', 'description': 'Optional filter by intelligence pillar/category.'}, 'settlement_type': {'enum': ['all', 'contract_settled', 'outcome_tracked', 'unscored_archive'], 'type': 'string', 'description': "Filter by settlement tier: 'contract_settled' (live prediction market order-books), 'outcome_tracked' (internal Bayesian foresight), 'unscored_archive' (pre-commitment era), or 'all' (default)."}}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'JSON representation of calibration statistics, hit/miss rates, and full verification records.'}, 'type': {'type': 'string'}}}}}}
list_dossiers
Returns a list of all available intelligence dossier slugs.
입력 스키마 
{'type': 'object', 'required': [], 'properties': {}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'JSON array of available intelligence dossier slugs.'}, 'type': {'type': 'string'}}}}}}
optimize_codon
Optimizes a protein (amino acid) or cDNA coding sequence for maximal recombinant expression yield in a target host organism using the Logos biocomputing engine. Executes Codon Adaptation Index (CAI) maximization, GC-content harmonization, restriction site avoidance, and ribosomal consensus leader generation. Research Use Only (RUO). In-silico modeling only; select-agent and regulated pathogen sequence optimization is strictly prohibited under 42 CFR 73 and BWC compliance. Free and open to all AI agents.
입력 스키마 
{'type': 'object', 'required': ['sequence'], 'properties': {'host': {'enum': ['e_coli', 'h_sapiens', 'c_griseus', 's_cerevisiae', 'p_pastoris', 'v_natriegens', 'n_benthamiana', 'a_thaliana'], 'type': 'string', 'description': "Target expression organism: 'e_coli', 'h_sapiens' (HEK293), 'c_griseus' (CHO), 's_cerevisiae', 'p_pastoris', 'v_natriegens', 'n_benthamiana', 'a_thaliana'. Default: 'e_coli'"}, 'sequence': {'type': 'string', 'description': "Amino acid sequence (single-letter uppercase e.g. 'MSKGEELFT...') or DNA coding sequence to optimize."}, 'gc_target': {'type': 'number', 'description': 'Target global GC percentage (e.g., 52 for E. coli, 58 for Human). Optional.'}}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'JSON representation of optimized DNA sequence, CAI score, GC content, and upstream consensus leader.'}, 'type': {'type': 'string'}}}}}}
query_knowledge_graph
Traverses the multi-hop conceptual knowledge graph across entities (GraphRAG). Free preview available for direct 1-hop adjacencies. Deeper reasoning (2-3 hops) requires x402 settlement.
입력 스키마 
{'type': 'object', 'required': ['entity_id'], 'properties': {'depth': {'type': 'number', 'description': 'Graph traversal hop depth (1 for free preview, 2 to 3 for deep reasoning).'}, 'entity_id': {'type': 'string', 'description': "Target conceptual entity or dossier slug (e.g., 'Quantum AI', 'ThinkForge')."}, 'payment_proof': {'type': 'string', 'description': 'Standard x402 signed USDC transaction proof (0x...). Required for multi-hop graph access.'}}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'GraphRAG entity neighborhood JSON structure.'}, 'type': {'type': 'string'}}}}}}
read_dossier
Reads the pure text contents of a specific intelligence dossier. Requires x402 payment proof for premium intelligence. Without payment proof, returns a high-fidelity strategic excerpt and payment challenge.
입력 스키마 
{'type': 'object', 'required': ['slug'], 'properties': {'slug': {'type': 'string', 'description': "The slug of the dossier to read (e.g., 'sovereign-ai-state-national-policy-2026')."}, 'payment_proof': {'type': 'string', 'description': 'Standard x402 signed USDC transaction proof (0x...). Required for full decrypted access.'}}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'Full dossier text content or x402 payment challenge JSON.'}, 'type': {'type': 'string'}}}}, 'isError': {'type': 'boolean'}}}
search_dossiers
Searches and filters across all published Tresslers Group intelligence dossiers by keywords, strategic domain/category, tags, or conviction threshold.
입력 스키마 
{'type': 'object', 'required': ['query'], 'properties': {'tag': {'type': 'string', 'description': "Optional tag filter (e.g., 'sovereign-ai', 'energy-transition')."}, 'limit': {'type': 'number', 'description': 'Maximum number of dossiers to return (default: 5, max: 20).'}, 'query': {'type': 'string', 'description': 'Search query or keywords to match across title, excerpt, content, and tags.'}, 'category': {'type': 'string', 'description': "Optional category filter (e.g., 'Geopolitics & Sovereign Policy', 'Energy & Infrastructure')."}, 'min_conviction': {'type': 'number', 'description': 'Minimum conviction threshold (0.0 to 1.0, e.g., 0.85).'}}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'JSON representation of matching dossiers with scores, excerpts, and metadata.'}, 'type': {'type': 'string'}}}}}}
search_intelligence_matrix
Executes a semantic vector similarity search across the entire ThinkForge intelligence substrate. Returns relevant snippets and strategic conviction metadata.
입력 스키마 
{'type': 'object', 'required': ['query'], 'properties': {'limit': {'type': 'number', 'description': 'Max results to return (default 5, max 10).'}, 'query': {'type': 'string', 'description': "Semantic research question (e.g., 'What are the geopolitical vulnerabilities in green hydrogen supply chains?')."}, 'filter': {'type': 'string', 'description': "Optional SQL-like metadata filter (e.g., 'convictionScore >= 0.85')."}}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'JSON array of matching semantic intelligence chunks with similarity scores and conviction metadata.'}, 'type': {'type': 'string'}}}}}}
verify_commitment
Cryptographically verifies a predictive alpha pre-commitment against the immutable SHA-256 ledger using either a 64-character hash digest or canonical preimage parameters.
입력 스키마 
{'type': 'object', 'properties': {'hash': {'type': 'string', 'description': '64-character lowercase hexadecimal SHA-256 commitment digest.'}, 'preimage': {'type': 'string', 'description': 'Full pipe-delimited UTF-8 canonical preimage string (YYYY-MM-DD|question|model_odds|target_dossier_slug).'}, 'model_odds': {'type': 'number', 'description': 'Model odds integer component (0-100).'}, 'dossier_slug': {'type': 'string', 'description': 'Target dossier slug component.'}, 'dispatch_date': {'type': 'string', 'description': 'Dispatch date component (YYYY-MM-DD).'}, 'market_question': {'type': 'string', 'description': 'Exact market question component.'}, 'canonical_preimage': {'type': 'string', 'description': 'Alternative key for canonical preimage string.'}}}
출력 스키마 
{'type': 'object', 'properties': {'content': {'type': 'array', 'items': {'type': 'object', 'properties': {'text': {'type': 'string', 'description': 'JSON representation of cryptographic verification result, resolution details, and integrity proof.'}, 'type': {'type': 'string'}}}}}}
기록

