"Comment perdre du poids (How to lose weight)" matching MCP servers:

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.

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.

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

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.

Enables Claude to list Iceberg tables and run read-only DuckDB SQL queries, including time travel by snapshot, with per-caller token authentication and audit logging.

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.

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

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.

Enables AI assistants to search multiple tech job boards at once, summarize what the market is asking for, and rank postings by how well they match a user's own skills.

A server that connects to the Health Planet API to fetch and provide weight measurement data through any MCP-compatible client, allowing for retrieval and analysis of personal weight records.

Enables analyzing sales and stock CSV files to identify what to reorder, how much to buy, when products will run out, and how much inventory is tied up in dead stock. Provides read-only inventory checks and CSV column inspection without uploading data.

Analyzes user-authorized CSV, JSON, or XLSX comment exports locally to generate risk signals, prioritized comments, evidence-backed actions, and reports while keeping data private.

Enables AI agents to reverse-engineer how an organization actually operates by ingesting documents, spreadsheets, chat exports, invoices and audio, then tracing every claim back to graded evidence. It supports process mining, stated-vs-enacted comparison, coverage and obligation tracking, and bilingual report generation while keeping unproven areas explicitly flagged as unknown.

Enables users to look up how specific v2 fields, datatypes, and table codes translate to R4, and to convert whole ORM, ADT, and ORU messages into transaction Bundles with explicit warnings for unmapped values.

Enables AI clients to ask quantitative questions about any continent's economy, answering with live reconciled GDP, population, per-capita, ranking, and historical FX conversion figures pulled from three public APIs at query time. Every judgment call—which countries count, how missing years are handled, which currency and FX date—is exposed as a parameter, and the server reports exactly which rows it excluded, why, and where each number came from.

Answers whether a route ran late and by how much, with drill-down into individual trip delays and an audit trail of data-quality exclusions and duplicate resolutions.

Tracks buying and momentum signals for companies, such as funding rounds, senior hires, office openings, customer wins, and partnerships, with each event carrying its date.

Enables agents to reconcile piping material take-offs against Plant 3D SCSA exports by previewing diffs and applying quantity corrections, insertions, and gated removals with dry-run and explicit confirmation flags.

Deterministic encode, decode, hash and convert for agents, on the same pipeline engine as the String Utility Belt web app, CLI and editor extensions: a pipeline built and checked in the browser runs unchanged from its share link. Base64/hex/URL, JWT, hashes, JSON/YAML/CSV/XML, dates and regex; tools to search, describe and run utilities or whole pipelines, and to detect input formats. Runs locally

Enables clients to interact with PipesHub data and functionality through MCP, including chat, search, record retrieval, directory browsing, sources, and agents.

Connects your BitScale workspace to Claude, allowing you to manage grids, fetch row data, and trigger enrichment runs via natural language. It provides tools for listing grids, checking run status, and retrieving workspace details.

Enables AI clients to securely connect to Volleymetrics and Hudl volleyball analytics for opponent scouting, rotation diagnostics, box scores, serve-receive audits, and DataVolley export.

Superhuman data-driven science. Allows agents to upload any tabular dataset, specify a target column, and get validated predictive patterns (with p-values, effect sizes, and context from literature) that surface feature interactions and subgroup effects you'd otherwise miss. Many discoveries already made, free for open data!

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

Enables users to perform deterministic time series forecasting through Claude by running reproducible Python statistical and foundation models, providing tools for loading data, cross-validation, forecasting, anomaly detection, and exporting re-runnable manifests without any LLM involvement.

An MCP server that enables AI coding assistants to interact with a local Airflow cluster via its REST API for triggering DAG runs, monitoring status, reading logs, and diagnosing errors.