"Allowing an AI agent to ask user questions during a task" matching MCP servers:

Enables loading CSV text, filtering and aggregating datasets over Streamable HTTP, and optionally using Amazon Bedrock to turn query results into concise answers suitable for speech.

Exposes a governed dbt MetricFlow semantic layer to AI agents through three tools — list metrics, list dimensions and query metrics — so questions about federal contract spend are answered from defined metrics rather than raw tables. Guardrails reject undefined metrics, raw columns, SQL in filters and bulk exports, and no tool can execute SQL.

Enables AI agents to discover official public data sources, inspect their metadata and endpoint parameters, and execute live queries through a unified response envelope with a 500 ms budget.

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.

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.

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

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.

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

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.

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

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

Enables AI agents to import and refresh product feeds, edit products, write rules, run optimization and audits, connect ad channels, set performance labels, and create product ad creatives.

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

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.

Enables an AI assistant to query existing business systems through narrow typed tools, stage and confirm gated writes, and verify stated figures against source data with freshness checks.

基于Model Context Protocol (MCP) 的A股实时行情查询服务器，支持查询A股实时价格、历史K线数据、财务信息及市场概况。

Enables Alexa+ to answer conversational weather queries by retrieving current conditions, forecasts, alerts, and historical data from Open-Meteo, NWS, and simulated IoT sensors stored in SQLite.

Enables natural-language battery-cell requirements to be parsed and routed to simulation backends, returning design forms and performance metrics while supporting calibration, thermal-runaway, COMSOL, and UI draft workflows.

Enables AI agents to explore a synthetic product-event dataset and answer questions about signups, trends, funnels, and property breakdowns using a small set of composable, self-describing tools that return precomputed totals and actionable error hints.

Enables governed interaction with synthetic people data through MCP tools, providing per-caller scoped access, semantic-layer metrics, and read-only SQL with authorization checks.

MCP server for cleaning messy CSV data via a single clean_csv tool. Uses an agentic workflow with rule-based or LLM planners to transform data safely.

Enables LLMs to interact with any ERPNext instance through comprehensive CRUD operations, advanced permissions, and a web chat interface.

Transforms high-level business intents into structured Data Product Requirement Prompts through AI-powered conversational refinement. Guides users through clarifying questions to gather comprehensive requirements for automated Business Intelligence dashboard generation.

MCP server that enables one-click BIM audits by chaining IFC statistics, validation, and quantity takeoff workflows through natural language queries.

An autonomous MCP server that fetches datasets from IPFS/Filecoin, performs computation (anomaly detection, statistics, data quality scoring), and stores results via Multi-Chain Storage simulation.

Enables AI-assisted CSV data quality profiling through MCP tools, offering analysis reports and quality scores while preserving privacy by processing only aggregated data.

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.

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.