About Evipedia
Evipedia MCP Server connects AI agents to evipedia.ai, an updated encyclopedia of evidence reviews on health and longevity interventions. It supports review discovery, detailed source and metadata retrieval, and suggestions for new topics.
Key features
- 01Search and browse intervention reviews
- 02Read conclusions and full reviews
- 03Retrieve medical metadata and citations
- 04Track recently published or revised reviews
- 05Suggest an intervention for review
Use cases
- 01Find evidence reviews for an intervention
- 02Check review dates and cited PubMed records
- 03Follow changes to the review catalogue
- 04Submit a topic for a future review
MCP support
Exposes review search, catalogue, updates, conclusion, full-text, metadata, suggestion, and version tools to MCP clients. It can run locally over stdio or be accessed through the hosted HTTP server.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
8Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| query | string | Yes | Search query — intervention name, synonym, drug class, or category |
Details
From the project READMEEvipedia MCP Server
A small Model Context Protocol server that lets AI agents query evipedia.ai — our continuously-updated encyclopedia of evidence reviews on health & longevity interventions — and suggest new interventions for review.
Tools
-
search_reviews(query)→ matching reviews (name/synonym/keyword/category), each with its URL and conclusion -
list_reviews()→ the full catalogue as{topic, slug}pairs (canonical topic + the slug you pass toget_review/get_conclusion) -
list_updates(days?)→ the change feed, newest first:{title, slug, status, date}per recently published (new) or revised (updated) review. With no arguments it returns the last 7 days, capped at 100 entries; passdaysfor a wider window (e.g.days: 30) -
get_conclusion(slug|url)→ just the review's plain-text conclusion -
get_review(slug|url)→ the full review as raw Markdown -
get_metadata(slug|url)→ structured medical metadata as JSON — review dates (datePublished/dateModified/lastReviewed, a freshness signal not in the Markdown), the typedaboutentity with alternate names, and an orderedcitationlist with PubMed PMIDsThe read tools accept either a bare slug (e.g.
rapamycin) or a full evipedia.ai URL (e.g.https://evipedia.ai/rapamycin) — the URL's last path segment is the slug, so a search result's URL can be passed straight through. -
suggest_review(intervention, goal?, references?, email?)→ submit a new review for an intervention to evipedia's public suggestion form (the same one at evipedia.ai/suggest) -
get_version()→ the running server's package name and version
Install (local or remote)
The server is published to npm as evipedia-mcp and runs over stdio via npx — no global install needed. It's also listed in the official MCP Registry as io.github.forever-healthy/evipedia-mcp, so MCP-aware clients can discover and install it automatically.
Client config: Local MCP (Requires Node.js ≥ 18)
{
"mcpServers": {
"evipedia": {
"command": "npx",
"args": ["-y", "evipedia-mcp"]
}
}
}
For environments where a local install is not possible or desired, we also provide a hosted MCP server at https://mcp.evipedia.ai/ that can be used over HTTP.
Client config: Remote MCP via HTTP
{
"mcpServers": {
"evipedia": {
"type": "http",
"url": "https://mcp.evipedia.ai/mcp"
}
}
}
- Claude Code — add to your project's
.mcp.json(or runclaude mcp add). - Claude Desktop — add to
claude_desktop_config.json. - Cursor — add to the MCP settings.
Try it
You can use the bundled /demo skill to smoke-test the respective connection.
It walks through the read tools (get_version, search_reviews, list_reviews, list_updates, get_review, get_conclusion, get_metadata) against live evipedia.ai data.
Architecture
The server is a thin client that only uses evipedia.ai's public endpoints. It does not depend on the evipedia content repo — the public surfaces are the API by design.
- Fetches live from
https://evipedia.aiwith a small in-process cache (both JSON indexes are tiny) - Mostly read-only, no auth required. The one write path is
suggest_review, which POSTs to evipedia's public suggestion form (Formspree)
Public API Surface
Base URL: https://evipedia.ai
| Endpoint | Description |
|---|---|
GET /reviews.json | Full catalogue: canonical_name, alternate_names[], permalink, permalink_md, permalink_meta, category, creation_date, dateModified, lastReviewed, er_conclusion |
GET /search.json | Search index: short_topic, alternate_names, ep_keywords, ep_category, url |
GET /updates.json | Change feed, newest first: title, permalink, status (new/updated), date |
GET /{permalink}.md | Complete review as raw Markdown (frontmatter + full body) |
GET /{permalink}.meta.json | Flattened medical metadata: slug, topic, url, datePublished/dateModified/lastReviewed, about (type/name/alternateName), ordered citation[] (name, url, pmid?) |
GET /llms.txt | Agent/human signpost — includes the stable section anchor list |
GET /sitemap.xml | Canonical review URLs |
GET /feed.xml | RSS feed of latest updates |
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
