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Subscription Sniper — AI Agent for Recurring Spend Audits

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Subscription Sniper — AI Agent for Recurring Spend Audits

Subscription Sniper is an MCP-powered AI agent that audits your Gmail for recurring subscriptions, detects silent price increases, and flags services you're…

Model Context Protocol Built with Nitrostack Status

Subscription Sniper — AI Agent for Recurring Spend Audits is an MCP (Model Context Protocol) server that extends AI assistants — like Claude, Cursor, and any MCP-compatible client — with new, real-world capabilities. It is built and deployed on Nitrostack, the fastest way to build, deploy, and share MCP apps.

Table of Contents

Related MCP server: zombie-killer

Overview

Subscription Sniper is an MCP-powered AI agent that audits your Gmail for recurring subscriptions, detects silent price increases, and flags services you're paying for but no longer use. Instead of guessing, it reasons through real inbox signals — then hands you a safe, manual step-by-step guide to cancel autopay on PhonePe, GPay, or Paytm. One question in chat, a full financial audit out.

What is MCP?

The Model Context Protocol (MCP) is an open standard that lets AI assistants securely connect to external tools, data sources, and services. Instead of being limited to what it was trained on, an AI model can call MCP servers to fetch live data, run actions, and integrate with real systems.

This project is one such MCP server. Learn more about building and shipping MCP apps at nitrostack.ai.

Features

  • 🔌 MCP-native — works with any MCP-compatible client (Claude, Cursor, and more)

  • 🛠️ Tools, resources & prompts — exposes structured capabilities to AI agents

  • ⚡ Deployed on Nitrostack — reliable, hosted, and instantly shareable

  • 🔐 Secure by design — secrets stay in environment variables, never in code

  • 🧩 Composable — combine with other MCP apps to build powerful AI workflows

Live Demo

🚀 Live MCP endpoint: https://subscription-snip-six-seven-amrita-university-amritapuri-campus.app.nitrocloud.ai

Point your MCP client at the endpoint above to try it instantly. Prefer a hosted setup? Deploy your own in minutes on Nitrostack.

Getting Started

Prerequisites

  • Node.js 18+ (or your project runtime)

  • An MCP-compatible client (Claude Desktop, Cursor, etc.)

Installation

git clone https://github.com/your-username/your-mcp-project.git
cd subscription-sniper-ai-agent-for-recurring-spend-audits
npm install

Configuration

Copy the example environment file and add your own values:

cp .env.example .env

Run

npm run start

Connect to an MCP Client

Add this server to your MCP client configuration. A typical entry looks like:

{
  "mcpServers": {
    "subscription-sniper-ai-agent-for-recurring-spend-audits": {
      "url": "https://subscription-snip-six-seven-amrita-university-amritapuri-campus.app.nitrocloud.ai"
    }
  }
}

Restart your client and the tools from this MCP server will be available to your AI assistant.

Deploy Your Own MCP App

Want to build and ship an MCP server like this one? Nitrostack lets you create, deploy, and host MCP apps in minutes — no infrastructure to manage.

👉 Start building: https://nitrostack.ai

Explore More MCP Apps

  • 🌙 Discover and share MCP projects with the community on r/mcptothemoon

  • 🧰 Browse a growing catalog of MCP apps on Nitrostack

FAQ

What is an MCP server?

An MCP server implements the Model Context Protocol to expose tools, resources, and prompts that AI assistants can call. It lets an AI model take real actions and access live data.

What does Subscription Sniper — AI Agent for Recurring Spend Audits do?

Subscription Sniper is an MCP-powered AI agent that audits your Gmail for recurring subscriptions, detects silent price increases, and flags services you're…

Which AI clients does this work with?

Any MCP-compatible client, including Claude Desktop and Cursor. New clients are adding MCP support regularly.

How do I deploy my own MCP app?

Use Nitrostack to build, deploy, and host MCP apps without managing infrastructure.

Keywords

BFSI & FinTech · Subscription Sniper — AI Agent for Recurring Spend Audits · MCP · Model Context Protocol · MCP server · MCP app · AI tools · AI agents · LLM tools · Claude MCP · Nitrostack · deploy MCP server · build MCP app

License

MIT © 2026

Built with ❤️ using the Model Context Protocol on Nitrostack. Share your MCP app on r/mcptothemoon.

