trip2g SEO Plan

Scope: trip2g.com — self-hosted markdown vault / MCP memory server for AI agents.
Audience: Developers building AI agents, LLM-Wiki pattern followers, Obsidian power users.
Date: June 2026.

0. Site Crawlability — Critical Finding

The good news: Pages appear to be server-side rendered — the fetched HTML includes actual
text content, not empty divs. Google and LLM crawlers can read the content.

The bad news (observed on live site):

Problem Evidence
No <meta name="description"> on any page checked Confirmed on /en/user/agent_memory, /en/user/token_economy_bench, /en/user/use_cases, /en/thoughts — none returned a meta description
No <link rel="canonical"> Confirmed in engine audit (docs/dev/seo_audit.md:47) — tag is entirely absent from all pages
No structured data / JSON-LD Confirmed on all pages fetched — no SoftwareApplication, FAQ, HowTo, or Article schema
/en/ homepage returns 404 https://trip2g.com/en/ is 404; the sitemap lists /en/ paths but homepage lives at /
robots.txt returns only "open" No crawl directives, no Sitemap: pointer — crawlers don't auto-discover the XML sitemap
Missing hreflang on bilingual pages Both /en/ and /ru/ versions exist; no hreflang signals to avoid duplicate content penalty
No og:description or twitter:description Falls back to nothing when description frontmatter is absent (confirmed in engine code)

These are the highest-priority technical fixes — they undercut everything else.

1. Keyword Strategy

The Audience's Search Behavior

Developers building AI agents search with high specificity. They are Googling, but increasingly
asking ChatGPT/Perplexity/Claude directly. Both channels must be served.

Primary Clusters (High Intent, Reachable)

Cluster A — MCP Memory (HOTTEST, low-to-medium competition)

The MCP ecosystem is new. Competition is thin and the intent is perfect.

Keyword Intent Competition Priority
MCP server memory Tool-shopping Low ★★★★★
MCP long-term memory Tool-shopping Low ★★★★★
Claude Code memory MCP Tool-shopping Low ★★★★★
persistent memory MCP server Tool-shopping Low ★★★★★
MCP knowledge base Tool-shopping Low-Med ★★★★
MCP agent context Informational Low ★★★★
claude desktop memory server Tool-shopping Low ★★★★

Rationale: MCP is ~18 months old. The directory of MCP servers is still sparse. A focused
landing page here can rank #1 within weeks, not months.

Cluster B — AI Agent Memory (Medium competition, growing fast)

Keyword Intent Competition Priority
AI agent long-term memory Informational + shopping Med ★★★★
agent memory self-hosted Tool-shopping Low ★★★★★
LLM memory persistence Informational Med ★★★
agent knowledge base Tool-shopping Med ★★★★
self-hosted agent memory Tool-shopping Low ★★★★★
AI agent context window management Informational Low-Med ★★★

Cluster C — LLM Wiki / Karpathy Pattern (Niche, very low competition)

Karpathy's "LLM Wiki" concept has been circulating on X/HN. Direct targeting.

Keyword Intent Competition Priority
LLM wiki Informational Very Low ★★★★★
Karpathy LLM wiki implementation Informational Very Low ★★★★★
personal knowledge base for AI Informational Low ★★★★
AI-queryable wiki self-hosted Tool-shopping Very Low ★★★★★

Cluster D — Obsidian Integration (Medium competition, large audience)

Keyword Intent Competition Priority
Obsidian MCP server Tool-shopping Low ★★★★★
Obsidian AI agent integration Tool-shopping Low-Med ★★★★
Obsidian publish alternative Tool-shopping Med ★★★
Obsidian two-way sync self-hosted Tool-shopping Low ★★★★
Obsidian as knowledge base for AI Informational Low ★★★★

Cluster E — Head Terms (Aspirational, long-term)

These are competitive but worth targeting via content, not direct homepage optimization.

