"Using agents in VSCode to reference library documentation during programming" matching MCP servers:

Enables AI agents to inspect, query, test, and safely fix dbt projects and BigQuery warehouses under policy-enforced controls, with read-only direct queries and dbt-only allowlisted writes.

Enables ingestion of HL7 v2, ASTM, and JSON control results, Westgard multirule evaluation, automatic patient-result holds, explainable root-cause investigation, and supervisor-approved CAPA workflows for clinical laboratories. Agents can also establish targets, compute Levey-Jennings and sigma statistics, and verify corrective actions before a supervisor lifts a hold.

Enables AI agents to convert CSV text into JSON arrays with automatic delimiter detection and optional header-row handling, accessed through a pay-per-call x402 micropayment API.

Convert PDFs to structured JSON. Extract invoices, bank statements, contracts, and more. Pay per call via x402 USDC.

The Excel to JSON MCP provides a standardized interface for converting Excel and CSV data into JSON format using the Model Context Protocol.

The JSON to Excel MCP provides a standardized interface for converting (1)JSON data (2)URL pointing to JSON files into CSV format

Converts JSON data to TOON (Token-Oriented Object Notation) format and back, reducing token usage by 30-60% for more efficient LLM applications.

A stdio MCP server exposing a validate_emails tool that lets agents screen email lists without sending mail or probing SMTP, checking syntax, DNS/MX records, disposable domains, role accounts, free providers, typo suggestions, and SPF/DMARC. Each address comes back with a valid/risky/invalid/unknown verdict plus machine-readable reason codes so agents can clean leads and signups before outreach or import.

Enables agents to transform tabular Markdown content into structured, type-inferred records with normalized formatting through an MCP-compatible interface.

Enables AI agents to search projects, retrieve and validate CDOP documents, explain schemas, list enums, and inspect state machines over Streamable HTTP, returning HAL links so agents can continue navigating the API.

Data observability for AI agents. Query alerts, monitor freshness, investigate schema drift, and trace lineage across your data warehouse via 53 MCP tools.

MCP server for configuring the konsol Frappe app via the CLI. Enables AI clients to manage dimensions, measures, fact tables, connectors, and schemas using natural language.

Give your AI agent the entire China A-share market: 55 datasets of prices, financials, capital flows, corporate events and futures/options, stored locally and queryable with read-only SQL. Built point-in-time with delisted stocks kept, so backtests don't lie — no account, no API key.

An open-source MCP server that imports HR CSV data into an in-memory SQLite database for structured querying and metadata retrieval. It enables users to perform read-only SQL queries and structured searches on employee data through natural language.

An implementation that integrates with Odoo ERP systems, enabling AI assistants to interact with Odoo data and functionality through the Model Context Protocol.

Enables AI agents to rank US healthcare organizations for clinical trial site feasibility, predicting patient accrual for a Graves' disease protocol using claims and registry data.

Converts eFax documents (PDF, TIFF, CCD XML) from OpenText Fax Server Software into structured JSON format with OCR support, metadata extraction, and batch processing capabilities.

Enables AI agents to discover, request access to, and query data products in Data Mesh Manager, enforcing governance policies while retrieving business data from platforms like Snowflake and Databricks.

Extracts structured JSON data from unstructured text using predefined schemas for receipts, invoices, resumes, and emails. It allows users to transform messy text into organized data through built-in or custom-defined fields.

A portfolio analysis MCP server that enables AI agents to manage investment portfolios, fetch financial data from Yahoo Finance and CoinGecko, and perform advanced analysis like weight optimization and Monte Carlo simulations. It utilizes reference-based caching to efficiently handle large datasets without bloating the LLM's context window.