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Tresslers Intelligence MCP Server

Sovereign intelligence dossiers, daily briefings, RAG search, knowledge graph, codon optimizer.

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质量与安全性

A
描述质量
96%
模式完整度
91%
命名质量
96%
投毒风险
100%
权限匹配度
100%
协议合规性
100%

基于对工具定义和协议合规性的自动分析。

上下文开销

~2,558token 数（工具定义）
~2.1 KB典型响应大小
对注意力有中等影响（占 128k 上下文窗口的 2.00%）

这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高，可用于其他任务的注意力就越少。

安装

一键安装

将以下内容添加到你的 `claude_desktop_config.json` 文件中：

{
  "mcpServers": {
    "tresslers-intelligence": {
      "url": "https://tresslersgroup.com/api/mcp"
    }
  }
}

远程端点

https://tresslersgroup.com/api/mcpstreamable-http

它能做什么

工具清单

工具（11）

🟢 只读🟡 写入🔴 删除⚪ 未知
🟢list_dossiers

Returns a list of all available intelligence dossier slugs.

输入模式

{
  "type": "object",
  "properties": {},
  "required": []
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "JSON array of available intelligence dossier slugs."
          }
        }
      }
    }
  }
}
🟢read_dossier(slug, payment_proof)

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.

输入模式

{
  "type": "object",
  "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."
    }
  },
  "required": [
    "slug"
  ]
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "Full dossier text content or x402 payment challenge JSON."
          }
        }
      }
    },
    "isError": {
      "type": "boolean"
    }
  }
}
🟢search_intelligence_matrix(query, filter, limit)

Executes a semantic vector similarity search across the entire ThinkForge intelligence substrate. Returns relevant snippets and strategic conviction metadata.

输入模式

{
  "type": "object",
  "properties": {
    "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')."
    },
    "limit": {
      "type": "number",
      "description": "Max results to return (default 5, max 10)."
    }
  },
  "required": [
    "query"
  ]
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "JSON array of matching semantic intelligence chunks with similarity scores and conviction metadata."
          }
        }
      }
    }
  }
}
🟢query_knowledge_graph(entity_id, depth, payment_proof)

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.

输入模式

{
  "type": "object",
  "properties": {
    "entity_id": {
      "type": "string",
      "description": "Target conceptual entity or dossier slug (e.g., 'Quantum AI', 'ThinkForge')."
    },
    "depth": {
      "type": "number",
      "description": "Graph traversal hop depth (1 for free preview, 2 to 3 for deep reasoning)."
    },
    "payment_proof": {
      "type": "string",
      "description": "Standard x402 signed USDC transaction proof (0x...). Required for multi-hop graph access."
    }
  },
  "required": [
    "entity_id"
  ]
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "GraphRAG entity neighborhood JSON structure."
          }
        }
      }
    }
  }
}
🟢get_dossier_delta(since_timestamp)

Queries intelligence updates and conviction deltas attested since a specific UTC timestamp.

输入模式

{
  "type": "object",
  "properties": {
    "since_timestamp": {
      "type": "string",
      "description": "ISO UTC timestamp (e.g., '2026-05-01T00:00:00Z')."
    }
  },
  "required": [
    "since_timestamp"
  ]
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "JSON object listing dossier updates and conviction deltas since timestamp."
          }
        }
      }
    }
  }
}
⚪ask_intelligence_rag(question, max_sources)

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.

输入模式

{
  "type": "object",
  "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)."
    }
  },
  "required": [
    "question"
  ]
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "Structured RAG response object with chunks, source citations, conviction metadata, and graph neighborhood."
          }
        }
      }
    }
  }
}
🟢optimize_codon(sequence, host, gc_target)

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.

输入模式

{
  "type": "object",
  "properties": {
    "sequence": {
      "type": "string",
      "description": "Amino acid sequence (single-letter uppercase e.g. 'MSKGEELFT...') or DNA coding sequence to optimize."
    },
    "host": {
      "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'",
      "enum": [
        "e_coli",
        "h_sapiens",
        "c_griseus",
        "s_cerevisiae",
        "p_pastoris",
        "v_natriegens",
        "n_benthamiana",
        "a_thaliana"
      ]
    },
    "gc_target": {
      "type": "number",
      "description": "Target global GC percentage (e.g., 52 for E. coli, 58 for Human). Optional."
    }
  },
  "required": [
    "sequence"
  ]
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "JSON representation of optimized DNA sequence, CAI score, GC content, and upstream consensus leader."
          }
        }
      }
    }
  }
}
🟢get_model_record(settlement_type, status, category)

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.