Keyword Intent Competition Priority
AI agent memory Informational High ★★ (content-only)
self-hosted RAG Tool-shopping Med-High ★★★
vector database alternative Tool-shopping High ★★
digital garden Informational High ★ (secondary)

Long-Tail Wins (Target immediately via content)

  • "how to give Claude long-term memory"
  • "MCP server for agent notes"
  • "self-hosted memory for AI assistants without vector database"
  • "Obsidian vault as MCP context"
  • "token-efficient agent memory retrieval"
  • "agent memory 15x cheaper than RAG"

2. Technical SEO

Fix 1: Add <link rel="canonical"> to Every Page (CRITICAL)

Problem: Absent entirely. The same content is reachable at multiple URLs (main domain,
custom domains, alternate-permalink redirects, /index aliases).

Fix: Emit canonical in internal/defaulttemplate/views.html. The logic already exists in
ogURLForNote (endpoint.go:419-447) — reuse it:

<link rel="canonical" href="{% full absolute URL from ogURLForNote %}">

The seo_audit.md confirms this is the right reference point.

Fix 2: Auto-generate Meta Descriptions (CRITICAL)

Problem: Descriptions only emit when description: frontmatter is set. Most pages have none.
Without this, Google generates its own snippet (usually bad) and LLM crawlers get no summary.

Fix A (engine): Add first-paragraph fallback in resolve.go — the PartialRenderer().Introduce()
method is already used for magazine cards (views.html:362). Truncate to 155 chars. Remove
the // TODO comment from endpoint.go:401.

Fix B (content): Add explicit description: frontmatter to the 10 most important pages
immediately (list in §3 below).

Fix 3: Add hreflang for EN/RU pairs (HIGH)

The sitemap confirms full /en/ and /ru/ mirroring. Without hreflang, Google sees duplicate
content and may penalize or split ranking signals.

Add in <head> for every bilingual page:

<link rel="alternate" hreflang="en" href="https://trip2g.com/en/user/agent_memory">
<link rel="alternate" hreflang="ru" href="https://trip2g.com/ru/user/agent_memory">
<link rel="alternate" hreflang="x-default" href="https://trip2g.com/en/user/agent_memory">

Fix 4: robots.txt — Add Sitemap Pointer (HIGH)

Current robots.txt returns only "open" (likely a bare page, not a proper robots.txt).

Fix: Serve a proper text file at /robots.txt:

User-agent: *
Allow: /

Sitemap: https://trip2g.com/sitemap.xml

The sitemap at /sitemap.xml exists (680 URLs confirmed) but crawlers won't auto-discover it
without this pointer.

Fix 5: Fix /en/ 404 (HIGH)

https://trip2g.com/en/ returns 404. The sitemap references /en/ paths. This breaks
Googlebot's crawl of the English section's root. Either:

  • Redirect /en/ → /en/user (the docs index), or
  • Create an English homepage at /en/

Fix 6: Add Structured Data (MEDIUM)

No JSON-LD anywhere. Opportunities:

SoftwareApplication schema on the homepage:

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "trip2g",
  "applicationCategory": "DeveloperApplication",
  "description": "Self-hosted MCP memory server and markdown vault for AI agents. 15× cheaper token retrieval than whole-note dumps.",
  "operatingSystem": "Linux, macOS, Docker",
  "offers": {"@type": "Offer", "price": "0", "priceCurrency": "USD"},
  "url": "https://trip2g.com",
  "license": "https://opensource.org/licenses/MIT"
}

FAQPage schema on the token economy benchmark page (the Q&A structure is already there).

HowTo schema on the agent_memory quickstart page (numbered steps already present).

Article schema on all /en/thoughts/ posts.

Fix 7: Open Graph Tags Audit (MEDIUM)

Confirm all pages have og:title, og:description, og:image, og:url.
Currently og:description is absent when description: frontmatter is missing (same bug as §Fix 2).
The OG image appears to link Yandex Cloud signed URLs — ensure these are stable/permanent for
social sharing previews.

