evipedia-mcp
This server lets AI agents search, browse, and read evipedia.ai's evidence reviews on health and longevity interventions, check what's new, pull structured medical metadata, and suggest new interventions for review.
Search reviews —
search_reviews(query)finds matching reviews by name, synonym, keyword, or category, ranked by relevance, so you can tell whether a review exists.Browse the catalogue —
list_reviews()returns every review as{topic, slug}pairs for enumeration.Track changes —
list_updates(days?)returns the newest-first change feed as{title, slug, status, date}, flagging newly published (new) or revised (updated) reviews; defaults to the last 7 days, capped at 100.Read the conclusion —
get_conclusion(slug|url)returns just the plain-text bottom line.Read the full review —
get_review(slug|url)returns the complete review as raw Markdown.Get structured metadata —
get_metadata(slug|url)returns JSON with review dates (freshness signal), the typedaboutentity with alternate names, and an ordered citation list with PubMed PMIDs.Suggest a new review —
suggest_review(intervention, goal?, references?, email?)submits a proposal to evipedia's public suggestion form; the only write path (use only when the user explicitly wants to propose a review).Check the build —
get_version()returns the running server's package name and version.All read tools accept either a bare slug (e.g.
rapamycin) or a full evipedia.ai URL, so search results can be passed straight through.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@evipedia-mcp What's the evidence on rapamycin for longevity?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.

Evipedia 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 conclusionlist_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 conclusionget_review(slug|url)→ the full review as raw Markdownget_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
Related MCP server: pubmed-mcp-server
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 |
| Full catalogue: |
| Search index: |
| Change feed, newest first: |
| Complete review as raw Markdown (frontmatter + full body) |
| Flattened medical metadata: |
| Agent/human signpost — includes the stable section anchor list |
| Canonical review URLs |
| RSS feed of latest updates |
Available Tools
8 toolsget_conclusionA
Get just the plain-text conclusion of an evidence review — the evidence-based bottom line, without the methodology, findings, or references. Prefer this for 'does X work?' questions; use get_review only when the user needs the full evidence, or get_metadata for dates and citations. Takes a slug from search_reviews, list_reviews, or list_updates; a full https://evipedia.ai/{slug} URL is also accepted and normalised to the slug. Read-only, no authentication required; an unknown slug raises "Review not found: {slug}".
Inputs: slug (required).
get_metadataA
Get a review's structured medical metadata as JSON: review dates (datePublished/dateModified/lastReviewed — a freshness signal absent from the Markdown), the intervention as a typed about entity with alternate names, and an ordered citation list of primary sources (each with a PubMed pmid when available). Use when you need the review's freshness, machine-readable references/PMIDs, or drug classification rather than prose.
Inputs: slug (required).
get_reviewA
Get the full evidence review as raw Markdown — methodology, findings, safety, dosing, and references. Reviews are long (often 10k+ tokens); use get_conclusion when the bottom line is enough, or get_metadata for machine-readable dates and citations. Takes a slug from search_reviews, list_reviews, or list_updates; a full https://evipedia.ai/{slug} URL is also accepted and normalised to the slug. Read-only, no authentication required; an unknown slug raises "Review not found: {slug}".
Inputs: slug (required).
get_versionA
Get the running evipedia MCP server's package name and version. Useful to confirm which build is loaded.
Inputs: no named parameters.
list_reviewsA
List every evidence review in the evipedia.ai catalogue. Returns a JSON array of {topic, slug}, where topic is the review's canonical topic and slug is its identifier. Pass a slug directly to get_review or get_conclusion; the full review URL is https://evipedia.ai/{slug} (raw Markdown at https://evipedia.ai/{slug}.md). A bare topic (just the intervention name, e.g. 'Rapamycin') implies the default Health & Longevity goal; a topic with an explicit goal (e.g. 'Botox for Skin Rejuvenation') is a review targeting that specific goal. Use to enumerate or browse the full catalogue; use search_reviews to find specific reviews.
Inputs: no named parameters.
list_updatesA
List recently published or revised evipedia.ai reviews, newest first — the catalogue's change feed. Returns a JSON array of {title, slug, status, date}, where status is 'new' (first publication) or 'updated' (an existing review revised) and date is YYYY-MM-DD. Called with no arguments it returns the last 7 days, capped at the 100 most recent entries; pass days for a different window (e.g. days: 30) — an explicit days is never capped, so a wide window can return hundreds of entries. The full review is at https://evipedia.ai/{slug}, and get_conclusion/get_review take the slug. Use this for recency ('what's new?'); use list_reviews to enumerate the whole catalogue and search_reviews to find a specific intervention.
Inputs: days.
search_reviewsA
Search evipedia.ai evidence reviews by name, synonym, keyword, or category. A discovery tool: returns the matching reviews (goal-qualified topic + URL) ranked by relevance so you can tell whether a review exists. To read one, pass its slug to get_conclusion (quick bottom line) or get_review (full Markdown).
Inputs: query (required).
suggest_reviewA
Suggest a new review for evipedia.ai — propose an intervention the catalogue doesn't cover yet. Submits to evipedia's public suggestion form (the same one at evipedia.ai/suggest). Use only when the user explicitly wants to propose a new review — this sends a message to the evipedia team. Search first with search_reviews to confirm the catalogue doesn't already cover it. Only intervention is required; goal, references, and email are optional and are forwarded verbatim. Returns a plain-text confirmation on success and raises "Suggestion failed: HTTP {status}" otherwise. Not idempotent — calling it twice files two separate suggestions.
Inputs: intervention (required), goal, email, references.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 8 tools
Each tool has a clearly distinct purpose: catalog listing, discovery search, change feed, conclusion retrieval, full review, suggestion submission, and metadata. The descriptions explicitly differentiate similar tools (e.g., get_conclusion vs get_review vs get_metadata), making misselection unlikely.
All tools follow a consistent snake_case verb_noun pattern (get_version, list_reviews, search_reviews, etc.), with clear and predictable names matching their actions.
8 tools is well-scoped for an evidence review server, covering retrieval, discovery, metadata, and suggestion submission without redundancy or bloat.
The surface covers the full lifecycle: browsing, searching, reading conclusions, full reviews, metadata, recent updates, version check, and even proposing new reviews. No obvious gaps for the stated domain.
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