Available Tools

3 tools
calculate_financial_metricsA

Calculate total monthly and annual spend, potential savings from no-engagement subscriptions, and detected price increases.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description must carry the burden. It only lists outputs without disclosing whether the tool is read-only, requires permissions, or has side effects. For a calculation tool, additional context about data dependencies or error handling would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that communicates the core functionality without extraneous words. Every phrase adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description provides minimal information about the return format. The tool's simplicity (zero parameters) makes the description adequate, but it could list the specific metrics returned or note any limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has zero parameters, achieving 100% coverage. The description adds no parameter info but implicitly explains what the tool returns. With no parameters, a baseline of 4 is appropriate, as the description clarifies the output beyond just the tool name.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool computes total monthly/annual spend, savings, and price increases. The verb 'calculate' and resource 'financial metrics' are specific. Sibling tools like 'check_engagement_signals' and 'generate_cancellation_playbook' have distinct focuses, so the tool's purpose is well-differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No usage guidelines are provided. The description does not mention when to use this tool, prerequisites, or when alternatives might be better. This forces the agent to infer context from the tool name and siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

check_engagement_signalsB

Search recent non-billing mock emails from a sender to classify a subscription as HIGH_ENGAGEMENT or NO_ENGAGEMENT.

ParametersJSON Schema
NameRequiredDescriptionDefault
service_nameYesSubscription service name to check for recent non-billing activity

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states a search and classification action but does not clarify whether the operation is read-only, destructive, or what side effects (if any) occur. The terms 'recent' and 'mock emails' are ambiguous, lacking details on time windows or the nature of such emails.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the action and outcome. Every word contributes meaning; there is no redundancy or irrelevant detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema, the description should clarify how the classification is returned (e.g., as a string, a boolean, or a side effect). It fails to define what constitutes 'recent' (e.g., emails within the last 30 days) and what 'mock emails' implies. These gaps make the description incomplete for an agent to reliably invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes the single parameter 'service_name' with a clear description. The tool description adds valuable context by specifying 'mock emails' and the binary classification result (HIGH_ENGAGEMENT or NO_ENGAGEMENT), which enriches the parameter's meaning beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action: search recent non-billing mock emails from a sender, and the outcome: classify subscription as HIGH_ENGAGEMENT or NO_ENGAGEMENT. This verb+resource combination is specific and distinct from sibling tools which handle financial metrics or cancellation playbooks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus alternatives, such as when to check engagement before cancellation or financial analysis. It does not mention prerequisites, scenarios to avoid, or references to sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_cancellation_playbookA

Generate a markdown guide for cancelling autopay on PhonePe, GPay, or Paytm for a specific subscription service.

ParametersJSON Schema
NameRequiredDescriptionDefault
service_nameYesSubscription service name to generate cancellation instructions for

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description is the sole source of behavioral traits. It states the tool generates a guide, implying a read-only operation, but does not explicitly mention whether it modifies anything, requires authentication, or has any side effects. The description is adequate but lacks explicit disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that conveys the core functionality without unnecessary words. It is front-loaded with the action and resource. Could potentially include more detail, but it is efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has a single parameter, no output schema, and no annotations, the description sufficiently explains the purpose and output (a markdown guide). It covers what the tool does and what it returns, making it usable for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'service_name' has full schema coverage with a description. The tool description adds context by listing the platforms (PhonePe, GPay, Paytm) for which the guide is generated, which clarifies the expected input and adds value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a markdown guide for cancelling autopay on specific platforms (PhonePe, GPay, Paytm) for a given subscription service. The verb 'Generate' and resource 'markdown guide' are specific. However, the role of platforms vs. service name in the input could be clarified, slightly reducing clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs. alternatives. Sibling tools are unrelated, so there is no confusion, but the description does not specify conditions or prerequisites for using the tool (e.g., when a user wants cancellation steps). Usage is implied but not stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv1.0.0
    • First observedcalculate_financial_metrics
    • First observedcheck_engagement_signals
    • First observedgenerate_cancellation_playbook
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored across 3 tools

Disambiguation5/5

Each tool addresses a distinct aspect of subscription auditing: engagement detection, financial analysis, and cancellation guidance. No overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (check_, calculate_, generate_), making them predictable and easy to understand.

Tool Count4/5

Three tools is slightly lean for a comprehensive audit agent, but they cover the core workflow (detect, analyze, act) without unnecessary redundancy.

Completeness3/5

Missing a tool to discover or list subscriptions from data sources (e.g., email or accounts). The current set requires the agent to already know the subscription, limiting automated audit.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources

Related MCP Connectors

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