Docs MCP

Your docs, agent-ready

Index Markdown docs into a production-ready MCP.
Agents search, retrieve, and correctly cite your docs.Offline-firstPerformant
Markdown-based
Get accessView Github
~/acme-project

Trusted by

Solution

Three tools, zero friction

A compact, read-only MCP toolset optimized for agent workflows. Search, retrieve, and improve — all over MCP.

search_docs

Semantic, keyword, or path-prefix search across all indexed docs. Compact hits with scores, snippets, and anchors — optimized for token efficiency.

get_doc

Fetch a full document or a specific section by anchor. Bounded payloads with token estimates prevent context window overflow.

guides/retries
742 tokens
#backoff-strategy
## Backoff strategy
The SDK uses exponential backoff with
jitter to retry failed requests.
Default configuration:
- Max retries: 3
- Initial delay: 500ms
- Max delay: 60s
- Backoff multiplier: 1.5
truncated: false

give_feedback

Agents and users report missing or outdated docs inline. Feedback is logged as structured JSON for docs improvement loops — no separate database required.

give_feedback
medium
fromagent:cursor
doc_idguides/retries
categorymissing_content

"Missing details for per-request override in Python SDK"

logged
stdout

How it works

From Markdown to
agent-ready in minutes

1

1. Configure your repo

Point Speakeasy at your Markdown docs and SDK repos with a simple gen.yaml. Define which folders to index and which SDK references to include.

acme/docs
docs
getting-started.md
authentication.md
webhooks.md
guides
pagination.md
error-handling.md
api-reference
users.md
orders.md
payments.md
sdk-repos
acme-ts-sdk
README.md
reference

2

2. Index your content

Speakeasy scans your docs, builds a full-text search index, and generates vector embeddings — optimized for fast, token-efficient agent queries.

Terminal
$ speakeasy run
 
→ Scanning ./docs — 47 Markdown files
→ Scanning acme-ts-sdk — 12 reference docs
→ Building full-text search index
→ Generating vector embeddings
 
✓ MCP server compiled → @acme/docs-mcp

3

3. Use your favorite client

Add the compiled MCP server to Cursor, VS Code, or any MCP-compatible client. Agents can immediately search, retrieve, and cite your docs.

~/acme-project
Performance

Built for speed, optimized for agents

Hybrid search combining full-text, phrase proximity, and vector similarity. Fast enough for multi-turn agent loops.

$0

To embed a 28.8 MB corpus

0ms

Median search latency (FTS)

$0

Per 1,000 queries with embeddings

"Speakeasy was critical in launching our MCP server, and they’ve continued to be a great partner in iterating on the server since then"

Benjamin Woskow

Sr. Director of Eng @ LaunchDarkly

"The MCP we built using Speakeasy just works. It was honestly much simpler than we expected"

Constantine Nathanson

Staff Full Stack Engineer @ Cloudinary

"It took me 30 minutes to set up the first toolset which included testing it out locally to see how it worked. I was extremely impressed with the experience"

James Perkins

Co-founder & CEO @ Unkey

"Speakeasy was critical in launching our MCP server, and they’ve continued to be a great partner in iterating on the server since then"

Benjamin Woskow

Sr. Director of Eng @ LaunchDarkly

"The MCP we built using Speakeasy just works. It was honestly much simpler than we expected"

Constantine Nathanson

Staff Full Stack Engineer @ Cloudinary

"It took me 30 minutes to set up the first toolset which included testing it out locally to see how it worked. I was extremely impressed with the experience"

James Perkins

Co-founder & CEO @ Unkey

Make your docs work for every AI agent

Book a demoRead the docs