"Agent File Control MCP Server" matching MCP servers:

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.

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.

Lets an agent query JSON files too large for its context: JSONPath with aggregation pipes, schema, field profiles, samples, identity-keyed diff, CSV and spreadsheet export, and checks that gate a file. Int64 IDs and decimals keep their exact digits, and zstd, Base64-Zstd and PostgreSQL bytea payloads open directly.

Enables checking lead rows, CSV files, or Apify datasets for duplicates, email syntax and risk signals, domain issues, missing fields, and stale dates, returning a per-row import, review, or reject verdict so only clean records reach a CRM or sequencer.

Parses NACHA/ACH files into structured JSON and summaries, performing structural and arithmetic validation with IAT support, enabling automated file inspection and validation through MCP clients.

Enables local MCP-assisted processing of Access database tables, HTML5 advertising ZIP image assets, layered PDFs, and ONIX book-update requests through RapidAPI. It saves results in a selected workspace folder while requiring explicit consent for each processing call.

Enables AI agents to discover and query enterprise knowledge documents and business metrics through standardized MCP tools, supporting retrieval, structured sales/order analysis, anomaly scanning, and data lineage lookups.

A tutorial implementation MCP server that enables analysis of CSV and Parquet files. Allows users to summarize data and query file information through natural language interactions.

Enables autonomous agents and scraping pipelines to deduplicate high-throughput event streams, repair and validate gigabyte-scale JSONL data, retry HTTP/S requests with exponential backoff and header rotation, mimic Chrome/Safari TLS fingerprints, and sync cross-domain cookie sessions. Tool invocations are metered through x402 USDC micropayments on Base L2, returning a payment challenge until a signed transaction unlocks execution.

Enables AI agents to search data sellers and datasets across categories, inspect sellers and data points, and request purchases, samples, or unlisted data without an API key.

An MCP server that exposes Estuary Flow pipeline operations as typed tools, enabling agents to inspect pipeline health, publish captures and materializations, edit/restart tasks, and read landed documents via tool calls.

Enables natural language interaction with JSON files, providing tools to load, query, aggregate, transform, and export data directly from AI editors.

An MCP server that gives AI assistants full control over Telegraf Controller to manage agent fleets, configurations, labels, and health rules using natural language.

Provides efficient handling of large Excel files through automatic chunking and pagination, using MCP to enable seamless file reading and management features such as sheet selection and error handling.

An MCP server that enables Claude to train a linear regression model by simply uploading a CSV file, handling the entire ML pipeline from data preprocessing to model evaluation.

Connects AI clients to the Matih data platform, enabling SQL queries, table profiling, analytics, chart creation, and file uploads through MCP tools with OAuth/PKCE authentication.

MCP server that exposes Parseur document parsing and mailbox management as tools for AI assistants, allowing natural language control of parseur.com.

Bring professional data to the AI you already use. QVeris connects AI assistants and workflows to services for company financials, social media, life sciences, research, patents, and e-commerce. Discover services, review scope and pricing, call them, and audit usage through one MCP connection. Requires a QVeris API key; charges depend on the selected service.

Enables AI assistants to look up FIRE data standard fields, validate draft records, and receive mapping suggestions when mapping internal data formats onto FIRE.

Enables AI applications to interact with decentralized storage through a standardized Model Context Protocol interface, allowing file uploads, retrievals, and identity management.

Provides deterministic tools for transforming, formatting, and inspecting structured data for AI agents.

Enables an AI agent to verify a single email address in real time, clean an entire uploaded list file with per-address verdicts, and download the results as CSV with the addresses that are safe to send to. It runs on npm with zero dependencies and includes built-in credit reporting and spending limits so agents cannot overspend without approval.

This read-only MCP Server allows you to connect to CSV Files data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for CSV Files (https://www.cdata.com/drivers/csv/download/mcp).

Enables AI assistants and editors to profile, clean, and analyze messy CSV/Excel files directly through MCP tools such as profile_dataset, clean_dataset, and list_sheets.

Enables agents to answer analytics questions with provable correctness by resolving metrics through the semantic layer, tracing lineage, traversing knowledge graphs, checking freshness, and searching glossary definitions, while enforcing access controls and logging every call.

Lets coding agents run governed SQL against a Databricks SQL warehouse and inspect Unity Catalog metadata over stdio or Streamable HTTP, with persistent operational logs stored in SQLite. Access is read-only by default and bounded by the configured identity's Unity Catalog permissions.

Turns natural language into data pipeline actions using six specialist agents that collaborate through MCP to build, validate, and monitor data infrastructure.

An MCP server that provides data bridging from Spring Boot interfaces and a lightweight Python sandbox for script execution. It enables agents to fetch data as Markdown or Parquet files and perform automated data analysis within a controlled environment.

Converts legacy supply chain databases and file drops into MCP servers for AI agents, enabling direct interaction with closed systems via CLI.

Provides an instant MongoDB MCP server that can be used from any AI agent without local installation.