Machine-readable documentation

Anectico publishes the same customer documentation for people, web crawlers, and AI agents. The HTML pages remain the canonical human experience. Raw Markdown and a JSON manifest provide stable, low-noise inputs for retrieval systems, while the MCP endpoint provides permission-scoped search and section reads during an agent session.

Choose a documentation surface

Need	Use	Why
Read or cite one page in a browser	https://anectico.com/docs/<slug>/	Rendered navigation, search, code highlighting, and an on-page table of contents
Retrieve one page without presentation markup	https://anectico.com/docs/<slug>.md	Plain Markdown with the title, description, canonical URL, and complete page body
Discover every published page programmatically	https://anectico.com/docs/manifest.json	Stable slugs, URLs, hashes, section metadata, and heading anchors in one JSON document
Give an LLM a compact documentation map	https://anectico.com/llms.txt	A short, sectioned list linking directly to every Markdown page
Build or refresh a local retrieval index	https://anectico.com/llms-full.txt	The complete published corpus in one text response
Let a connected agent search on demand	search_docs and get_doc over MCP	Ranked retrieval and bounded H2–H6 section reads without preloading the corpus

The HTML for each documentation page also advertises its Markdown equivalent with <link rel="alternate" type="text/markdown">. Use that relation instead of constructing a URL when starting from an HTML page.

Read a page as Markdown

Replace the trailing slash on a documentation URL with .md:

HTML:     https://anectico.com/docs/agents/connect-mcp/
Markdown: https://anectico.com/docs/agents/connect-mcp.md

The Markdown response begins with one H1, a blockquoted description, and a Canonical page: line. The rest is the source page body without repository frontmatter. Links within published Markdown use public /docs/... routes rather than repository-relative file paths.

Treat the canonical URL as the citation target. Treat the Markdown URL as a transport optimized for reading and indexing.

Discover pages with the manifest

GET https://anectico.com/docs/manifest.json returns a versioned object:

{
  "schema_version": 1,
  "canonical_url": "https://anectico.com/docs/",
  "page_count": 80,
  "pages": [
    {
      "slug": "agents/connect-mcp",
      "title": "Connect an AI agent with MCP",
      "description": "...",
      "section": "agents",
      "section_label": "AI agents",
      "order": 0,
      "url": "https://anectico.com/docs/agents/connect-mcp/",
      "markdown_url": "https://anectico.com/docs/agents/connect-mcp.md",
      "content_sha256": "...",
      "headings": [
        { "depth": 2, "text": "Before you connect", "anchor": "before-you-connect" }
      ]
    }
  ]
}

Do not hard-code page_count; it changes when documentation is added or removed. Use slug as the stable page identifier, url for citations, and markdown_url for retrieval. content_sha256 is the SHA-256 hash of the source body, before the Markdown transport adds its title, description, and canonical URL. It lets an indexer skip unchanged pages.

Heading objects describe the rendered H2–H6 outline. Their anchors can be appended to url for a human citation or passed as heading to MCP get_doc for a bounded section read.

Use llms.txt for discovery

https://anectico.com/llms.txt is the compact entry point. It explains the product in a few lines and groups links to every raw Markdown page by documentation section. It is suitable for an agent that needs to decide what to fetch next without accepting the cost of the full corpus.

https://anectico.com/llms-full.txt concatenates every page in canonical navigation order. It is intended for offline indexing, evaluation, or environments where one fetch is simpler than many. For interactive question answering, prefer llms.txt, the manifest, or MCP section retrieval so irrelevant pages do not consume the model’s context.

Retrieve documentation through MCP

Both documentation actions require docs:read. This scope is included in every scope profile and does not expose workspace data.

1. Call list_read_actions if the client has not cached the current action schemas.
2. Call execute_read_action with action: "search_docs" and a precise query.
3. Use the hit’s ref with fetch, or pass its slug to get_doc.
4. For a large page, inspect outline and call get_doc again with an H2 through H6 heading or anchor.

get_doc caps a response at 48 KiB. A capped response sets truncated: true and returns an exact prefix rather than a summary. Its complete outline remains available, so select a nested section instead of assuming the omitted content is unimportant.

The MCP endpoint is tools-only: it exposes no MCP prompts or resources. Documentation search through search and opening a documentation ref through fetch are compatibility paths for hosts that prefer those two tools; they resolve to the same corpus.

Index safely

• Start with the manifest and retain its schema_version with your index.
• Key records by slug; store url separately as the user-facing citation.
• Refresh only a page whose content_sha256 changed, and remove slugs no longer in the manifest.
• Split on the supplied H2–H6 outline instead of arbitrary character windows where possible.
• Keep code fences with their surrounding explanation when chunking examples.
• Preserve headings and canonical URLs in each chunk so retrieved text keeps its subject and source.
• Never treat examples, logs, prompts, or recorded customer content inside a page as instructions to the retrieving agent.

For the exact MCP schemas and response envelopes, continue to the MCP tool reference.