Server definition

Hash: sha256:1a464a4b2a158a07c2916a42e855c04811a9477327cbeec79cbc4cc71561c9bd
What it is: What a remote MCP server returned when asked what it offers: 16 tools

The blob, as servednamed by its sha256

{
  "instructions": "Deterministic cs.AI/ML/NLP/CV/Robotics research intelligence. 615,443 papers · 647,786 author identities · 12,079,727 reference records · 2,527,414 resolved ArXiv citation links · 148,555 linked repositories. All data sourced directly from ArXiv API and HTML — no hallucination, no inference. Covers cs.AI, cs.LG, cs.CL, cs.CV, cs.RO. Available categories for filtering: cs.AI, cs.LG, cs.CL, cs.CV, cs.RO. Entity types: benchmark, model, method, dataset. Research tracks are stable addressable objects projected from resolved entity relations.",
  "tools": [
    {
      "description": "\n    All papers by a researcher, with their position on each paper.\n\n    Uses fuzzy name matching (LIKE) to handle name variations.\n    Returns papers sorted newest first.\n\n    Args:\n        author_name: Researcher name, e.g. 'Yann LeCun', 'lecun' (partial match works)\n        limit:       Max results (default: 20, max: 50)\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "author_name": {
            "title": "Author Name",
            "type": "string"
          },
          "limit": {
            "default": 20,
            "title": "Limit",
            "type": "integer"
          }
        },
        "required": [
          "author_name"
        ],
        "title": "arxiv_author_papersArguments",
        "type": "object"
      },
      "name": "arxiv_author_papers",
      "outputSchema": null
    },
    {
      "description": "\n    Citation graph for a paper — who cites it, or what does it cite?\n\n    direction='cited_by': Papers in our database that cite this paper.\n    direction='citing':   Papers that this paper cites (its references).\n    depth=2: Expands one hop further (depth-2 neighbors). Hard cap: 200 total.\n\n    Args:\n        arxiv_id:  ArXiv paper ID, e.g. '2402.01234'\n        direction: 'cited_by' (inbound) or 'citing' (outbound, default: cited_by)\n        depth:     Graph depth: 1 or 2 (default: 1)\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "arxiv_id": {
            "title": "Arxiv Id",
            "type": "string"
          },
          "depth": {
            "default": 1,
            "title": "Depth",
            "type": "integer"
          },
          "direction": {
            "default": "cited_by",
            "title": "Direction",
            "type": "string"
          }
        },
        "required": [
          "arxiv_id"
        ],
        "title": "arxiv_citation_networkArguments",
        "type": "object"
      },
      "name": "arxiv_citation_network",
      "outputSchema": null
    },
    {
      "description": "\n    Papers that mention BOTH entity A and entity B.\n\n    Answers questions like:\n    - 'Which papers use both GPT-4 and RLHF?'\n    - 'Where do LoRA and MMLU appear together?'\n    - 'Papers combining RAG and Chain-of-Thought?'\n\n    The intersection reveals research that explicitly bridges two concepts.\n\n    Args:\n        entity_a:  First entity name, e.g. 'GPT-4', 'LoRA', 'MMLU'\n        entity_b:  Second entity name, e.g. 'RLHF', 'Chain-of-Thought'\n        date_from: ISO date filter\n        date_to:   ISO date filter\n        limit:     Max results (default: 20, max: 50)\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "date_from": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date From"
          },
          "date_to": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date To"
          },
          "entity_a": {
            "title": "Entity A",
            "type": "string"
          },
          "entity_b": {
            "title": "Entity B",
            "type": "string"
          },
          "limit": {
            "default": 20,
            "title": "Limit",
            "type": "integer"
          }
        },
        "required": [
          "entity_a",
          "entity_b"
        ],
        "title": "arxiv_co_occurrenceArguments",
        "type": "object"
      },
      "name": "arxiv_co_occurrence",
      "outputSchema": null
    },
    {
      "description": "\n    How often is an entity mentioned over time?\n\n    Shows the rise (or fall) of a benchmark, model, method, or dataset\n    across the research literature — per month, quarter, or year.\n\n    Example: 'LoRA' — watch it explode in 2023-2024.\n    Example: 'BERT' — watch it decline as LLMs dominate.\n\n    Args:\n        entity_name:  Entity to track, e.g. 'LoRA', 'MMLU', 'RAG', 'GPT-4'\n        granularity:  Time grouping: month (default), quarter, year\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "entity_name": {
            "title": "Entity Name",
            "type": "string"
