"Understanding Feedback Enhanced Concepts or Technologies" matching MCP servers:

Enables AI agents to securely connect to and query Microsoft SQL Server databases with read-only access, schema discovery, and relationship mapping. Features advanced security protections, health monitoring, and bulk operations for production environments.

Enables data-quality workflows by applying named, deterministic transformations to records and generating replayable lineage with content hashes, so users can verify that cleaned results follow exactly from raw inputs.

This server enables agents to identify users and organizations, inspect versioned datasets and model lineage, start training jobs, run and read evaluations, verify deployments, and execute typed workflows through authenticated MCP endpoints.

Connects AI clients to the Standard Metrics API for automated analysis of venture capital portfolio data. It enables users to query financial metrics, track company performance, and generate comprehensive reports using natural language.

Market data + venue access layer — not an exchange: one book, one fee truth, one ledger of record.

The Polymarket adapter: the venue's prices, with their criteria and liquidity attached.

Enables MCP clients to search and analyze monthly Hacker News "Who is hiring?" threads by parsing free-form job comments into structured fields such as company, role, location, work mode, salary, visa, and technologies, with tools for listing threads, searching jobs, retrieving individual posts, and viewing hiring trends.

An MCP server that provides comprehensive market intelligence by analyzing data from both the Apple App Store and Google Play Store, enabling users to research apps, track market trends, study competitors, and understand user feedback across mobile marketplaces.

Enables structured document understanding of local PDFs via a dual-extract MCP server, combining MinerU text/layout and Qwen3-VL vision with fusion adjudication for field extraction, tables, formulas, and validation. Fully local and offline.

Enables AI agents to discover governed business concepts and request deterministic metrics with expanded formulas, rules, row filters, reference dates, and evidence instead of writing SQL against raw tables. It keeps personal data from agents and serves the same metric consistently across multiple databases.

Enables AI to analyze and transform data using fusion algorithms with statistical, machine learning, or hybrid methods. Provides seamless data format conversion and enhanced analytical capabilities through the Model Context Protocol.

Connects MCP clients to databases like Snowflake, BigQuery, and Databricks through Alkemi's data platform, enabling natural language database queries with proper schema understanding and metadata management for consistent team-wide access.

Enables AI assistants to read a verified household financial record across linked accounts, including holdings, transactions, fees, performance, cash flow, advisors, diagnostics, and score findings. It can also refresh connections and manage manually tracked assets or liabilities inside the service, but cannot move money or place trades at financial institutions.

Exposes 250+ financial data tools across 24 categories, letting AI assistants retrieve and analyze stock quotes, company fundamentals, financial statements, technical indicators, ETFs, crypto, forex, commodities, and economic data through the Financial Modeling Prep API. Supports dynamic or pre-loaded toolsets over HTTP/SSE, with hosted or self-hosted deployment.

Lets agents declare what data they need by concept plus entity (e.g. "price.close of Moutai"), then resolves those concepts to physical columns, ranks candidate sources, and fetches with failover. Over 70 tools also cover catalog and binding management, entity identity and graph, semantic and AI search, crawl policy and platform control, coverage reporting, schedules, authenticated-login pools, and governed observation reads.

Exposes a catalog of deterministic text tools to MCP clients, letting them extract emails, URLs, domains and IPs, clean and deduplicate lines, split or reorder columns, convert CSV to JSON, encode/decode, and format JSON, plus get suggestions for which tool fits a paste. An AI picks the tool, but code produces the exact, repeatable result with warnings for anything skipped or changed.

Enables AI agents to query Mixpanel event data, funnel conversion rates, and retention cohorts via MCP tools or natural language.

Enables users and AI agents to reconcile CSV bank statements against invoices or bills by matching amounts and references, then sorting each line into matched, likely, review, or unmatched groups with reasons. It exposes read-only tools for matching files and inspecting detected CSV columns and row counts.

Enables agents to explore a project's captured events and properties, then run trend, funnel and retention analyses and create or list saved insights. Tools are model-oriented, returning trimmed, budgeted results and actionable errors, with the project resolved from the API key or bearer token rather than passed as an argument.

An MCP server that connects Claude to Google Sheets for enhanced brainstorming and structured data output, enabling seamless capture, organization, and manipulation of ideas directly within spreadsheets.

Eunomia MCP Server is an extension of the Eunomiaframework that connects Eunomia instruments with servers. It provides a simple way to orchestrate data governance policies (like PII detection or user access control) and seamlessly integrate them with external serve

Expose IBKR account data, portfolio positions, orders, executions, contracts, and market data snapshots via MCP to Claude or Codex.

Enables inspecting local PDFs and extracting tables into CSV, XLSX, or JSON with options for pages, merging, OCR, engine selection, headers, merged cells, regions, and columns.

Enables users to run deterministic rule checks against their own metrics or HTTP/CSV sources and pull back a short decision brief: only the items that crossed a threshold, each with the number that tripped it and suggested next steps. Users can also record accept/reject/edit feedback so subsequent briefs reflect their own playbook, all without API keys or a live LLM.

Provides deterministic tools for understanding, transforming, and verifying structured data via MCP, enabling rule inference from examples and verification of transformed records.

Executes SQL queries via ConnectorX and streams results to CSV or Parquet files, supporting multiple databases and optional token counting for CSV output.

Enables agents to list metrics, retrieve contracts, plan allowed queries, and have proposed charts or decompositions vetoed with explicit rule reasons before SQL or insights are produced.

MCP server enabling AI agents to request user feedback during tasks, reducing wasted API calls. Optimized for data sync workflows with tools for audience sync confirmation, DMP verification, and rollback confirmation.

Enables MCP clients to access A-share research data, run quantitative screening and Chan-theory/factor analysis, scan dragon leaders and sector rotation, and manage recommendation feedback with realistic entry-price validation.

Exposes an agentic AutoML pipeline as a callable tool so any MCP client can upload a CSV and natural language goal and receive a full ML analysis report with SHAP visualizations. Enables autonomous problem understanding, data cleaning, feature engineering, model training, and explainability through a single remote tool call.