varta-mcp
This server lets AI agents classify messages for spam and pull spam-related statistics and examples from isitaspam.com.
Classify a message (
check_spam_text): submit text (up to 5,000 chars, optional ISO 639-1 locale hint) and get a verdict of SPAM / SUSPICIOUS / SAFE plus confidence (0–1), category, risk signals, red flags, and a plain-language recommended action.Get daily spam stats (
get_spam_stats): total checks today and how many were classified SPAM vs SUSPICIOUS (cached 60s).Get recent spam examples (
get_spam_examples): the 5 latest public spam/scam catches with verdict, category, and a link to full analysis.Use it hosted or locally: connect over SSE/HTTP with a Bearer API key (free tier 50 checks/day; paid tiers up to 10,000/day), or run the bundled stdio entry point locally.
initializeandtools/listwork without a valid key so registries can introspect the server.
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., "@varta-mcp classify this message: "Claim your prize now!""
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.
varta-mcp
Project context: I built Varta for my own use and shared this MCP integration so others could use it too. The service runs largely autonomously, with automated checks and maintenance and occasional additions from me. My full focus is Deep In, where I am co-founder and Product / AI Lead.
Spam classification MCP server for AI agents — built from real Telegram moderation data.
Live at: https://mcp.getvarta.com/sse
Get a free API key: https://isitaspam.com/developers
Tools
Tool | Description |
| Classify a message as SPAM / SUSPICIOUS / SAFE. Returns verdict, confidence (0–1), category, risk signals, red flags, and plain-language recommendation. |
| Daily spam classification statistics from isitaspam.com. |
| Five most recent public spam catches with links to full analysis. |
Related MCP server: Straight Connect
Add to Claude Code
claude mcp add varta \
--transport sse \
https://mcp.getvarta.com/sse \
--header "Authorization: Bearer varta_YOUR_KEY"Get a free key (50 checks/day) at https://isitaspam.com/developers — instant, no credit card.
Run locally (stdio)
The hosted server above is the easiest path. If you'd rather run the server as a local process — or your MCP client only speaks stdio — use the bundled stdio entry point. Same three tools, no dependencies, Node 18+.
git clone https://github.com/DarynaFor/mcp-varta.git
cd mcp-varta
API_KEY=varta_YOUR_KEY node src/stdio.jsOr wire it into a client config:
{
"mcpServers": {
"varta": {
"command": "node",
"args": ["/path/to/mcp-varta/src/stdio.js"],
"env": { "API_KEY": "varta_YOUR_KEY" }
}
}
}initialize and tools/list work without a valid key, so registries and clients
can introspect the server before you have one.
Example
curl -s -X POST https://mcp.getvarta.com/messages \
-H "Content-Type: application/json" \
-H "Authorization: Bearer varta_YOUR_KEY" \
-d '{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "check_spam_text",
"arguments": {
"text": "Earn $5000/week from home! Click: bit.ly/abc123"
}
}
}'Response:
**Verdict: SPAM** (97% confidence)
**Category:** financial scam
**Why flagged:**
- Unrealistic income claim ($5000/week)
- Shortened URL hides real destination
**Recommended action:** Delete message and ban sender.
**Source:** isitaspam.com/check/financial_scam-earn-5000-week-a1b2c3d4How it works
The classifier runs 7-layer detection:
Fast pre-classifier (keyword + pattern signals)
Multi-LLM consensus (GPT + Claude + Gemini) for ambiguous cases
Vector similarity against 1,100+ verified scam patterns
URL safety check (Google Safe Browsing + PhishTank)
Crypto scam signal detection
Language detection (33 languages)
Stylometric analysis (urgency density, caps ratio, mixed-script patterns)
Data comes from 51 live Telegram groups protecting 29,000+ members via Varta — a Telegram anti-spam SaaS.
Endpoints
Endpoint | Transport |
| SSE (Claude Code |
| HTTP ( |
| Info / health check |
Auth
Pass your API key as a Bearer token:
Authorization: Bearer varta_YOUR_KEYFree tier: 50 checks/day
Paid tiers: 2,000/day ($19/mo) · 10,000/day ($49/mo) — see https://isitaspam.com/developers#pricing
Rate limits & errors
Rate limiting is applied by isitaspam.com. On 429, the error body includes reset_at and upgrade_url.
Code | Meaning |
| Bearer token not recognised |
| Daily or burst limit hit |
| Message exceeds 5,000 chars |
REST API
If you prefer REST over MCP, the same keys work on isitaspam.com/api/check via X-API-Key header. Full OpenAPI 3.1 spec: https://isitaspam.com/openapi.yaml
License
MIT
Available Tools
3 toolscheck_spam_textA
Classify a text message as SPAM, SUSPICIOUS, or SAFE using multi-LLM consensus (GPT + Claude + Gemini) and vector similarity against 1,100+ verified scam patterns. Returns verdict, confidence, category, risk signals, red flags, and plain-language recommendation. Built from real Telegram moderation data.
Inputs: text (required), locale.
get_spam_examplesA
Get the 5 most recent public spam/scam examples caught by the isitaspam.com classifier. Each result includes verdict, category, and a link to the full analysis.
Inputs: no named parameters.
get_spam_statsA
Get daily spam classification statistics from isitaspam.com — total checks today, how many were classified as SPAM, how many as SUSPICIOUS. Cached 60 seconds.
Inputs: no named parameters.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: check_spam_text classifies a single message, get_spam_stats provides aggregate counts, and get_spam_examples shows recent samples. There is no overlap in functionality or confusion about which tool to use for a given task.
All tool names follow a consistent verb_noun pattern: check_spam_text, get_spam_stats, get_spam_examples. The verbs (check, get) and nouns (text, stats, examples) are clear and predictable.
With three tools, the server is on the lower end of the ideal range, but it is well-scoped for a narrow spam-classification utility. Each tool serves a distinct need, and the count feels appropriate rather than sparse.
The core functionality (classifying a text) is covered, along with supporting statistical and example retrieval. Minor gaps exist (e.g., batch processing or category filtering), but these are not essential for the stated purpose and can be worked around.
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