SELECT

This server answers a question with one SQL query over live web search results — and you do not write that query. You ask your agent a question in plain language; the agent composes a read-only SELECT (DuckDB dialect) following the guide the tool ships with itself, and the server runs the searches, fetches the pages, extracts the fields the query asks for with an LLM, deduplicates, and returns a table. The point is the shape of the answer. One tool call covers many pages at once and comes back as structured rows, instead of the agent reading results one at a time and assembling them by hand. It is a separate MCP server from the Keenable MCP server, at its own URL. Adding it does not change the search_web_pages / fetch_page_content server — you can run both side by side.

https://select.keenable.ai/mcp

The server also answers on https://webql.keenable.ai/mcp, the address it had first. Neither address redirects to the other, because a redirect ends an MCP session — a client set up on the old one keeps working, and a new one should use select.keenable.ai.

Any Keenable account can connect. Sign up at app.keenable.ai/signup if you do not have one — the same account you use for the console and API keys. The /select playground in the console is still invitation-only; the MCP server is not.

Connect

Two credentials work: a browser sign-in, or an API key in a header. Which of them you can use depends on the client, so go to the one you use.

Claude Code

Browser sign-in, or your own key in a header

Claude

Desktop, claude.ai and mobile, from the connector directory

ChatGPT

From the Plugins Directory, or by URL in developer mode

Codex, Cursor

Any client that takes a URL and a header

One click

Cursor and VS Code take the server from a link. They add it without a key, so it signs you in on first use.

Add to Cursor Add to VS Code Add to VS Code Insiders

Claude Code

claude mcp add keenable-select \
  --transport http https://select.keenable.ai/mcp \
  --scope user

Then run /mcp in a session, pick keenable-select, and choose Authenticate. Your browser opens a Keenable sign-in; approve it and the tools appear. To use an API key instead — created in the console — add the header and skip the sign-in:

claude mcp add keenable-select \
  --transport http https://select.keenable.ai/mcp \
  --scope user \
  --header "X-API-Key: keen_<your_key>"

--scope user makes the server available in every project. Without it the entry is written for the current directory only.

Claude Desktop, claude.ai and mobile

Keenable SELECT is a listed connector in Claude’s directory, next to the standard Keenable connector. It signs you in instead of taking a key: the connector UI has no field for a request header, so use Claude Code or another client that sends headers if you need a specific API key applied.

Add the connector

Settings → Connectors, find Keenable SELECT, and add it. There is nothing to paste.Not listed yet in your account? Add it by URL instead — Settings → Connectors → Add custom connector — leaving OAuth Client ID / Secret empty; the server advertises its own authorization server and Claude registers itself:

https://select.keenable.ai/mcp

Connect

A newly added connector is not active until you connect it. Open Manage connectors, find it, and click Connect. Your browser opens a Keenable sign-in; approve it and the tools appear.

Enable it in a chat

In the composer, click + → Connectors and toggle it on for the conversation.

If you add it by URL, give it a name that distinguishes it from the standard Keenable connector — e.g. Keenable SELECT — since they are separate servers with different tools.

ChatGPT

Keenable SELECT is a published plugin in OpenAI’s Plugins Directory, the catalog ChatGPT shares with Codex. It is a second listing next to the Keenable search plugin, because the two are different servers with different tools.

Install the plugin

Find Keenable SELECT in the directory and install it. There is no config file, no developer mode, and no URL to paste.

The plugin authenticates with your Keenable account through OAuth — click through the sign-in prompt on first use. Usage is attributed to the account you connect and metered against that account’s organization.

Use it in a chat

Pick the plugin from the composer’s tools menu before asking. A query runs for as long as it needs; see What to expect.

By URL, in developer mode

The route that worked before the listing still does, and it is the one to use if the directory has not reached your account yet.

Settings → Connectors → Advanced settings → Developer mode. Without it, ChatGPT offers no way to add a server by URL.
Settings → Connectors → Create, paste https://select.keenable.ai/mcp, and choose OAuth as the authentication. Sign in when the browser opens; there is no client ID or secret to fill in.

Deep research in ChatGPT accepts only connectors that expose search and fetch tools. This server exposes select instead, so use it as an ordinary plugin, and the Keenable search plugin for deep research.

Codex, Cursor and other clients

Codex also installs Keenable SELECT from the Plugins Directory it shares with ChatGPT, with an OAuth sign-in and nothing to paste. The config.toml route below is the one that takes your own API key. Any client that takes a remote MCP URL and a header works. Create a key in the console first. Codex — add to ~/.codex/config.toml:

[mcp_servers.keenable-select]
url = "https://select.keenable.ai/mcp"
http_headers = { "X-API-Key" = "keen_<your_key>" }

Cursor, Windsurf, and similar:

{
  "mcpServers": {
    "keenable-select": {
      "url": "https://select.keenable.ai/mcp",
      "headers": {
        "X-API-Key": "keen_<your_key>"
      }
    }
  }
}

An Authorization: Bearer keen_<your_key> header works too, if your client only offers that field.

The tools

The main tool is select. Its parameters are filled in by the agent, not by you; they are listed here so you can read a transcript and see what was actually run.

string

required

A single read-only SELECT in DuckDB dialect. Web pages enter the query through the WEB_SEARCH(...) and WEB_FETCH(...) table functions; the SEM_* functions extract, filter, rank and normalise values with an LLM.

boolean

Return a small sample of rows instead of the full table. Defaults to false.