Fix 8: Page Speed

No page speed data was observable. Given the server is Docker-based:

  • Ensure static assets (CSS/JS) have long cache headers and are minified.
  • The SSR approach is good for Core Web Vitals. Verify LCP (Largest Contentful Paint) image
    is preloaded via <link rel="preload">.
  • Consider adding <link rel="preconnect"> for Yandex Cloud CDN origin.

3. On-Page / Content

Title & Meta Description Rewrites

Priority pages:

Page Current Title Recommended Title (≤60 chars) Recommended Meta Description (≤155 chars)
Homepage (/) "trip2g" (implied) trip2g — Self-Hosted MCP Memory for AI Agents Persistent long-term memory for AI agents. Self-hosted MCP server, Obsidian sync, and 15× cheaper token retrieval than whole-note RAG. Docker in 30 seconds.
/en/user/agent_memory Long-term memory for AI agents with trip2g AI Agent Long-Term Memory with MCP — trip2g Give your AI agent persistent memory via MCP. search → expand → read_section costs 15× fewer tokens than full-note retrieval. Self-hosted, Docker, Obsidian sync.
/en/user/token_economy_bench Token economy: measured on this site MCP Memory Token Benchmark: 15–37× Savings vs RAG Real benchmark: focused section retrieval costs 15× fewer tokens than whole-note dumps, 23× cheaper than grep. Open methodology, reproducible Python script.
/en/user/agent_status Auto-broadcast your work status to the team Team Status Broadcasting via Claude Code Hooks Auto-sync your Claude Code session summaries to a shared trip2g vault. Session-end hook redacts secrets, LLM-summarizes, and broadcasts structured status to teammates.
/en/user/use_cases Use cases trip2g Use Cases: AI Agents, Digital Gardens & More From AI consultant knowledge bots to Obsidian-powered digital gardens. See how developers use trip2g as self-hosted MCP memory and public knowledge base.
/en/user (docs index) Documentation trip2g Docs — MCP Memory Server & Obsidian Sync Full documentation for trip2g: self-hosted MCP memory, Obsidian two-way sync, federation, token economy, and AI agent integration.

Heading Structure

Homepage H1: "Your second brain was always meant to be shared." — This is poetic but SEO-blind.
Visitors arriving from "MCP memory server" search need to recognize the product instantly.

Recommendation: Add an invisible (sr-only) or secondary H1 alongside the tagline, OR change
the H1 to something like:

"Self-Hosted MCP Memory for AI Agents"

And demote the tagline to a subheading or <p class="hero-subtitle">.

agent_memory page H2s: The structure is solid (numbered steps). Add <h2>Why trip2g for AI agent memory?</h2> near the top to capture informational queries and give Google a clear topic.

Internal Linking (Exploit the Wikilink Graph)

The [[wikilink]] graph is trip2g's biggest untapped internal-link asset. Every note already
links to related notes. SEO actions:

  1. Ensure wikilinks resolve to <a href> tags in rendered HTML (confirm engine renders them,
    not just displays them as text). If they render, you already have a powerful internal link graph.

  2. Create hub pages that aggregate clusters:

    • /en/user/mcp-memory — hub for all MCP agent memory content (links to agent_memory,
      token_economy_bench, agent_status, federation)
    • /en/user/obsidian-mcp — hub for Obsidian integration content

  3. Link from homepage to the benchmark page and the agent_memory quickstart — currently the
    homepage is the only entry point and may not pass PageRank to these high-value pages.

  4. Cross-link the benchmark from agent_memory quickstart: "See our token economy benchmark
    showing 15–37× savings vs whole-note retrieval."

Content Gap: Comparison Pages (High-ROI)

These are the clearest opportunities to intercept high-intent queries:

Article 1: "trip2g vs Vector Database (Pinecone / Weaviate / Qdrant) for Agent Memory"

  • Target: "self-hosted RAG alternative", "vector db for AI agents", "agent memory without Pinecone"
  • Angle: trip2g uses full-text + vector hybrid; no embeddings infra required for most use cases;
    Markdown-native; 23× cheaper token retrieval than grep-over-files.
  • Note: docs/dev/why_not_qdrant.md exists internally — this is the source material.