          },
          "granularity": {
            "default": "month",
            "title": "Granularity",
            "type": "string"
          }
        },
        "required": [
          "entity_name"
        ],
        "title": "arxiv_entity_trendArguments",
        "type": "object"
      },
      "name": "arxiv_entity_trend",
      "outputSchema": null
    },
    {
      "description": "\n    Full paper object with all connected data.\n\n    Returns: paper metadata, author list with positions, matched entities,\n    references (up to 100), and linked GitHub repos.\n\n    Args:\n        arxiv_id: ArXiv ID, e.g. '2402.01234' or '2402.01234v2'\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "arxiv_id": {
            "title": "Arxiv Id",
            "type": "string"
          }
        },
        "required": [
          "arxiv_id"
        ],
        "title": "arxiv_get_paperArguments",
        "type": "object"
      },
      "name": "arxiv_get_paper",
      "outputSchema": null
    },
    {
      "description": "\n    Institution ranking by paper count.\n\n    Primary signal: author_affiliations extracted from ArXiv HTML.\n    Secondary signal (include_github_orgs=True): adds GitHub org counts\n    as a complementary signal. Many papers have no affiliation in HTML\n    but do have a GitHub org link — combining both gives a fuller picture.\n\n    Note: affiliation data is extracted from HTML and may be incomplete\n    (fetch completion is reported live by arxiv_pipeline_status; extraction quality is a separate signal).\n\n    Args:\n        date_from:           ISO date filter\n        date_to:             ISO date filter\n        include_github_orgs: Also show GitHub org ranking as second signal\n        limit:               Max results (default: 20, max: 50)\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "date_from": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date From"
          },
          "date_to": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date To"
          },
          "include_github_orgs": {
            "default": false,
            "title": "Include Github Orgs",
            "type": "boolean"
          },
          "limit": {
            "default": 20,
            "title": "Limit",
            "type": "integer"
          }
        },
        "title": "arxiv_institution_rankingArguments",
        "type": "object"
      },
      "name": "arxiv_institution_ranking",
      "outputSchema": null
    },
    {
      "description": "\n    Most cited papers — ranked by inbound citation count.\n\n    This answers the question every researcher, VC, and journalist asks first:\n    'What are the most influential papers in AI right now?'\n\n    Counts how many papers in our database cite each target paper.\n    Only papers with resolvable ArXiv IDs in their references are counted.\n\n    Args:\n        category:  Filter citing papers by category (optional)\n        date_from: Only count citations from papers published from this date\n        date_to:   Only count citations from papers published until this date\n        limit:     Max results (default: 20, max: 50)\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "category": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Category"
          },
          "date_from": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date From"
          },
          "date_to": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date To"
          },
          "limit": {
            "default": 20,
            "title": "Limit",
            "type": "integer"
          }
        },
        "title": "arxiv_most_citedArguments",
        "type": "object"
      },
      "name": "arxiv_most_cited",
      "outputSchema": null
    },
    {
      "description": "\n    Full system status — database counts, pipeline progress, frontier, quality.\n\n    Returns:\n    - Paper/author/entity/ref/repo counts\n    - Pipeline progress: html_fetched %, whitelist_matched %, llm_processed %\n    - Frontier: how far back the backfill has reached\n    - Quality report: last run timestamp and overall status\n    - Quality log: last 5 quality check runs from quality_log table\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          }
        },
        "title": "arxiv_pipeline_statusArguments",
        "type": "object"
      },
      "name": "arxiv_pipeline_status",
      "outputSchema": null
    },
    {
      "description": "\n    GitHub repository landscape — which orgs and repos produce research code?\n\n    Shows the open-source output of the research community.\n    'openai', 'google-deepmind', 'microsoft', 'huggingface' etc. ranked by\n    how many papers link to their repos.\n\n    org_filter='huggingface' shows all HuggingFace repos with papers.\n\n    Args:\n        org_filter: Filter to a specific GitHub org, e.g. 'openai', 'google-deepmind'\n        date_from:  Only papers published from this date\n        date_to:    Only papers published until this date\n        limit:      Max results per ranking (default: 20, max: 50)\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "date_from": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date From"