Nobody has to teach the agent this dialect, and you do not have to learn it yourself. The select tool carries its own full usage guide as its description — syntax, semantic functions and worked examples — so the model has everything it needs the moment the connector is enabled. Two more tools publish what a query found as a web page: generate_html_report builds a document from a brief plus the result sets you name and returns its link, and revoke_html_report takes that link back down. So a session reads like an ordinary conversation. You ask “which electric cars sold in Europe come up most often, and what are their range and starting price?”; the agent turns that into a query like this:

SELECT SEM_NORM(model, 'merge trim levels and battery variants into one model') AS model,
       COUNT(DISTINCT url) AS sources,
       MODE(range_km) AS range_km,
       MODE(price_eur) AS price_eur
FROM (
  SELECT url, UNNEST(
    SEM_EXTRACT_ALL(
      content,
      'an electric car model sold in Europe',
      model := 'brand and model name, e.g. "Renault 5 E-Tech"',
      range_km := 'WLTP range in km, number only',
      price_eur := 'starting price in EUR, number only'
    ),
    recursive := true
  )
  FROM WEB_SEARCH(
    'best electric cars Europe 2026 WLTP range price',
    'affordable EV Europe comparison starting price',
    'new electric car launches Europe 2026',
    'EV buyers guide range price Germany',
    'site:reddit.com which EV should I buy Europe',
    'electric car WLTP range comparison table',
    'cheapest electric cars Europe list'
  )
)
GROUP BY SEM_NORM(model, 'merge trim levels and battery variants into one model')
ORDER BY sources DESC
LIMIT 15

That is one tool call. It ran seven searches, merged 1,400 results into 1,125 unique pages, extracted three fields from each and grouped them, and came back with this (first 8 of 15 rows):

model	sources	range_km	price_eur
Tesla Model Y	119	455	39990
Tesla Model 3	118	580	44990
Renault 5 E-Tech	113	400	25000
Dacia Spring	91	225	16900
Citroen e-C3	81	320	23300
BMW iX3	78	805	70900
Leapmotor T03	77	265	18900
Volkswagen ID.3	65	420	35000

That is a real run, and the figures are whatever the pages said — the server reports its sources, it does not audit them. See What to expect.

The seven searches are the point of the shape: the guide tells the agent to cast wide with genuinely different phrasings rather than variations of one, and the pages they return are merged and deduplicated by URL before the rest of the query runs. On the wire the rows arrive in a compact tabular encoding rather than JSON, which is why a table this size costs so little context.

Result sets

Every call stores its output and returns a result set id — ra84968f0532 for the query above. A later query reads it by putting that id in FROM, so a follow-up costs no new searching. That is what makes the second question cheap. Ask “now just the ones under €30,000, longest range first” and the agent queries the stored table instead of going back to the web:

SELECT model, range_km, price_eur
FROM ra84968f0532
WHERE TRY_CAST(price_eur AS INTEGER) < 30000
ORDER BY TRY_CAST(range_km AS INTEGER) DESC

model	range_km	price_eur
Renault 5 E-Tech	400	25000
Citroen e-C3	320	23300
Leapmotor T03	265	18900
Dacia Spring	225	16900
BYD Dolphin Surf	220	18990

That one returned in seconds, because no page was searched or fetched. The searched pages are also kept as their own result set, so a different question can be asked of the same 1,125 pages without paying for the crawl twice. Result sets are kept for 30 days.

What to expect

Queries are slow by ordinary tool standards — seconds to minutes, because each one runs real searches, fetches pages and calls an LLM per row. That is normal, not a hang.
Large tables come back as a preview. Above roughly 50,000 characters the response degrades to a few sample rows with long strings clipped; the full table stays available through the result set id.
A report takes minutes to build, and a client that gives up on a slow tool call cancels it. Ask for a report from aggregated result sets rather than a raw table of hundreds of rows, so it finishes before the client stops waiting.
Extraction does not fact-check its sources. A single page can be outdated or the wrong edition, so cross-check precise numbers across independent pages.

When something is refused

401 — no credential reached the server, or it is invalid. Check the header name and the key.
429 — too many queries at once, cluster-wide. Retry shortly.
403 — this deployment runs an allowlist and the account is not on it. Production does not, so you should not see this; if you do, email support@keenable.ai with the address you signed in with. Signing in again will not clear it.

Get started

Tools

API reference

Reference

Connect

Claude Code

Claude

ChatGPT

Codex, Cursor

One click

Claude Code

Claude Desktop, claude.ai and mobile

ChatGPT

Codex, Cursor and other clients

The tools

Result sets

What to expect

When something is refused

​Connect

Claude Code

Claude

ChatGPT

Codex, Cursor

​One click

​Claude Code

​Claude Desktop, claude.ai and mobile

​ChatGPT

​Codex, Cursor and other clients

​The tools

​Result sets

​What to expect

​When something is refused

Connect

One click

Claude Code

Claude Desktop, claude.ai and mobile

ChatGPT

Codex, Cursor and other clients

The tools

Result sets

What to expect

When something is refused