{"id":"https://openalex.org/W7140202376","doi":"https://doi.org/10.48550/arxiv.2603.20316","title":"Bypassing Document Ingestion: An MCP Approach to Financial Q&A","display_name":"Bypassing Document Ingestion: An MCP Approach to Financial Q&A","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7140202376","doi":"https://doi.org/10.48550/arxiv.2603.20316"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20316","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20316","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.20316","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041035111","display_name":"Sasan Mansouri","orcid":"https://orcid.org/0000-0003-0168-7580"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mansouri, Sasan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118860990","display_name":"Edoardo Pilla","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pilla, Edoardo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082032051","display_name":"Mark Wahrenburg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wahrenburg, Mark","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112669930","display_name":"Fabian Woebbeking","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Woebbeking, Fabian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.6086999773979187,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.6086999773979187,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13274","display_name":"Expert finding and Q&A systems","score":0.042899999767541885,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.041600000113248825,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6589999794960022},{"id":"https://openalex.org/keywords/vendor","display_name":"Vendor","score":0.5853000283241272},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.5031999945640564},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.41670000553131104},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.3882000148296356},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.37700000405311584}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.705299973487854},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6589999794960022},{"id":"https://openalex.org/C2777338717","wikidata":"https://www.wikidata.org/wiki/Q1762621","display_name":"Vendor","level":2,"score":0.5853000283241272},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.5031999945640564},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.3882000148296356},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.34709998965263367},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33739998936653137},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.32260000705718994},{"id":"https://openalex.org/C47487241","wikidata":"https://www.wikidata.org/wiki/Q5227230","display_name":"Data access","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.25929999351501465},{"id":"https://openalex.org/C3019144022","wikidata":"https://www.wikidata.org/wiki/Q4124998","display_name":"Questions and answers","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C23925645","wikidata":"https://www.wikidata.org/wiki/Q5449731","display_name":"Financial modeling","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20316","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20316","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.20316","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20316","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Answering":[0],"financial":[1,125,163,171],"questions":[2,110,140],"is":[3,19,114,152],"often":[4],"treated":[5],"as":[6,84,138],"an":[7],"information":[8,18],"retrieval":[9],"problem.":[10],"In":[11],"practice,":[12],"however,":[13],"much":[14],"of":[15],"the":[16,90,99],"relevant":[17,112],"already":[20],"available":[21],"in":[22],"curated":[23,150],"vendor":[24],"systems,":[25],"especially":[26],"for":[27,123,139,161,169],"quantitative":[28,162],"analysis.":[29],"We":[30,71],"study":[31],"whether,":[32],"and":[33,68,86,129,155],"under":[34],"which":[35],"conditions,":[36],"Model":[37],"Context":[38],"Protocol":[39],"(MCP)":[40],"offers":[41],"a":[42,76,121,153,167],"more":[43],"reliable":[44],"alternative":[45,157],"to":[46,57,104,149,158],"standard":[47],"retrieval-augmented":[48],"generation":[49],"(RAG)":[50],"by":[51,74],"allowing":[52],"large":[53],"language":[54],"models":[55],"(LLMs)":[56],"interact":[58],"directly":[59],"with":[60],"data":[61,151],"rather":[62],"than":[63],"relying":[64],"on":[65,89,98,107,131],"document":[66],"ingestion":[67],"chunk":[69],"retrieval.":[70],"test":[72],"this":[73,133],"building":[75],"custom":[77],"MCP":[78],"server":[79],"that":[80],"exposes":[81],"LSEG":[82],"APIs":[83],"tools":[85],"evaluating":[87],"it":[88],"FinDER":[91],"benchmark.":[92],"The":[93,116],"approach":[94,134],"performs":[95],"particularly":[96],"well":[97],"Financials":[100],"subset,":[101],"achieving":[102],"up":[103],"80.4%":[105],"accuracy":[106],"multi-step":[108],"numerical":[109],"when":[111],"context":[113],"retrieved.":[115],"paper":[117],"thus":[118],"provides":[119],"both":[120],"baseline":[122],"MCP-based":[124],"question":[126],"answering":[127],"(QA)":[128],"evidence":[130],"where":[132],"breaks":[135],"down,":[136],"such":[137],"requiring":[141],"qualitative":[142],"or":[143],"document-specific":[144],"context.":[145],"Overall,":[146],"direct":[147],"access":[148],"lightweight":[154],"effective":[156],"document-centric":[159],"RAG":[160],"QA,":[164],"but":[165],"not":[166],"substitute":[168],"all":[170],"QA":[172],"tasks.":[173]},"counts_by_year":[],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2026-03-25T00:00:00"}