varta-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| API_KEY | Yes | Your Varta API key (Bearer token) for authentication. Obtain from https://isitaspam.com/developers. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| check_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. |
| 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. |
| 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.