Server definition

Hash: sha256:2c6d553f494a0344e7f6850c54773bf128cd0e392d4cf7da6ab8a8f9bee489b2
What it is: What a remote MCP server returned when asked what it offers: 12 tools

The blob, as servednamed by its sha256

{
  "instructions": "Multi-engine scholarly research server. Use it to traverse from keyword -> paper -> author -> paper -> references, save interesting papers into an in-memory reading list, and export citations/abstracts/full-text corpora for downstream synthesis.",
  "tools": [
    {
      "description": "Read the best abstract available for a paper. Use with a DOI or with author_name + candidate_index + paper_index after author_papers. If you pass `engines`, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.",
      "inputSchema": {
        "properties": {
          "author_name": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Author Name"
          },
          "candidate_index": {
            "default": 1,
            "title": "Candidate Index",
            "type": "integer"
          },
          "doi": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Doi"
          },
          "engines": {
            "default": "",
            "title": "Engines",
            "type": "string"
          },
          "paper_index": {
            "default": 1,
            "title": "Paper Index",
            "type": "integer"
          }
        },
        "title": "scholarfetch_abstractArguments",
        "type": "object"
      },
      "name": "scholarfetch_abstract",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_abstractOutput",
        "type": "object"
      }
    },
    {
      "description": "Read full paper text when machine-readable content is recoverable. Use with a DOI or with author_name + candidate_index + paper_index. Uses Elsevier first, then open-access fallbacks such as Springer OA, Europe PMC, arXiv PDF, and generic PDF URLs when text is recoverable. If you pass `engines`, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.",
      "inputSchema": {
        "properties": {
          "author_name": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Author Name"
          },
          "candidate_index": {
            "default": 1,
            "title": "Candidate Index",
            "type": "integer"
          },
          "doi": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Doi"
          },
          "engines": {
            "default": "",
            "title": "Engines",
            "type": "string"
          },
          "paper_index": {
            "default": 1,
            "title": "Paper Index",
            "type": "integer"
          }
        },
        "title": "scholarfetch_article_textArguments",
        "type": "object"
      },
      "name": "scholarfetch_article_text",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_article_textOutput",
        "type": "object"
      }
    },
    {
      "description": "Disambiguate a human author name into ranked identity candidates. Use this before `scholarfetch_author_papers` when the name is ambiguous and you need a stable `candidate_index`. If you pass `engines`, it must include `openalex`.",
      "inputSchema": {
        "properties": {
          "engines": {
            "default": "",
            "title": "Engines",
            "type": "string"
          },
          "limit": {
            "default": 10,
            "title": "Limit",
            "type": "integer"
          },
          "name": {
            "title": "Name",
            "type": "string"
          }
        },
        "required": [
          "name"
        ],
        "title": "scholarfetch_author_candidatesArguments",
        "type": "object"
      },
      "name": "scholarfetch_author_candidates",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_author_candidatesOutput",
        "type": "object"
      }
    },
    {
      "description": "Expand one author into a deduplicated paper list. This is the main author->paper traversal tool and supports research filters. Use `author_id` when you already know the exact author, or `author_name` plus `candidate_index` after `scholarfetch_author_candidates`. Supported comma-separated `filters`: year>=YYYY, year<=YYYY, year=YYYY, has:abstract, has:doi, has:pdf, venue:<text>, title:<text>, doi:<text>. If you pass `engines`, it must include `openalex`.",
      "inputSchema": {
        "properties": {
          "author_id": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Author Id"
          },
          "author_name": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Author Name"
          },
          "candidate_index": {
            "default": 1,
            "title": "Candidate Index",
            "type": "integer"
          },
          "engines": {
            "default": "",
            "title": "Engines",
            "type": "string"
          },
          "filters": {
            "default": "",
            "title": "Filters",
            "type": "string"
          },
          "limit": {
            "default": 50,
            "title": "Limit",
            "type": "integer"
          }
        },
        "title": "scholarfetch_author_papersArguments",
        "type": "object"
      },
      "name": "scholarfetch_author_papers",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_author_papersOutput",
        "type": "object"
      }
    },
    {
      "description": "Enrich one known DOI with metadata, reading links, and full-text availability signals. If you pass `engines`, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.",
      "inputSchema": {
        "properties": {
          "doi": {
            "title": "Doi",
            "type": "string"
          },
          "engines": {
            "default": "",
            "title": "Engines",
            "type": "string"
          }
        },
        "required": [
          "doi"
        ],
        "title": "scholarfetch_doi_lookupArguments",
        "type": "object"
      },
      "name": "scholarfetch_doi_lookup",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_doi_lookupOutput",
        "type": "object"
      }
    },
    {
      "description": "Expand a paper into its references. Use with a DOI or with author_name + candidate_index + paper_index. This is the main edge-expansion tool for traversing the literature graph. If you pass `engines`, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.",
      "inputSchema": {
        "properties": {
          "author_name": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Author Name"
          },
          "candidate_index": {
            "default": 1,
            "title": "Candidate Index",
            "type": "integer"
          },
          "doi": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Doi"
