> ## Documentation Index
> Fetch the complete guide index at: https://www.synscribe.com/agentic-discovery/llms.txt
> Use this file to discover all pages before exploring further.

---
title: "Resend: the Smallest Corpus With the Highest Score"
description: Resend benchmarks 92.3 on Context7, the highest score in our probes, on a small corpus, with an agent-addressed llms.txt and .well-known discovery.
slug: /agentic-discovery/case-studies/resend
series: The Agentic Discovery Playbook · Case Study
last_verified: 2026-06-11
---

# How Resend Scored 92.3: the Smallest Corpus With the Highest Score

> **The lesson:** Resend posts the highest retrieval-quality score in our category probes, 92.3 on Context7, with one of the smallest corpora we audited. Every page is runnable markdown. The llms.txt talks to agents directly. Discovery manifests live at `.well-known` URIs. Density and addressability beat bulk. A young product can out-score corpora many times its size.

## At a glance

| | |
|---|---|
| Category | Transactional email API |
| Context7 benchmark (`websites/resend`) | **92.3**, the highest single score in our category probes (2026-06-11) |
| Corpus size (`websites/resend`) | 3,765 snippets, small by winner standards |
| SDK entry (`resend/resend-node`) | just 33,355 tokens / 712 snippets, benchmark 80.2, updated 2 weeks |
| Agent-skills installs | ~17.6K (order of magnitude only, telemetry-based) |

## What they built

Resend's agent surface is small, complete, and pointed entirely at machines. The llms.txt doesn't just exist for agents. It talks to them:

> "For AI agents and automation, use the tools below"
> resend.com/llms.txt

And the "tools below" include something we found nowhere else in 18 audits: discovery manifests at well-known URIs:

> "- [MCP Discovery](https://resend.com/.well-known/mcp.json)" · "- [Skills Discovery](https://resend.com/.well-known/agent-skills/index.json)"
> resend.com/llms.txt (the only well-known-URI agent discovery we found in the wild)

| Surface | What Resend ships |
|---|---|
| llms.txt | Fully agent-addressed; links MCP + skills discovery manifests and the OpenAPI spec |
| `.well-known` manifests | `resend.com/.well-known/mcp.json` and `.well-known/agent-skills/index.json` (unique in our survey) |
| Markdown mirrors | Sitewide `.md`, including `pricing.md`, so even the commercial pages are agent-readable |
| OpenAPI | Spec served at the site root |
| MCP server | Official, at github.com/resend/resend-mcp |
| Agent Skills | Official skills repo; ~17.6K installs (order of magnitude) |
| Docs style | Quickstart-dense; every page self-contained and runnable |

The docs style is the part that moves the score. Resend's pages are quickstart-shaped: install command, imports, code, expected result. That is the exact snippet anatomy retrieval rubrics reward. There's no sprawling conceptual layer for a scorer to wade through, because the product itself is narrow: send email, manage domains, check delivery. The corpus is small because the product surface is small, and every snippet earns its place.

The `pricing.md` detail is worth pausing on. Mirroring docs pages as markdown is now common among agent-forward products. Mirroring the *pricing page* signals a different assumption: an agent may be the one comparing vendors, not just building with one. Resend builds for the evaluation visit, not only the integration visit.

## The receipts

All figures observed 2026-06-11; single-day metrics carry ±10% error bars.

**The headline:** querying Context7 for "resend" returns `/websites/resend` at benchmark **92.3**. That is among the highest we observed anywhere, and the top single score in our category probes. Where that sits against the field:

| Entry | Snippets | Benchmark |
|---|---|---|
| `websites/resend` | 3,765 | **92.3** |
| `websites/stripe` (largest corpus we found anywhere) | 265,284 | 84.5 |
| Drizzle (`drizzle-team/drizzle-orm`) | 440 | 82.8 |
| Resend SDK (`resend/resend-node`) | 712 | 80.2 |
| Polar (`polarsource/polar`) | 2,297 | 64.7 |

Resend beats the largest docs corpus on the internet on quality with roughly 1.4% of its volume. Even its SDK entry, just 33K tokens, outscores Polar's corpus three times its size.

**Quality over size is the pattern, not the exception.** The same audit batch shows Drizzle at 82.8 on 440 snippets while Polar scores 64.7 on 2,297. That pair proves the scoring rubric measures density, not mass. Resend is the extreme point on that curve: corpus cut down to runnable essentials, score near the ceiling. Our correlation study across 17 indexed entries found benchmark barely tracks corpus mass (ρ≈0.24–0.30). The benchmark is an eval you can study for, and Resend studied.

