"Answer" matching MCP servers:

Lets you inspect a Datadog organization's usage and answer cost questions in plain English, helping identify savings opportunities.

An MCP server that exposes a dbt project's run state as tools, enabling AI assistants to answer questions like 'is the warehouse healthy?' or 'what broke and why?' via plain English, using read-only access to dbt artifacts and warehouse data.

Lets an AI assistant answer performance questions from Tideways data by reading projects, metrics, issues and traces through the read-only REST API.

An MCP server that provides offline searchable Microsoft 365 roadmap data and live Azure status, enabling AI agents to answer questions about upcoming features, roadmap changes, and Azure incidents.

Enables querying OpenObserve logs—listing streams, inspecting schemas, and running SearchSQL time-windowed searches—so you can answer what actually happened in production. Optionally correlates those logs against a database through a verified read-only connection via DbSchema and DbQuery tools.

Enables LLMs to answer Berserk observability questions by calling verified KQL tools instead of hand-authoring queries, with role-based tool filtering and automated query discovery.

Exposes a Raspberry Pi homelab's systemd service health, disk usage, CPU temperature, and LAN game-server status as read-only MCP tools, letting MCP clients answer questions like "how's the homelab doing?" without SSHing in manually.

Enables AI agents to diagnose broken XAMPP/WAMP localhost stacks by inspecting real machine state — running processes, listening ports, HTTP probes, and Apache/MySQL error logs — to pinpoint port conflicts, startup crashes, unclean-shutdown damage, and zombie processes with plain-English explanations. All diagnostics are read-only, so agents can safely answer questions like why Apache won't start or why localhost refuses to connect instead of guessing.

Enables AI agents to ask what changed in a time window and get back a short, ranked answer across metrics, logs and traces, with the service where a failure started identified and its echoes in other services labelled as such. Read-only and deterministic, it scores changes against a baseline and trims results to a token budget, so agents avoid paging through raw queries and blaming the loudest symptom.

MCP server that wraps Cursor Enterprise API to enable AI agents to answer questions about team spending, usage, and model adoption.

Enables MCP-aware AI agents to query the local machine's health over stdio, retrieving CPU, memory, disk, and load-average metrics along with boot time and the top processes by CPU usage. It gives coding agents a structured, protocol-standard way to answer questions like whether it is safe to run a build or what is currently consuming resources.

Enables network engineers and AI agents to answer troubleshooting questions like "why can't A reach B?" by choosing typed tools and filling parameters, with the server rendering deterministic, pre-shaped queries across firewall, VNet flow, Cisco IOS and Cisco ISE logs in Azure Log Analytics. The agent never writes raw query language, keeping the blast radius bounded to a fixed, closed tool catalog that runs locally on synthetic data or against a real workspace.

Enables an AI assistant to answer plain-language questions about a TP-Link Omada network — why Wi-Fi drops, which switch port a device is on, which VLAN it uses, and whether radar or interference is at play — by reading switches, access points, SSIDs, clients, topology and logs through 59 tools. It stays read-only unless two separate gates are opened, and any change is dry-run first, then read back and diffed.

Enables Alexa+ to answer plain-English questions about a home network using live diagnostics such as ping latency, bandwidth, connected devices, and interface health, with optional local or AWS Bedrock LLM reasoning and safe service restart tools.

A local MCP server that lets Claude answer plain-language questions about your Anthropic usage and spend across the developer API platform and Claude Enterprise products.

Enables any MCP client to drive a retrieval-grounded multi-agent workflow over stdio JSON-RPC, with journalled effects so recorded runs replay byte-for-byte from their trace. It also lets an agent answer retrieval by calling a tool on another MCP server.