          },
          "engines": {
            "default": "",
            "title": "Engines",
            "type": "string"
          },
          "paper_index": {
            "default": 1,
            "title": "Paper Index",
            "type": "integer"
          }
        },
        "title": "scholarfetch_referencesArguments",
        "type": "object"
      },
      "name": "scholarfetch_references",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_referencesOutput",
        "type": "object"
      }
    },
    {
      "description": "Add one paper to a named in-memory reading list on the MCP server. Best input is paper_json copied from another ScholarFetch tool result, but DOI, query+result_index, or author_name+candidate_index+paper_index also work. Reuse the same collection name across calls to keep one research session together.",
      "inputSchema": {
        "properties": {
          "author_name": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Author Name"
          },
          "candidate_index": {
            "default": 1,
            "title": "Candidate Index",
            "type": "integer"
          },
          "collection": {
            "default": "default",
            "title": "Collection",
            "type": "string"
          },
          "doi": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Doi"
          },
          "engines": {
            "default": "",
            "title": "Engines",
            "type": "string"
          },
          "paper_index": {
            "default": 1,
            "title": "Paper Index",
            "type": "integer"
          },
          "paper_json": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Paper Json"
          },
          "query": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Query"
          },
          "result_index": {
            "default": 1,
            "title": "Result Index",
            "type": "integer"
          }
        },
        "title": "scholarfetch_saved_addArguments",
        "type": "object"
      },
      "name": "scholarfetch_saved_add",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_saved_addOutput",
        "type": "object"
      }
    },
    {
      "description": "Clear all papers from a named in-memory reading list. Useful when restarting a research branch.",
      "inputSchema": {
        "properties": {
          "collection": {
            "default": "default",
            "title": "Collection",
            "type": "string"
          }
        },
        "title": "scholarfetch_saved_clearArguments",
        "type": "object"
      },
      "name": "scholarfetch_saved_clear",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_saved_clearOutput",
        "type": "object"
      }
    },
    {
      "description": "Export the current reading list as citations, abstracts, BibTeX, or an aggregated full-text corpus. Valid `format` values: citations, abstracts, bib, fulltext. Valid `style` values when `format=citations`: harvard, apa, ieee. Use `include_references=true` with `format=fulltext` when you want a richer downstream synthesis corpus.",
      "inputSchema": {
        "properties": {
          "collection": {
            "default": "default",
            "title": "Collection",
            "type": "string"
          },
          "engines": {
            "default": "",
            "title": "Engines",
            "type": "string"
          },
          "format": {
            "default": "citations",
            "title": "Format",
            "type": "string"
          },
          "include_references": {
            "default": false,
            "title": "Include References",
            "type": "boolean"
          },
          "style": {
            "default": "harvard",
            "title": "Style",
            "type": "string"
          }
        },
        "title": "scholarfetch_saved_exportArguments",
        "type": "object"
      },
      "name": "scholarfetch_saved_export",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_saved_exportOutput",
        "type": "object"
      }
    },
    {
      "description": "List all papers currently saved in a named in-memory reading list. Use this to inspect the working set before exporting or removing items.",
      "inputSchema": {
        "properties": {
          "collection": {
            "default": "default",
            "title": "Collection",
            "type": "string"
          }
        },
        "title": "scholarfetch_saved_listArguments",
        "type": "object"
      },
      "name": "scholarfetch_saved_list",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_saved_listOutput",
        "type": "object"
      }
    },
    {
      "description": "Remove one paper from a named in-memory reading list by DOI or exact title.",
      "inputSchema": {
        "properties": {
          "collection": {
            "default": "default",
            "title": "Collection",
            "type": "string"
          },
          "doi": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Doi"
          },
          "title": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Title"
          }
        },
        "title": "scholarfetch_saved_removeArguments",
        "type": "object"
      },
      "name": "scholarfetch_saved_remove",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_saved_removeOutput",
        "type": "object"
      }
    },
    {
      "description": "Start a research traversal from keywords, a DOI, or a person name. Returns deduplicated paper records that you can inspect, save, expand through references, or use as seeds for author exploration. If you pass `engines`, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.",
      "inputSchema": {
        "properties": {
          "engines": {
            "default": "",
            "title": "Engines",
            "type": "string"
          },
          "limit": {
            "default": 20,
            "title": "Limit",
            "type": "integer"
          },
          "query": {
            "title": "Query",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "title": "scholarfetch_searchArguments",
        "type": "object"
      },
      "name": "scholarfetch_search",
      "outputSchema": {
        "properties": {
          "result": {
            "additionalProperties": true,
            "title": "Result",
            "type": "object"
          }
        },
        "required": [
          "result"
        ],
        "title": "scholarfetch_searchOutput",
        "type": "object"
      }
    }
  ]
}

Verify it yourself

curl -s https://api.teppi.xyz/v1/evidence/sha256:2c6d553f494a0344e7f6850c54773bf128cd0e392d4cf7da6ab8a8f9bee489b2 | sha256sum