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toy-mcp-server

by kritikatripathi03
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TypeScript
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toy-mcp-server

A minimal Model Context Protocol (MCP) server built from scratch to learn how MCP actually works — no boilerplate generators, no templates, just the SDK.

Built as a first hands-on MCP project: two tools and one resource, wired into Claude Desktop over stdio.

What it does

MCP lets an LLM client (like Claude Desktop) call functions running on your own machine, instead of only generating text from its training data. This server exposes:

Tools (functions Claude can call):

  • roll_dice — rolls N dice with a configurable number of sides

  • flip_coin — flips a coin N times

Resources (read-only data Claude can fetch):

  • server-info — basic metadata about the server (name, purpose, start time)

Related MCP server: Random-Generator

Why this exists

LLMs can't access anything outside their own training data, and can't do anything by default — they only generate text. MCP is a standard way to give a model:

  • capability it doesn't have on its own (e.g. true randomness — LLMs are notoriously bad at picking random numbers themselves)

  • access to live or private data it has no way of knowing

This project is a small, safe sandbox for that idea before pointing an MCP server at something real (a database, an API with auth, etc.).

Tech stack

  • TypeScript

  • @modelcontextprotocol/sdk — official MCP SDK

  • zod — runtime schema validation for tool arguments

  • stdio transport (local subprocess communication with Claude Desktop)

Project structure

toy-mcp-server/
├── src/
│   └── index.ts       # server setup, tools, and resource
├── build/              # compiled output (git-ignored)
├── package.json
├── tsconfig.json
└── README.md

Setup

1. Install dependencies

npm install

2. Build

npm run build

3. Connect to Claude Desktop

Find your Claude Desktop config file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: usually under %LOCALAPPDATA%\Packages\<Claude package folder>\LocalCache\Roaming\Claude\claude_desktop_config.json — the exact path can vary by install method (Microsoft Store vs. direct installer). The reliable way to find it: open Claude Desktop → Settings → Developer → Local MCP servers → Edit Config, which opens the exact file the app reads.

Add this server under mcpServers (merge into the existing file rather than overwriting it):

{
  "mcpServers": {
    "toy-mcp-server": {
      "command": "node",
      "args": ["/absolute/path/to/toy-mcp-server/build/index.js"]
    }
  }
}

Replace the path with the actual absolute path to build/index.js on your machine. On Windows, escape backslashes (\\) in the JSON string.

4. Restart Claude Desktop

Fully quit (not just close the window) and reopen. Check Settings → Developer → Local MCP servers to confirm toy-mcp-server shows as connected.

5. Try it

In a chat, ask:

  • "Roll 3 six-sided dice"

  • "Flip a coin 10 times"

You should see a small tool-call indicator (e.g. "Roll Dice") above the response, confirming Claude actually invoked the function rather than guessing an answer.

How it works, briefly

  • McpServer — the object that declares the server's capabilities to any connecting client

  • registerTool(name, config, handler) — registers a callable function. config.inputSchema uses Zod to validate whatever arguments the model sends before the handler runs

  • registerResource(name, uri, config, handler) — registers read-only data addressable by a URI, fetched without arguments

  • StdioServerTransport — the wire format: Claude Desktop spawns this file as a subprocess and communicates over stdin/stdout using JSON-RPC. console.log is never used for logging here, since stdout is the actual protocol channel — console.error (stderr) is used instead

Next steps

  • Swap the toy tools for real ones hitting an actual database (Postgres via Prisma)

  • Add a GitHub-backed tool (e.g. list_open_prs) to practice token-based auth

  • Explore Streamable HTTP transport to host this remotely instead of running it locally

License

MIT

Available Tools

2 tools
flip_coinFlip CoinB

Flip a coin one or more times

ParametersJSON Schema
NameRequiredDescriptionDefault
timesNoNumber of flips

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of disclosing behavior. It merely says 'Flip a coin one or more times' without mentioning that the outcome is random, what the return format is (e.g., 'heads'/'tails'), or any side effects. The agent is left to guess the result structure.

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, efficient sentence that states the action and the parameter scope. There is zero wasted text, and the core information is front-loaded. It is appropriately concise for a trivial tool.

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 there is no output schema, the description should explain what the tool returns (e.g., results of each flip). It omits any mention of return values or randomness, leaving the agent with incomplete information to use the tool correctly. For a tool this simple, more context is still needed.

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

Parameters3/5

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

The single parameter 'times' is fully described in the schema ('Number of flips'), covering 100% of parameters. The tool description repeats the concept ('one or more times') but adds no extra meaning beyond what the schema already provides, so the baseline score of 3 applies.

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 states a specific verb and resource ('Flip a coin') and adds the scope 'one or more times', which distinguishes it from the sibling roll_dice by naming the object of the action. An agent can clearly identify what this tool does.

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 guidance is given about when to use this tool versus roll_dice. The description only states the action, leaving the agent to infer from the name that this is for coins rather than dice. No exclusions, prerequisites, or alternative-routing hints are provided.

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

roll_diceRoll DiceB

Roll one or more dice with a given number of sides

ParametersJSON Schema
NameRequiredDescriptionDefault
countNoNumber of dice to roll
sidesNoNumber of sides per die

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It does not mention that the result is random, that it is a safe non-mutating operation, or any other behavioral trait beyond the literal act of rolling. This is a significant gap for a tool whose entire purpose is random generation.

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 concise sentence with no waste. The core action is front-loaded. It is appropriately sized for a tool with only two parameters, though it could have been slightly more informative without losing conciseness.

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?

For a simple tool with two parameters fully documented in the schema, the description is reasonably complete. However, the lack of any mention of randomness or return behavior (even though no output schema is provided) leaves a small but notable gap. An agent might not know the outcome is random without prior knowledge.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents both parameters (count and sides) with defaults, minimums, and maximums. The description adds no semantic meaning beyond what the schema provides, which meets the baseline of 3.

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 states exactly what the tool does: 'Roll one or more dice with a given number of sides'. It uses a specific verb (roll) and resource (dice), and clearly distinguishes from the sibling flip_coin, since dice and coins are different objects.

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 guidance on when to use this tool versus the sibling flip_coin. The context implies dice vs coin, but the description does not explicitly state that this is for dice and flip_coin is for coins, nor does it mention any exclusions or alternative conditions.

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. 2 tool updatesv1.0.0
    • First observedflip_coin
    • First observedroll_dice
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TDQS

A3.6/5.0

Scored across 2 tools

Disambiguation5/5

The two tools, roll_dice and flip_coin, are completely distinct in their purpose and behavior. There is no overlap or ambiguity, making it trivial for an agent to select the correct tool.

Naming Consistency5/5

Both tool names follow the same verb_noun pattern (roll_dice, flip_coin), using snake_case and clear action-object structure. This is perfectly consistent and predictable.

Tool Count4/5

With only 2 tools, the server is minimal, but given its explicit 'toy' purpose and narrow domain (random chance events), this count is appropriate and focused. It is slightly thin by general standards but well-scoped for a toy.

Completeness4/5

The tool surface covers the two most common random generators (dice and coin flips). While a generic random number generator or card draw might be expected in a broader utilities server, for a toy server focused on simple chance, these tools are sufficient and leave no obvious dead ends.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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