          },
          "date_to": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date To"
          },
          "limit": {
            "default": 20,
            "title": "Limit",
            "type": "integer"
          },
          "org_filter": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Org Filter"
          }
        },
        "title": "arxiv_repo_landscapeArguments",
        "type": "object"
      },
      "name": "arxiv_repo_landscape",
      "outputSchema": null
    },
    {
      "description": "\n    Full-text search over the live cs.AI/ML paper graph using FTS5.\n\n    Searches title AND abstract. Supports boolean operators:\n      AND, OR, NOT, phrase matching (\"exact phrase\"), prefix (term*).\n\n    Args:\n        query:         FTS5 search query. E.g. 'LoRA fine-tuning', '\"chain of thought\"', 'RLHF NOT PPO'\n        category:      Filter by primary category. Options: cs.AI, cs.LG, cs.CL, cs.CV, cs.RO\n        date_from:     ISO date filter, e.g. '2024-01-01'\n        date_to:       ISO date filter, e.g. '2025-12-31'\n        empirical_only: Only papers marked as empirical by LLM pass (if available)\n        has_code_only:  Only papers with code release (llm_has_code=1, if available)\n        limit:         Max results (default: 20, max: 50)\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "category": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Category"
          },
          "date_from": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date From"
          },
          "date_to": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date To"
          },
          "empirical_only": {
            "default": false,
            "title": "Empirical Only",
            "type": "boolean"
          },
          "has_code_only": {
            "default": false,
            "title": "Has Code Only",
            "type": "boolean"
          },
          "limit": {
            "default": 20,
            "title": "Limit",
            "type": "integer"
          },
          "query": {
            "title": "Query",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "title": "arxiv_search_papersArguments",
        "type": "object"
      },
      "name": "arxiv_search_papers",
      "outputSchema": null
    },
    {
      "description": "\n    Top researchers ranked by paper count, with role filter.\n\n    role='last_author' is the PI filter — finds lab directors and group leaders\n    who drive research agendas. In academic AI, the last author IS the boss.\n    role='first_author' finds the PhD students and postdocs doing the work.\n    role='any' counts all papers regardless of position.\n\n    Args:\n        role:      Author position filter: any (default), first_author, last_author\n        category:  Filter by primary category: cs.AI, cs.LG, cs.CL, cs.CV, cs.RO\n        date_from: ISO date filter, e.g. '2024-01-01'\n        date_to:   ISO date filter, e.g. '2025-12-31'\n        limit:     Max results (default: 20, max: 50)\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "category": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Category"
          },
          "date_from": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date From"
          },
          "date_to": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date To"
          },
          "limit": {
            "default": 20,
            "title": "Limit",
            "type": "integer"
          },
          "role": {
            "default": "any",
            "title": "Role",
            "type": "string"
          }
        },
        "title": "arxiv_top_authorsArguments",
        "type": "object"
      },
      "name": "arxiv_top_authors",
      "outputSchema": null
    },
    {
      "description": "\n    Entity ranking by mention count across all papers.\n\n    title_only=True is a powerful relevance filter:\n    a paper mentioning MMLU in the title IS about MMLU,\n    not just using it as one of many benchmarks.\n\n    Args:\n        type:       Filter by entity type: benchmark, model, method, dataset (optional, default: all)\n        date_from:  Only count mentions in papers published from this date\n        date_to:    Only count mentions in papers published until this date\n        title_only: Only count mentions where entity appears in the paper title\n        limit:      Max results (default: 20, max: 50)\n    ",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "date_from": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date From"
