"How to extract and analyze data from a MongoDB database" matching MCP servers:

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 interaction with MongoDB databases through MCP, supporting database queries, resource reading, and multi-collection operations including aggregation pipelines and lookups.

Enables an AI assistant to reliably read and chunk PDF/text documents, validate extracted JSON against a schema with full error paths, and save structured output—all confined to a single allowed directory.

Provides deterministic, schema-driven structured data extraction from PDF documents via regex/heuristic matching, enabling repeatable, inspectable, and cost-free field extraction without LLM API calls.

Loads CSV files into a temporary SQLite database and provides comprehensive data analysis tools via MCP, enabling AI assistants to query, analyze, and export data using natural language.

Enables agents to deterministically extract structured entities from input payloads and validate or coerce them against JSON schemas, returning a standardized response dossier with execution telemetry. It runs as a zero-dependency MCP server that plugs into Claude Desktop, Cursor, and other MCP clients.

MCP server providing a single query tool to execute SQL against GetDot Database, enabling schema discovery, data loading, and querying via natural language.

Enables deterministic extraction and validation of structured JSON data from inputs using a defined schema. It plugs into MCP-compatible clients like Claude Desktop, Cursor, and Windsurf to return standardized, schema-validated results.

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

Enables LLMs to profile and analyze tabular data files (CSV, Parquet, Excel, JSON) by extracting schema, statistics, data quality issues, and dtype suggestions, returning structured JSON.

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.

MCP server that unifies real-time telemetry from industrial systems into a single queryable interface, enabling production visibility, anomaly detection, and operational insights.

An MCP server for dataset exploration and analysis, enabling LLM clients to perform summary, correlation, distribution, missing value analysis, data cleaning, and statistical tests directly on CSV files.

Bootstrap MCP server for future data exploration and querying across multiple databases, currently only provides a hello_world tool with no actual data connectivity.

A Model Context Protocol server for data wrangling that provides standardized interfaces for data preprocessing, transformation, and analysis tasks including data aggregation and descriptive statistics.

Enables AI assistants to query and interact with Treasure Data through a secure interface, supporting SQL queries, database exploration, CDP segment management, and workflow monitoring across multiple regions.

Provides a read-only MCP interface to locally synchronized Amazon seller data, enabling AI agents and analytics tools to query order, inventory, advertising, and settlement information with completeness and freshness checks for safe analysis.

A production-ready MCP server that provides comprehensive dbt project quality assessment for any GitHub repository, enabling AI agents to analyze dbt models, check metadata coverage, and map data lineage.

A governed MCP server enabling LLM agents to query BigQuery, inspect GCS, trigger Airflow DAGs, and run data-quality checks with built-in security guardrails like allow-lists, cost ceilings, and audit trails.

Lets MCP clients safely investigate live business data across multiple enterprise APIs by giving each subject a consistent scoped pseudonym and surfacing disagreements between sources as findings.