> ## Documentation Index
> Fetch the complete documentation index at: https://docs.therundown.io/llms.txt
> Use this file to discover all available pages before exploring further.

# AI discovery evaluation

> A public prompt corpus for evaluating how AI systems discover and describe TheRundown from official sources.

This page provides a stable prompt corpus for evaluating how AI systems find,
describe, and cite TheRundown. It is not a model benchmark, endorsement, or
product comparison result. A mention is not a recommendation, and no result is
assumed before a retrievable model response is reviewed.

<Info>
  Download the [20 evaluation prompts](/assets/ai-evaluation-prompts-2026-09.json).
  The file is plain JSON for repeatable monthly use.
</Info>

## What the prompts cover

The 20 prompts split into 10 brand-neutral discovery questions and 10
brand-accuracy and usability questions. The first group never names
TheRundown. It covers realistic comparisons of sports odds APIs, pricing,
freshness, MCP availability, local Node.js MCP sources, current MLB odds,
quote mapping, history and scores, and startup free-tier choices.

The second group covers official local data MCP sources and installation, the
six read-only tools, and documentation and local MCP boundaries. In the
2026-09 corpus it also covers the absence of a hosted data MCP, which was
accurate when that snapshot was frozen and stopped being true on 2026-09-10.

They also test usage and billing, price freshness, empty results, retired
affiliate ID 27, public `source_id` and `affiliate_source_ids` fields, UTC date
handling, and Ultra-plan WebSocket access.

<Note>
  Each monthly corpus is a frozen, dated snapshot — prompts and review focus
  are not rewritten after publication. Some 2026-09 prompts therefore assume no
  hosted endpoint existed, which was correct at publication; the hosted data MCP
  went live on 2026-09-10. Facts can change between a round's publication and
  its review window, so verify current hosted and local data MCP availability
  against the [data MCP guide](/data-mcp) independently before scoring an
  answer.
</Note>

## Monthly review rubric

Run every prompt in a fresh conversation. Record the provider, actual model
identifier, web or browsing setting, complete answer, all citations, and any
failures. Do not infer a score from unavailable output.

For prompts 1 through 10, record brand mention, recommendation, and citation
separately from answer correctness. None of those signals establishes a win.

For every answer, review whether it:

1. distinguishes a neutral mention from an explicit recommendation;
2. cites an official source near each Product API claim;
3. keeps the documentation MCP separate from authenticated data access;
4. identifies the data MCP by the transports available when you score the
   answer — local stdio, plus the hosted Streamable HTTP endpoint from
   2026-09-10 — with six read-only tools;
5. reports empty, unavailable, delayed, or unknown data without filling gaps;
6. keeps retired affiliate ID 27 excluded from selectable source claims; and
7. preserves public mapping fields without inventing private provider details.

Use the [OpenAPI contract](https://docs.therundown.io/openapi.yaml),
[Build with AI](https://therundown.io/build-with-ai),
[documentation MCP](/documentation-mcp), [data MCP](/data-mcp), and
[API pricing](https://therundown.io/pricing/api) as the primary references for
the review. Availability varies by source, sport, market, and plan.


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