Expense Tracker MCP Server
This server provides expense-tracking capabilities via MCP, along with simple demo utilities.
roll_dice: Roll one or more 6-sided dice and get the results.
add_numbers: Add two numbers together and return the sum.
add_expense: Add a new expense entry with date, amount, category, optional subcategory, and note.
list_expenses: List expense entries, optionally filtered by an inclusive date range.
summarize: Summarize expenses by category within a date range, optionally filtered by category.
expense:///categories resource: Retrieve the list of valid expense categories as JSON.
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., "@Expense Tracker MCP ServerRoll 3 dice and add the results together."
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.
Expense Tracker MCP Server
A Model Context Protocol (MCP) server built with FastMCP (v3.x) and Python, designed to provide tools and resources for expense tracking to MCP-compliant clients like Claude Desktop and the MCP Inspector.
📋 Prerequisites
Python: 3.10+ (tested with Python 3.14)
uv: Fast Python package installer and resolver (astral.sh/uv)
Node.js:
v22.xorv24.x(LTS) — Required for MCP Inspector UIClaude Desktop (optional): For testing tools directly within Claude
Related MCP server: Demo MCP Server
🚀 Setup & Installation
1. Initialize Project & Environment
# Initialize uv project (if starting fresh)
uv init .
# Install FastMCP
uv add fastmcp2. Prepare Node.js for MCP Inspector UI
The MCP Inspector (@modelcontextprotocol/inspector) requires Node.js v22.19.0+ or v24+. If using nvm-windows:
nvm install lts
nvm use 24Install the inspector globally or allow npx to fetch it:
npm install -g @modelcontextprotocol/inspector🛠️ Running & Development
Option A: Interactive Web UI (MCP Inspector)
To launch the MCP Inspector web UI with hot reloading:
uv run fastmcp dev inspector main.pyKeep this terminal window open.
The console will output a local URL with an authentication token:
MCP Inspector Web is up and running at: http://127.0.0.1:6274?MCP_INSPECTOR_API_TOKEN=<token>Open that link in your browser to test tools, inspect schemas, and view real-time request/response logs.
Option B: Run Server Directly (STDIO)
To run the MCP server in standard I/O mode:
uv run fastmcp run main.py(or uv run python main.py)
🤖 Connecting to Claude Desktop
1. Configuration File Location
On Windows, Claude Desktop's configuration is stored at:
%APPDATA%\Claude\claude_desktop_config.json(Full path: C:\Users\<YourUsername>\AppData\Roaming\Claude\claude_desktop_config.json)
2. Configuration Settings
Add the expense-server definition under mcpServers.
Note: On Windows, GUI applications like Claude Desktop often do not inherit the shell
PATH. Always specify the absolute path touv.exe.
{
"mcpServers": {
"expense-server": {
"command": "C:\\Users\\pyash\\AppData\\Local\\Python\\pythoncore-3.14-64\\Scripts\\uv.exe",
"args": [
"run",
"--directory",
"d:\\expense-tracker-mcp-server",
"fastmcp",
"run",
"main.py"
]
}
}
}3. Restart Claude Desktop
Completely close Claude Desktop (ensure it is quit from the Windows system tray near the clock).
Reopen Claude Desktop.
Check Settings > Developer to verify
expense-serveris connected.
🌐 Deployed MCP Endpoint
The server is deployed and accessible via HTTP / SSE:
Endpoint URL:
https://expense-tracker-ykp.fastmcp.app/mcp
🧰 Available Tools & Resources
Tools
Tool | Parameters | Description |
|
| Rolls |
|
| Adds two numbers together and returns the sum. |
|
| Adds a new expense entry to the database. |
|
| Lists expense entries, optionally filtered within an inclusive date range. |
|
| Summarizes expenses by category within an inclusive date range. |
Resources
URI | MIME Type | Description |
|
| Provides the list of valid expense categories. |
🔧 Troubleshooting
1. Unknown command "main.py". Available commands: inspector, apps.
In FastMCP 3.x, fastmcp dev is a command group. Use fastmcp dev inspector main.py instead of fastmcp dev main.py.
2. node:util does not provide an export named 'styleText'
Node.js is older than v20.12. Update to Node v22+ or v24+ via nvm install lts && nvm use 24.
3. Cannot find native binding (npm optional dependencies bug)
Corrupted npx cache from switching Node versions. Run:
npm cache clean --force
Remove-Item -Recurse -Force "$env:LOCALAPPDATA\npm-cache\_npx" -ErrorAction SilentlyContinue
npm install -g @modelcontextprotocol/inspectorAvailable Tools
2 toolsadd_numbersA
Add two numbers together.
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | ||
| b | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the operation but does not explicitly mention that it is pure, has no side effects, or what the return value is. While obvious for addition, it falls short of full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with zero filler. It is front-loaded and every word earns its place, exemplifying appropriate conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a trivial addition tool with a clear input schema and an output schema (as indicated by the signal), the description is largely sufficient. It lacks explicit mention of edge cases or preconditions, but none are expected for this operation, making it nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%—the description does not mention parameters by name or add any nuance. The schema already declares a and b as required numbers, but the description adds no value beyond that, failing to compensate for the low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Add two numbers together' uses a specific verb and resource, clearly stating exactly what the tool does. It is distinct from siblings like roll_dice and add_expense, so agents can easily differentiate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives, nor any exclusions. There is no context about when addition is appropriate or not, leaving the agent to infer usage from the name and schema alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
roll_diceA
Roll n_dice 6-sided dice and return the results.
| Name | Required | Description | Default |
|---|---|---|---|
| n_dice | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It correctly implies a random stochastic process and states the output ('return the results'), but does not explicitly mention randomness, the range of outcomes (1–6 per die), or that subsequent calls produce independent results. This is adequate for a simple tool but lacks any deeper context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the core action, and contains no filler words. Every term contributes to understanding the tool's behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (one parameter, simple output) and the presence of an output schema, the description is complete enough. It explains the parameter's role and the action's purpose. It could mention the non-deterministic nature more explicitly, but that is implicit for a dice-rolling tool, and the output schema likely covers return formatting.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero description coverage for the parameter n_dice, so the description must compensate. It does so by framing n_dice as the number of dice to roll, adding semantic meaning to an otherwise bare integer field. However, it does not specify constraints like minimum value or positivity, which would have improved clarity further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear, specific action: 'Roll n_dice 6-sided dice and return the results.' The verb 'Roll' and resource 'dice' are unambiguous, and the tool is easily distinguished from siblings like add_numbers or list_expenses, which handle arithmetic or expense management.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives, nor any exclusions or prerequisites. Sibling tools are unrelated, but the description provides no context for selection, leaving the agent to infer its suitability from the name alone.
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.
2 tool updates
v0.1.0- First observed
add_numbers - First observed
roll_dice
TDQS
Scored across 2 tools
The two tools have completely disjoint purposes—one rolls dice and the other adds numbers. There is no ambiguity between them and no chance of an agent misselecting one for the other.
Both tool names follow the same verb_noun snake_case pattern: roll_dice and add_numbers. The naming is predictable and straightforward.
A server named 'Expense Tracker' offers only two tools that perform unrelated operations (dice rolling and arithmetic). This is a severe mismatch between scope and purpose, making the count completely inappropriate.
The tools do not cover any expense tracking functionality whatsoever—no add expense, list expenses, delete expense, or summaries. The surface is entirely incomplete for the stated domain.
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