Article 2: "Obsidian as AI Agent Memory: Complete MCP Setup Guide"

  • Target: "Obsidian MCP server", "use Obsidian with Claude", "Obsidian long-term memory AI"
  • Angle: step-by-step with actual config, memcli one-liner, live demo GIF.

Article 3: "How to Give Claude Code Persistent Memory (trip2g MCP Setup)"

  • Target: "Claude Code memory", "Claude Code long-term memory", "Claude Code MCP memory"
  • Angle: Practical tutorial. This is a top-5 question in the Claude Code community right now.

Article 4: "LLM Wiki Pattern: Karpathy's Idea, Production Implementation"

  • Target: "LLM wiki", "Karpathy personal wiki AI", "AI-queryable knowledge base"
  • Angle: Acknowledge the origin, show trip2g as a concrete implementation with numbers.

Article 5: "Token Economy of AI Agent Memory: Why Section Retrieval Beats RAG Chunks"

  • Target: "token cost AI agent memory", "RAG token efficiency", "context window management agents"
  • Angle: Expand the existing benchmark page into a full explainer with diagrams.

/en/user/token_economy_bench is already the best link-bait asset on the site. To activate it:

  1. Write a standalone shareable version (blog post format, not just docs page).
  2. Add a social share image with the key number (15–37× savings).
  3. Link to it from the README on GitHub.
  4. Submit it to HN as "Show HN: We measured token costs of MCP section retrieval vs whole-note RAG".

4. Off-Page / Distribution

Where This Audience Lives

Hacker News

  • Best channel for the benchmark and the "LLM Wiki" angle.
  • Submission strategy: "Show HN: Self-hosted MCP memory server for AI agents (Obsidian sync, 15× cheaper retrieval than whole-note RAG)"
  • The benchmark with a reproducible Python script is perfect HN content — it's verifiable and opinionated.

r/LocalLLaMA

  • Active community for self-hosted AI tooling.
  • Post: "I built a self-hosted MCP memory server that syncs Obsidian in 500ms — benchmark inside"
  • The "no cloud, Docker in 30 seconds" angle plays extremely well here.

r/ObsidianMD

  • 150k+ members, Obsidian integrations are frequently upvoted.
  • Post about the two-way sync specifically.

MCP Registries / Directories

  • Submit to mcpservers.org, mcp.so, Anthropic's official MCP servers list, pulsemcp.com.
  • The MCP ecosystem directories are the highest-leverage single action for discoverability.
  • Create a proper manifest.json / MCP registry entry if not already present.

GitHub

  • Add "mcp-server", "ai-agent-memory", "obsidian-publish", "llm-memory", "self-hosted-rag"
    as GitHub Topics on the repo.
  • The README should prominently feature the benchmark numbers in the first screen.
  • Star-seeking: engage with issues in popular MCP repos (LangChain, LlamaIndex, CrewAI) where
    "persistent memory" comes up — link to trip2g as a solution.

X / Twitter

  • Karpathy's followers are prime audience. Post the benchmark thread tagging @karpathy.
  • Screenshot the 15–37× savings table as a visual asset.

Dev.to / Hashnode

  • Publish the comparison article ("trip2g vs vector DB") as a cross-post.
  • These rank in Google quickly for dev-tool queries.

Discord / Slack Communities

  • Anthropic's Discord (Claude channel), the official MCP Discord, LangChain Discord.
  • Answer questions about "Claude agent memory" and link to relevant docs.
  1. MCP ecosystem links: Get listed on every MCP directory/registry (high-authority, relevant).
  2. Benchmark citations: A reproducible benchmark with a downloadable script naturally attracts
    citations from blog posts about AI agent architecture. Reach out to 5-10 "AI agent framework"
    bloggers and offer the data.
  3. Obsidian plugin ecosystem: If trip2g has an Obsidian plugin or sync integration, get listed
    in the Obsidian community plugins page (very high-authority link).
  4. "Awesome" lists: Submit to awesome-mcp-servers, awesome-ai-agents, awesome-llm-apps.