**The honest gap:** Resend wins its *brand* query. But the *category* query "transactional email api" has no clear winner. The results are a jumble of Mailchimp Transactional SDKs, Listrak, ForwardEmail, and ElasticEmail, with `/resend/react-email` (benchmark 84.7) the only high-benchmark entry present. The highest quality score in the category has not yet claimed the category's task query. That's a description-engineering gap, not a docs gap.

**The unique artifact:** the `.well-known/mcp.json` + `.well-known/agent-skills/index.json` pair makes Resend the only product we observed using well-known-URI conventions for agent discovery. Cost to replicate: roughly an afternoon.

**Why early discovery surfaces matter more than they look:** our pilot experiment E4 found that when an agent *learns* a vendor ships agent tooling, that fact alone flipped vendor choice 2/2 (single model, pilot scale). Agent-operability has become a selection criterion. And `.well-known` manifests plus an llms.txt that lists your MCP are exactly how an agent learns it at decision time.

## What to copy

- [ ] Address your llms.txt to agents directly and link your MCP, skills, and OpenAPI from it. Make it a tool index, not a sitemap. ([Play 5](/agentic-discovery/llms-txt))
- [ ] Mirror *every* page as markdown, including pricing. Agents comparing vendors read commercial pages too. ([Play 6](/agentic-discovery/markdown-docs-for-ai-agents))
- [ ] Publish `.well-known/mcp.json` and `.well-known/agent-skills/index.json`. They cost nothing, follow a standard shape, and you'd be among the first. ([Play 3](/agentic-discovery/mcp-server-distribution))
- [ ] Make every docs page a self-contained runnable unit: install + imports + code + expected output. This is what 92.3 is made of. ([Play 7](/agentic-discovery/code-snippets-for-ai-agents))
- [ ] Ship the official MCP and skills repo even while small. Presence compounds. ([Play 3](/agentic-discovery/mcp-server-distribution), [Play 4](/agentic-discovery/agent-skills-and-agents-md))
- [ ] Then claim the *task* query, not just your brand query. Check who wins your category's words today. ([Play 2](/agentic-discovery/ai-agent-registries-and-directories))

## What NOT to over-copy

- **Small corpus works because the product is small.** A transactional email API genuinely fits in a few thousand snippets; a database or framework doesn't. Copy the density discipline, not the corpus size.
- **The 92.3 was measured on Resend's brand query.** It shows retrieval *quality*, not category *demand*. Resend does not appear in our top-50 fetch-share table, and its category query remains unclaimed. A high benchmark is necessary, not sufficient.
- **`.well-known` manifests are early, not proven.** Resend is the only product shipping them, and we have no traffic evidence they're being read yet. Ship them because they're cheap and standards-shaped, not because we measured payoff.
- **Survivorship and snapshot error.** One product, one day, ±10% error bars; benchmark scores are recomputed continuously. Install counts (~17.6K) are opt-out telemetry, order of magnitude only.

## FAQ

**Why does Resend score 92.3 on Context7?**
Because its corpus is built for the scoring rubric: quickstart-dense pages where every snippet is self-contained and runnable, no duplication, no repo noise. Context7's benchmark has a product's snippets answer common developer questions and scores the answers. Small-but-complete beats large-but-padded, and Resend (92.3 observed 2026-06-11) is the cleanest example we found.

**What is resend.com/.well-known/mcp.json?**
A discovery manifest at a well-known URI telling agents where Resend's MCP server lives, paired with `.well-known/agent-skills/index.json` for skills. Resend is the only product we found using well-known-URI conventions for agent discovery, and both files are linked directly from its llms.txt.

**Does a small docs corpus hurt AI agent discovery?**
Not on quality metrics. Our 17-entry correlation study found benchmark scores barely track corpus size (ρ≈0.24–0.30), and Resend's small corpus posts the highest score in our probes. Size only matters insofar as it covers the questions developers actually ask. Padding beyond that triggers duplication penalties.

**Does Resend win the "transactional email api" query?**
Not yet, as of 2026-06-11. That category query returns no clear winner, with `/resend/react-email` (benchmark 84.7) the only high-benchmark entry present. Resend's 92.3 lives on its brand query. Claiming the task query is a description-engineering move it hasn't made.

---

*Snapshot date 2026-06-11; single-day metrics carry ±10% error bars. Part of [Case Studies](/agentic-discovery/case-studies) · [The Complete Playbook to Agentic Discovery](/agentic-discovery).*

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