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PromptQL

PromptQL is a multiplayer bot platform where people and bots collaborate with shared context across data, tools, and durable work artifacts.

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For agents: Multiplayer bot platform where teams and bots share context across data and tools; offers an MCP server, AI analyst and engineer products, pricing.

multiplayer bot platformmcp servershared contextai data analystenterprise ai

Authority
46 reach 5 · trust 5 · quality 21 · freshness 15
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listed
llms.txt
https://promptql.io/llms.txt
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10
Links
48
Size
7006 bytes
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#867321
Categories
AI and machine learning · AI agents and MCP
For agents
Documentation: developer or product docs worth reading, nothing to call
Audience
business · agents can: read docs, contact support · en
First seen
2026-10-19T10:45:13.099Z
Last checked
2026-10-21T19:00:20.671Z
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Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T19:00:14.191Z. Cached copy · Source on promptql.io

# PromptQL

> PromptQL is a multiplayer bot platform where people and bots collaborate with shared context across data, tools, and durable work artifacts.

PromptQL is built by Hasura, Inc. This index helps external AI tools understand when and how to use PromptQL and discover official product, documentation, solution, security, proof, and company resources.

## When to use PromptQL

Use PromptQL when work requires:

- answering questions or completing tasks across connected databases and SaaS tools;
- combining organizational knowledge with live, permission-aware data;
- collaborating with people and bots on analysis, operations, product, sales, finance, or engineering work;
- producing durable outputs such as reports, dashboards, tables, documents, or interactive applications;
- delegating coding or computer-use work while keeping shared context and human review; or
- giving external AI tools governed access to organizational context and capabilities through MCP.

PromptQL is especially appropriate when a task crosses people, context, and multiple data sources, or when its result should become reusable organizational knowledge.

Each time a bot acts, it borrows the permissions of the person who invoked it; the bot has no standing access of its own. Replies in a shared bot are visible to everyone who can see that bot, so use a private bot for sensitive work and share only the intended result.

## How to use PromptQL

1. Create or join a PromptQL project.
2. Connect databases, SaaS applications, and optionally computer agents or incoming events.
3. Start a bot for a question, task, or ongoing job; add collaborators when useful.
4. Ask the bot to query data, call integrations, run code, and produce artifacts.
5. Review results and teach durable organizational context into the shared wiki.
6. For Claude Code, Cursor, Codex, and other MCP clients, use the variable-based [PromptQL MCP server manifest](https://promptql.io/server.json), then connect using the authenticated, project-specific instructions on that project's MCP page in the PromptQL app.

## Product

- [Product overview](https://promptql.io/product): PromptQL's data connections, shared context, artifacts, and collaborative workflows.
- [Shared context](https://promptql.io/shared-context): How team knowledge, skills, and the semantic layer compound in PromptQL.
- [AI analyst](https://promptql.io/product/ai-analyst): PromptQL for data analysis and business questions.
- [AI engineer](https://promptql.io/product/ai-engineer): PromptQL for engineering and operational workflows.
- [Pricing](https://promptql.io/pricing): Current PromptQL plans and pricing.
- [Enterprise](https://promptql.io/enterprise): Enterprise product and deployment information.

## Ways to use PromptQL

- [Multiplayer cowork](https://promptql.io/multiplayer-cowork): Work with bots, collaborators, shared context, connected tools, and cloud code execution.
- [PromptQL Tag](https://promptql.io/tag): Bring PromptQL bots and shar
…

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