输入模式

{
  "type": "object",
  "properties": {
    "settlement_type": {
      "type": "string",
      "enum": [
        "all",
        "contract_settled",
        "outcome_tracked",
        "unscored_archive"
      ],
      "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)."
    },
    "status": {
      "type": "string",
      "enum": [
        "ALL",
        "RESOLVED",
        "PENDING"
      ],
      "description": "Filter by resolution status (default: 'ALL')."
    },
    "category": {
      "type": "string",
      "description": "Optional filter by intelligence pillar/category."
    }
  },
  "required": []
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "JSON representation of calibration statistics, hit/miss rates, and full verification records."
          }
        }
      }
    }
  }
}
🟢search_dossiers(query, category, tag, limit, min_conviction)

Searches and filters across all published Tresslers Group intelligence dossiers by keywords, strategic domain/category, tags, or conviction threshold.

输入模式

{
  "type": "object",
  "properties": {
    "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')."
    },
    "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)."
    },
    "min_conviction": {
      "type": "number",
      "description": "Minimum conviction threshold (0.0 to 1.0, e.g., 0.85)."
    }
  },
  "required": [
    "query"
  ]
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "JSON representation of matching dossiers with scores, excerpts, and metadata."
          }
        }
      }
    }
  }
}
⚪verify_commitment(hash, preimage, canonical_preimage, dispatch_date, market_question, ...)

Cryptographically verifies a predictive alpha pre-commitment against the immutable SHA-256 ledger using either a 64-character hash digest or canonical preimage parameters.

输入模式

{
  "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)."
    },
    "canonical_preimage": {
      "type": "string",
      "description": "Alternative key for canonical preimage string."
    },
    "dispatch_date": {
      "type": "string",
      "description": "Dispatch date component (YYYY-MM-DD)."
    },
    "market_question": {
      "type": "string",
      "description": "Exact market question component."
    },
    "model_odds": {
      "type": "number",
      "description": "Model odds integer component (0-100)."
    },
    "dossier_slug": {
      "type": "string",
      "description": "Target dossier slug component."
    }
  }
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "JSON representation of cryptographic verification result, resolution details, and integrity proof."
          }
        }
      }
    }
  }
}
🟢get_alpha_gap(status, settlement_type, min_divergence, category, limit)

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.

输入模式

{
  "type": "object",
  "properties": {
    "status": {
      "type": "string",
      "enum": [
        "ALL",
        "RESOLVED",
        "PENDING",
        "CONTRACT_SETTLED",
        "OUTCOME_TRACKED"
      ],
      "description": "Filter by status or settlement type (e.g., 'CONTRACT_SETTLED', 'OUTCOME_TRACKED', 'RESOLVED', 'PENDING', 'ALL')."
    },
    "settlement_type": {
      "type": "string",
      "enum": [
        "all",
        "contract_settled",
        "outcome_tracked",
        "unscored_archive"
      ],
      "description": "Filter by settlement tier: 'contract_settled', 'outcome_tracked', 'unscored_archive', or 'all'."
    },
    "min_divergence": {
      "type": "number",
      "description": "Minimum absolute divergence gap in percentage points (e.g. 20 for >= 20% gap)."
    },
    "category": {
      "type": "string",
      "description": "Optional filter by strategic intelligence domain."
    },
    "limit": {
      "type": "number",
      "description": "Maximum number of Alpha Gap records to return."
    }
  }
}

输出模式

{
  "type": "object",
  "properties": {
    "content": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string"
          },
          "text": {
            "type": "string",
            "description": "JSON representation of prediction market alpha gaps, model odds, Polymarket odds, and Brier calibration scores."
          }
        }
      }
    }
  }
}

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已验证未记录版本11 个工具