{"id":258029,"recorded_at":"2026-10-26T15:51:38.690594Z","observed_at":"2026-10-26T15:51:38.690534Z","subject_kind":"endpoint","subject":"1e3a6b92-a92f-4c17-9c81-2a7cdfa0b9e3","kind":"mcp.tools","method":"probe.mcp/1","body":{"count":12,"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"},{"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"},{"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"},{"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"},{"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"},{"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"},{"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"},{"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"},{"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"},{"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"},{"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"},{"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"}]},"body_sha256":"1d04258b4cf9f5d2050dd3310c01bf9f438880d823f544408bcefdf951f6b611","prev_id":null,"leaf":"0xb85d9795823aa8c3f466957fe8eec2378ee7ab253bec8ef39c18cb20554e3e9d","leaf_text":"{\"body\":\"1d04258b4cf9f5d2050dd3310c01bf9f438880d823f544408bcefdf951f6b611\",\"id\":258029,\"kind\":\"mcp.tools\",\"method\":\"probe.mcp/1\",\"observed_us\":1793029898690534,\"prev\":null,\"subject\":\"1e3a6b92-a92f-4c17-9c81-2a7cdfa0b9e3\",\"subject_kind\":\"endpoint\"}","day":{"day":"2026-10-26","leaves":296695,"root":"0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8","chained":"0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a","anchor_chain":"eip155:5042002","anchor_tx":"0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae","anchor_block":69251483},"proof":["0xb85d83bb30d587a9f66d1174dcd93b6555cf632226354d07e78a4a6acdf04a7b","0x5d303bbe7aeee81be0910bcecd2c8464a8f057dba3f5478cc25cacf400cea14a","0x9497e8979fab1ef606de6c451221f8df808b72d5bcda6ba668e1f7cd8a1ea218","0xa2de7750f0915ac263b81de4204017ca081fd838f8ca37450732acfe566b3ba2","0x7b96433b6f9e1f4d195409de11aced7df695075c897e9caccf8d9f40f81574b4","0xa7f63213f7e9860b403974b5ee3d176ac079e200b56fc91863b3d6ae9d884658","0xe283ece512eff8d8fd29e8d9cf0f7113136c1b65aea932b57f8ec3d739d29f36","0x671c47ce606be10270a5033f214eee726cc33a9fd7505667f7ee3417eb5a4e62","0xc9bd1b155c05de30e363c07bb71e6d23e64c096985805036b2061a5c0cb9cbf8","0x305a3f47b3689c4a43df7d5c5f208a0395fe7436d6b4555db7943094d45d61a1","0x930671b38b693880d085315beda88a1c227f3639e9a0e0be6b0665bb173a65db","0xb6efd79902cac6d5a402ebecb8069402f00961ab80cd71b4d647fbc686d975ec","0x2f43fe2b2533a5f40c107c114e458e226102f5f8ae91f097e964345880e30928","0xf692ce2b0a640ec9f75aa960e5fb1c2c7091a04e24881798735752c19014e519","0x9f68b5e15459cb62b8c0cd4c124fa61c74147b4a5755a200fbecd21a581635d1","0x123ec8bee20ddc55223eeef5a4f4d6c4b6416515adbb911c56759f5cde56e452","0xd0651cb9bc8155eb52b5247d506e74b8a935057dc190ba52091702ac25ed3a40","0x9a9024edb5069d88a324facb310a5ce8e495214bbaa968f077bede437390194f","0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a"]}