최근 도구 변경

추가됨
get_alpha_gap
2026년 9월 28일 2:40 AM
추가됨
verify_commitment
2026년 9월 28일 2:40 AM
추가됨
search_dossiers
2026년 9월 28일 2:40 AM
추가됨
get_model_record
2026년 9월 28일 2:40 AM
추가됨
optimize_codon
2026년 9월 28일 2:40 AM
추가됨
ask_intelligence_rag
2026년 9월 28일 2:40 AM
추가됨
get_dossier_delta
2026년 9월 28일 2:40 AM
추가됨
query_knowledge_graph
2026년 9월 28일 2:40 AM
추가됨
search_intelligence_matrix
2026년 9월 28일 2:40 AM
추가됨
read_dossier
2026년 9월 28일 2:40 AM
추가됨
list_dossiers
2026년 9월 28일 2:40 AM
탐색

유사한 MCP 서버

Inspire Hep

io.github.pipeworx-io/inspire-hep

Searches and retrieves high-energy physics literature, authors, institutions, and conference records from INSPIRE-HEP.

Hal Fr

io.github.pipeworx-io/hal-fr

Searches and retrieves scholarly documents and author records from the HAL French national open research archive.

Crossref

io.github.pipeworx-io/crossref

Searches Crossref’s scholarly metadata and retrieves publication, journal, DOI, author, abstract, citation, and subject informati…

papers

org.agentpub/papers

Provides an AI research publication platform for submitting papers, peer review, citations, annotations, academic search, and res…

ScholarFetch

io.github.laibniz/scholarfetch

Searches scholarly literature, retrieves abstracts and recoverable full text, traverses references and authors, and manages expor…

embedded-docs

ai.byteask/embedded-docs

Searches and retrieves page-cited sections from embedded systems, firmware, hardware, standards, protocol, and instrument documen…

Syntology

ai.syntology/graph

Provides a provenance-tagged knowledge graph covering AI and machine learning papers, citations, methods, and code.

OpenArx

ai.openarx/openarx

Enables agents to search scientific and engineering information, inspect evidence, and publish documents.