5. LLM / AI-Search Visibility (GEO / AEO)

This audience uses ChatGPT, Claude, and Perplexity heavily. Being cited by these models is as
valuable as ranking on Google.

Add llms.txt

The emerging standard (llmstxt.org) for telling LLM crawlers what to read about your product.

Create https://trip2g.com/llms.txt:

# trip2g

trip2g is a self-hosted MCP memory server and markdown vault for AI agents.
It provides persistent long-term memory via the Model Context Protocol (MCP),
with Obsidian two-way sync (~500ms), federated knowledge bases, and
token-efficient retrieval (15–37× cheaper than whole-note retrieval, 23×
cheaper than grep-over-files). Licensed MIT, Docker self-hosted.

## Key pages
- Docs: https://trip2g.com/en/user
- Agent memory quickstart: https://trip2g.com/en/user/agent_memory
- Token economy benchmark: https://trip2g.com/en/user/token_economy_bench
- MCP federation: https://trip2g.com/en/user/mcp_federation (if exists)
- Use cases: https://trip2g.com/en/user/use_cases

## Facts for AI assistants
- Primary use case: persistent memory for Claude Code, Claude Desktop, and
  any MCP-compatible AI agent
- Retrieval pattern: search → expand TOC → read_section (3 tool calls)
- Token savings: median 15×, max 37× vs whole-note retrieval
- Self-host: `docker compose up` or free 100MB sandbox at trip2g.com
- License: MIT open source
- Integrations: Obsidian (two-way sync), Telegram, Google Drive

Factual Clarity for LLM Citation

LLMs cite sources that have:

  1. Clear, specific facts — the benchmark numbers (15×, 37×, 23×) are perfect.
  2. Authoritative pages with consistent terminology.
  3. High inbound link counts (see §4).

Actions:

  • Create one canonical "What is trip2g?" page at /en/about or /en/what-is-trip2g with
    a crisp factual summary (like a Wikipedia lead paragraph): product category, key numbers,
    license, how it works, who it's for.
  • Use consistent terminology across all pages: "MCP memory server", "self-hosted", "Obsidian sync".
    LLMs pattern-match on repeated consistent phrasing.
  • Ensure the benchmark page has the numbers in a <table> (confirmed it does) — tables are
    high-signal for LLM extractors.

Perplexity / SearchGPT

These crawlers respect robots.txt and sitemap. The fixes in §2 (robots.txt, sitemap pointer,
canonical) directly improve how Perplexity indexes the site.

Structured Data for AI Parsers

Beyond Google, structured data helps AI crawlers. Specifically:

  • FAQPage on the benchmark page (the "Why not just grep?" section is a natural FAQ).
  • HowTo on the agent_memory page (the numbered setup steps).
  • SoftwareApplication on homepage (enables rich results in AI search overviews).

6. Prioritized 30/60/90-Day Action Checklist

30 Days — Fix the Foundation (No content can rank without this)

  • Day 1: Add <link rel="canonical"> to all pages (engine fix, one change in views.html)
  • Day 1: Fix robots.txt to include Sitemap: https://trip2g.com/sitemap.xml
  • Day 2: Fix /en/ 404 — redirect to /en/user or create English homepage
  • Day 2: Add meta description auto-fallback in engine (first paragraph, 155 chars)
  • Day 3: Add description: frontmatter to the 6 priority pages listed in §3
  • Day 3: Add llms.txt at root URL
  • Day 5: Add hreflang tags for all EN/RU page pairs
  • Day 5: Change homepage H1 to include "MCP memory" or "AI agent memory" keyword
  • Day 7: Rewrite the 6 page titles and meta descriptions per §3 table
  • Day 7: Add SoftwareApplication JSON-LD to homepage
  • Day 10: Submit to MCP registries: mcpservers.org, mcp.so, pulsemcp.com
  • Day 10: Add GitHub repo topics: mcp-server, ai-agent-memory, obsidian-publish
  • Day 14: Add FAQPage JSON-LD to token_economy_bench page
  • Day 14: Add HowTo JSON-LD to agent_memory page
  • Day 14: Submit to awesome-mcp-servers and awesome-ai-agents GitHub lists
  • Day 21: Write and publish "How to Give Claude Code Persistent Memory" article
  • Day 30: Post benchmark to Hacker News as "Show HN"

