indx/docs

Build with an AI Agent

The fastest way to integrate Indx in 2026 is often not to read these docs yourself — it is to hand them to your coding agent. Indx ships three pieces that make agent-driven integration work end to end: an agent skill that teaches your agent how to build with Indx, agent-readable docs for everything else, and a built-in MCP server so the agent can search real data and verify its own work.

Point your agent at the right piece below, then ask it to integrate Indx into your project.

1. Install the Indx skill (Claude Code)

The indx-search skill packages what an agent needs to build correct Indx integrations: version detection (v4 vs v5), the C# and HTTP API surfaces, field configuration, filters, boosts, coverage tuning, and the mistakes to avoid.

Install from skills.sh, or manually:

bash
# from your project root — the skill loads automatically when Indx work comes up
git clone https://github.com/indxSearch/skill-indx-search.git .claude/skills/indx-search

(User-wide instead: clone into ~/.claude/skills/ to have it in every project.)

With the skill in place, prompts like these produce working integrations:

  • "Add Indx search to this project. Products are in products.json; name and brand should be searchable, category and price filterable."
  • "Set up an Indx dataset for this data over the REST API, with facets on genre."

2. Give any other agent the docs

For agents without skill support (Cursor, Copilot, custom frameworks), this entire documentation site is published as one agent-readable file:

https://docs.indx.co/llms-full.txt

Paste the URL into your agent's context, or reference it from your project's agent instructions (AGENTS.md, .cursorrules, or similar).

3. Connect the agent to a running server (MCP)

Every Indx ships an MCP server at /mcp. Connected, your agent does not just write the integration — it searches your live datasets to verify what it built: check that the field configuration behaves, that filters use real values, that a typo still finds the right document.

Create an API key in the console (Account → API keys) with the access level Read only, limited to the team and datasets the agent should see — it can then search, fetch documents and describe datasets, but not change anything. (Search only also works, without describe_dataset and get_synonyms.) Then:

bash
# Claude Code
claude mcp add --transport http indx https://<your-host>/mcp \
  --header "Authorization: Bearer <your-api-key>"

Or in any MCP client's JSON configuration:

json
{
  "mcpServers": {
    "indx": {
      "type": "http",
      "url": "https://<your-host>/mcp",
      "headers": { "Authorization": "Bearer <your-api-key>" }
    }
  }
}

The server is read-only — agents can search, never mutate — and exposes five tools: list_datasets, describe_dataset (fields, capabilities, and value hints, so agents filter with real values instead of guessing), search, get_document, and get_synonyms. Full tool reference: Using Indx for AI Agents.

A workflow that uses all three

  1. Install the skill (or feed the docs URL) — the agent knows how to build.
  2. Ask it to integrate Indx with your data — field config, load, index, queries.
  3. Connect MCP — the agent searches what it built and corrects itself against real results.

That last step is the difference between generated code that looks right and an integration verified against a running engine.

Why Indx suits agents

Beyond integration-time help, Indx is a good runtime fit for agent systems themselves: embedded via NuGet it runs in the agent's own process with sub-millisecond queries — no network round-trip, no provisioning, no cold starts. See Using Indx for AI Agents for the full picture, including boost rules over MCP.