CalQuity MCP
CalQuity MCP connects Claude, ChatGPT, and compatible AI clients.
Hosted MCP endpoint · OAuth-enabled connection · Citation-first research
Your data · any format
Company research
Earnings intelligence
Research context
Sample outputs
Analyst configuration
Upload the examples that define how your team works — a research report, an Excel model, a chart pack, or a house style guide. CalQuity uses that reference when it creates the next artifact through your AI client.
01
Share a sample report, finished note, Excel model, or workbook your team already trusts.
02
Add preferences for structure, tone, tabs, formulas, citations, and review conventions.
03
Invoke CalQuity MCP in your AI client and ask for a new report, model, chart, or other deliverable.
Your configuration
Next time in your AI client
@CalQuity MCP
Build a peer comparison model for [Company] using the latest filings. Keep my uploaded workbook structure and return the finished Excel model with citations.
[Company]_peer_model.xlsx
Same tabs · same layout · refreshed analysis
Why CalQuity MCP
Generic AI
CalQuity MCP
Data traceability
A generic AI answer can be hard to audit: you may not know which source set informed it, whether it included the latest disclosure, or what date boundary was applied. CalQuity MCP makes source selection part of the research workflow.
Illustrative retrieval record
No PDF relay
CalQuity MCP connects your AI client to indexed company materials directly. Analysts can search, compare, cite, and analyze without downloading a PDF and uploading it to a separate chat.
Capabilities
CalQuity MCP is a research layer for AI agents — built to find, verify, structure, and deliver investment research.
01
Search quarterly results, earnings-call transcripts, annual reports, investor presentations, filings, and announcements in natural language, by keyword, or with exact-match search — without manually downloading and re-uploading PDFs.
02
Bring sector research, broker notes, thematic reports, charts, and indexed spreadsheet datasets into the same AI workflow.
03
Move beyond document search with metric resolution, historical time series, thematic tags, and narrative-evolution tracking.
04
Built for Indian listed-company research: NSE-style tickers, sector context, filings, earnings, financial periods, and analyst-grade source discipline.
05
Curated research workflows guide AI agents through professional deliverables instead of letting them improvise from generic prompts.
06
Open a secure workspace where the AI can write and run Python, query spreadsheets, generate models and charts, create files, inspect outputs, and return downloadable artifacts in your configured format.
How it works
01
Add CalQuity MCP to an OAuth-capable AI client such as Claude or Codex.
02
Upload a reference report, model, or house style so the workflow knows how your team expects deliverables to look.
03
Invoke CalQuity MCP and describe the research or artifact you need in the AI client you already use.
04
Receive a cited answer or downloadable artifact with your configured structure, format, and review conventions.
Example prompts
A question in. Evidence-backed research outputs out.
“Summarize the quarter for [Company] and identify the three most material changes versus the prior quarter. Cite the supporting pages.”
“Track how management’s commentary on asset quality has changed over the last six earnings calls.”
“Create a peer comparison for [Company] versus its sector, focusing on growth, margins, and operating KPIs.”
“Find all disclosures mentioning capacity expansion across this sector’s latest quarterly results.”
Integration & security
Connect in the way your environment requires, with clear controls around access, tokens, and workspace activity.
CalQuity MCP
Connect your AI workflow to CalQuity’s research infrastructure and keep evidence close to every important claim.
Connect CalQuity MCP