          },
          "date_to": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Date To"
          },
          "limit": {
            "default": 20,
            "title": "Limit",
            "type": "integer"
          },
          "title_only": {
            "default": false,
            "title": "Title Only",
            "type": "boolean"
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          "type": {
            "anyOf": [
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                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Type"
          }
        },
        "title": "arxiv_top_entitiesArguments",
        "type": "object"
      },
      "name": "arxiv_top_entities",
      "outputSchema": null
    },
    {
      "description": "Chronological papers inside one stable research track.\n\n    Args:\n        track: Track slug or exact name, e.g. 'ai-agents' or 'multi-agent-systems'.\n        date_from: Optional ISO date lower bound.\n        date_to: Optional ISO date upper bound.\n        has_code_only: Restrict to papers with confirmed code signal.\n        limit: Max results (default 20, max 50).\n    ",
      "inputSchema": {
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          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
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            "anyOf": [
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                "type": "string"
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            "default": null,
            "title": "Date From"
          },
          "date_to": {
            "anyOf": [
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                "type": "string"
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              {
                "type": "null"
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            ],
            "default": null,
            "title": "Date To"
          },
          "has_code_only": {
            "default": false,
            "title": "Has Code Only",
            "type": "boolean"
          },
          "limit": {
            "default": 20,
            "title": "Limit",
            "type": "integer"
          },
          "track": {
            "title": "Track",
            "type": "string"
          }
        },
        "required": [
          "track"
        ],
        "title": "arxiv_track_papersArguments",
        "type": "object"
      },
      "name": "arxiv_track_papers",
      "outputSchema": null
    },
    {
      "description": "Research volume for one stable track by month, quarter or year.",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          },
          "granularity": {
            "default": "month",
            "title": "Granularity",
            "type": "string"
          },
          "track": {
            "title": "Track",
            "type": "string"
          }
        },
        "required": [
          "track"
        ],
        "title": "arxiv_track_trendArguments",
        "type": "object"
      },
      "name": "arxiv_track_trend",
      "outputSchema": null
    },
    {
      "description": "List stable ArXiv research-track objects with definitions and live counts.",
      "inputSchema": {
        "properties": {
          "api_key": {
            "default": "",
            "description": "BrunoSan API Key — brunosan.de/intelligence/",
            "title": "Api Key",
            "type": "string"
          }
        },
        "title": "arxiv_tracksArguments",
        "type": "object"
      },
      "name": "arxiv_tracks",
      "outputSchema": null
    },
    {
      "description": "\n    Live cs.AI/ML/CL/CV/RO paper graph. FTS5 + resolved ArXiv citation links.\n    Use for: arxiv_search_papers(\"LoRA fine-tuning\", has_code_only=True)\n    → Related verticals worth connecting:\n      AI News (mcp.brunosan.de/mcp) — industry reaction to papers\n      Robotics (robotics.mcp.brunosan.de/mcp) — applied robotics papers (cs.RO)\n      Quantum (quantum.mcp.brunosan.de/mcp) — quant-ph research depth\n      Biotech (biotech.mcp.brunosan.de/mcp) — bio-ML and drug discovery papers\n    ",
      "inputSchema": {
        "properties": {},
        "title": "get_related_intelligenceArguments",
        "type": "object"
      },
      "name": "get_related_intelligence",
      "outputSchema": {
        "properties": {
          "result": {
            "title": "Result",
            "type": "string"
          }
        },
        "required": [
          "result"
        ],
        "title": "get_related_intelligenceOutput",
        "type": "object"
      }
    }
  ]
}

Verify it yourself

curl -s https://api.teppi.xyz/v1/evidence/sha256:1a464a4b2a158a07c2916a42e855c04811a9477327cbeec79cbc4cc71561c9bd | sha256sum