"Answer" matching MCP servers:

A Model Context Protocol server that lets LLM clients answer business questions in natural language over a Databricks dataset without writing SQL by hand.

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 read-only intelligence over a Linear workspace by serving snapshot and change-history data stored in Cloudflare D1, so clients can answer questions about current work, project and milestone progress, member activity, and weekly reports without modifying Linear or invoking an LLM.

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 local read-only search and retrieval of approved, current evidence via hybrid lexical and dense methods, with tools to get exact source spans, answer from cited passages, and create and verify recheckable evidence packets.

Local 395M decision model as an MCP server: pick one option from a list or answer yes/no about text, or run a recipe over a CSV, XLSX or folder. Runs offline. Apache-2.0.

Read-only MCP server exposing Android phone timeline data, including events, hourly/daily features, data coverage, and gaps, so Claude can answer questions about observed phone activity.

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 employees to ask natural-language questions about a client's dbt semantic layer through the chatbot they already use, asking for clarification when needed, refusing when data can't answer, and logging every interaction so the semantic layer improves over time.

Enables Claude to answer plain-English metric questions by selecting validated metrics, dimensions, filters, and dates from a MetricForge semantic layer, ensuring answers use only controlled definitions and never model-written SQL.

Enables MCP-compatible agents to run the complete document extraction loop as remote tools: upload files, submit extraction jobs with plain-language instructions, answer clarifying questions, inspect review-needed rows and failed pages, and download results as spreadsheets or JSON.

Gives AI agents read-only access to curated, deduplicated, and well-described data products, complete with source metadata, freshness tracking, and per-agent permissions that preserve the source's access rights. The server can also answer "I don't know" rather than guessing when the approved data doesn't cover a question.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

Self-hosted MCP servers that let Claude, ChatGPT, or any MCP client answer questions about your own Google Maps data — where you were at a given moment, visits to specific places, dwell time, trips, distance traveled by mode, and your current position from a live feed. All tools are read-only and gated behind OAuth 2.1 tokens with configurable precision, so credentials and coordinates never leave your server.

Enables LLM agents to answer questions about Austrian passenger car registrations from an SQLite database, with tools for market overview, brand trends, brand rankings, and top models.

Enables natural-language querying of governed, certified People Analytics KPIs over heterogeneous HR data while enforcing data quality, mapping, lineage, and exception rules, including knowing when not to answer.

Exposes nine governed analytics tools — attrition, headcount, tenure, salary, promotion, and more — plus a restricted read-only SQL execution path, so an HR analyst's plain-English question resolves to a versioned, reproducible method instead of free-form LLM-written SQL. Tool selection is driven by the LLM, while role-based access control, PII screening, SQL guards, result validation, and audit logging keep every answer within policy.

Syncs bank data from Pluggy to Postgres and provides MCP tools to answer natural-language questions about spending, budgets, transactions, and categorization rules.

Enables an AI assistant to answer customer-success questions by synthesizing simulated billing, support ticket, and product usage data into composite churn-risk scores, bands, and plain-language explanations for fictional accounts. It exposes raw-data tools, a synthesis tool, and a methodology resource so users can list at-risk accounts or get a per-account health summary.

Exposes a self-hosted SQLite master of a user's complete Last.fm listening history over Streamable HTTP with OAuth 2.1, so ChatGPT or Claude can answer questions about any date range without hitting the Last.fm API. Provides 32 tools covering recent plays, period rankings, range comparisons, artist listening intensity, session segmentation, and shifts in taste over time.