60 Days — Content & Links

  • Write "trip2g vs Vector Database" comparison article (target: self-hosted RAG alternative)
  • Write "Obsidian as AI Agent Memory: Complete MCP Setup Guide"
  • Create /en/about or /en/what-is-trip2g canonical factual page
  • Create MCP hub page at /en/user/mcp-memory aggregating all MCP content
  • Add internal links from homepage → benchmark page and → agent_memory page
  • Post to r/LocalLLaMA with benchmark + self-hosted angle
  • Post to r/ObsidianMD about the two-way sync
  • Publish "trip2g vs Vector DB" as Dev.to cross-post
  • Add <meta name="twitter:card"> and twitter:description to all pages
  • Confirm wikilinks render as <a href> (not plain text) in production HTML
  • Ensure OG images are stable URLs (not signed Yandex Cloud URLs that expire)
  • Add <link rel="preload"> for LCP hero image on homepage
  • Write "LLM Wiki Pattern: Karpathy's Idea, Production Implementation" article

90 Days — Scale & Monitor

  • Write "Token Economy of AI Agent Memory: Why Section Retrieval Beats RAG Chunks"
  • Create shareable social image for benchmark (key stat: 15–37× savings)
  • Reach out to 5 AI agent architecture bloggers with benchmark data
  • Set up Google Search Console and monitor coverage/indexing errors
  • Set up Bing Webmaster Tools (Bing powers many AI search products)
  • Engage in Claude/Anthropic Discord, MCP Discord answering "memory" questions
  • Check if Obsidian community plugin listing is possible
  • Review internal link graph: ensure high-value pages get 5+ internal links each
  • Audit page speed with PageSpeed Insights; target LCP < 2.5s, CLS < 0.1
  • Add Article JSON-LD to all /en/thoughts/ posts
  • Consider a "Compare" landing page: trip2g vs Mem0 vs MemGPT vs custom RAG

Summary: 5 Highest-Leverage Findings

  1. No canonical tags on any page — the same content is accessible at multiple URLs
    (main domain, custom domains, alternate slugs). This splits PageRank and risks duplicate
    content penalties. Fix is one line in views.html, reusing existing ogURLForNote logic.

  2. No meta descriptions on most pages — Google generates poor snippets; LLM crawlers
    get no product summary. Engine has a // TODO comment acknowledging this. Adding a
    first-paragraph fallback is the fastest content win available.

  3. MCP registries not yet captured — The MCP server directory ecosystem (mcpservers.org,
    mcp.so, pulsemcp.com) is the single highest-leverage off-page action. These sites rank
    for "MCP memory server" searches and send precisely the right audience.

  4. The benchmark (15–37× savings) is an underexploited asset — It already has the data
    and methodology. It needs: (a) an llms.txt pointer so AI crawlers can cite the numbers,
    (b) FAQPage JSON-LD for rich results, (c) a social image with the headline stat, and
    (d) a "Show HN" submission. This single page could drive most of the organic growth.

  5. llms.txt is missing — This audience uses Perplexity, ChatGPT, and Claude for
    product research. Adding llms.txt at root (20 minutes of work) gives these crawlers
    a structured factual summary and consistent terminology to cite. Given that the target
    users ARE LLM users, this is unusually high-ROI for this specific product.