AI resources

This page is written for coding assistants (Claude Code, Codex, Cursor, or an agent of your own) that are asked to set up, configure, or explain Tares. Humans are welcome too.

Markdown endpoints

Every Tares docs page is available as plain Markdown.

Endpoint	What it is
`https://docs.glassflow.ai/tares/llms.txt`	The Tares index: every page with a one-line description, in reading order. Start here.
`https://docs.glassflow.ai/tares/llms-full.txt`	All Tares pages in one file. Large; use it only when one or two pages are not enough.
Any page URL + `.md`	The page as Markdown, for example `https://docs.glassflow.ai/tares/connectors/prometheus.md`. The docs root is `/tares.md`.
`https://docs.glassflow.ai/llms.txt`	The whole GlassFlow index, other products included. Not needed for Tares work.

Content negotiation with an Accept: text/markdown header is not supported; the server answers with HTML. Use the .md suffix.

Retrieval order

Fetch /tares/llms.txt.
Pick the one or two pages that match the task from the router below and fetch their .md URLs.
Fetch /tares/llms-full.txt only if the answer spans many pages.

Do not scrape the rendered HTML pages; the Markdown is the same content without the navigation.

Task router

The user asks	Read first	Then
What Tares is, what it is for	Introduction	Concepts
Install it and get it running	Quickstart	CLI reference
Add a source of type X	Connectors	The connector’s own page, for example Prometheus, OTLP, Docker logs, GitHub, Webhook
Connect Claude Code, Codex, Cursor or another client over MCP	Connecting agents	Quickstart, Connect an agent
Build an AI SRE	Build an AI SRE	Tares agents
Receive dispatches, write an agent of their own	Watch with triggers	The HTTP API in five minutes
Tares agents, findings, what an agent writes back	Tares agents	Concepts, Findings
Give a Tares agent external tools (MCP servers)	Tares agents, External tools	Connecting agents
Deploy on a server, Docker, TLS, auth	Deployment	Self-host on a server
The HTTP API	The HTTP API in five minutes	Connectors for `POST /api/sources` bodies
Ready-made setups	Use cases	The matching guide
Is it working, what next	`tares status` in the CLI reference	Quickstart

Agent instructions

Paste this into a system prompt or a project instruction file:


When the user asks about Tares (docs.glassflow.ai/tares):
- Answer from the docs. Fetch https://docs.glassflow.ai/tares/llms.txt first, then the one or
  two pages that match, as Markdown (append .md to the page URL). Cite the page URL you used.
- For POST /api/sources bodies, use the JSON shape and keys from that connector's page under
  /tares/connectors/. Never invent a connector config key; if the page does not list it, say so.
- Commands and flags come from /tares/cli.md and the quickstart. Do not invent flags.
- Secrets (GITHUB_TOKEN, ANTHROPIC_API_KEY) come from environment variables; never ask the user
  to paste one into the chat.
- When the user wants Tares installed or connected to this agent, use the tares skill:
  run `npx skills add glassflow/tares --skill tares`, then follow it. It installs Tares, starts
  it, adds a first source, connects this agent over MCP and shows one read.
- If the running instance disagrees with the docs, trust the instance (`tares status`,
  GET /health) and say that the docs may be behind.

Guardrails

Do not invent CLI flags, API routes, connector keys or MCP tool names. Everything real is in the pages above.
Treat llms.txt as a map, not as an answer.
Never place a credential in a command line, a config file you show, or a chat message.
Tares is read-only for agents by default. Do not describe write actions that are not on the Tares agents page.