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v4lheru

generic-mcp-server

by v4lheru
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TypeScript

Generic MCP Server Template

A flexible template for creating Model Context Protocol (MCP) servers using the official TypeScript SDK.

What is MCP?

The Model Context Protocol (MCP) enables communication between AI assistants and locally running servers that provide additional tools and resources.

Related MCP server: Swagger MCP

Features

  • Official SDK: Built with @modelcontextprotocol/sdk

  • Type Safety: Full TypeScript support with Zod validation

  • Stdio Transport: Standard transport for local agent integration

  • Modular Design: Easy to add new tools and resources

Getting Started

Prerequisites

  • Node.js (v16 or later)

  • npm or yarn

Installation

  1. Clone the repository:

    git clone https://github.com/v4lheru/generic-mcp-template.git
cd generic-mcp-server

  2. Install dependencies:

    npm install

  3. Build the project:

    npm run build

Usage

To run the server locally (for testing or development):

npm run start

Note: This server uses stdio transport, so it expects to communicate via standard input/output. It will not start an HTTP server.

Adding to Claude Desktop

  1. Open Claude Desktop config (e.g. ~/Library/Application Support/Claude/claude_desktop_config.json on macOS).

  2. Add your server:

{
  "mcpServers": {
    "my-server": {
      "command": "node",
      "args": ["/path/to/generic-mcp-server/dist/index.js"]
    }
  }
}

Customization

  • Tools: Add new tools in src/tools.ts.

  • Resources: Add new resources in src/resources.ts.

  • Config: Update src/config.ts for environment variables.

License

MIT

Available Tools

2 tools
calculate-sumB

Calculates the sum of two numbers

ParametersJSON Schema
NameRequiredDescriptionDefault
aYesThe first number
bYesThe second number

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'calculates' implies a read-only operation, the description doesn't address important behavioral aspects like error handling (e.g., overflow, invalid inputs), performance characteristics, or whether the operation has side effects. For a tool with zero annotation coverage, this represents a significant gap in behavioral context.

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 perfectly concise at just one sentence that directly states the tool's function. There's zero wasted language, no unnecessary elaboration, and the information is front-loaded effectively. Every word earns its place in this minimal but complete statement of purpose.

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 the tool's low complexity (simple arithmetic operation), 100% schema coverage, and no output schema, the description is minimally adequate. However, it lacks information about return values (though no output schema exists), error conditions, or limitations (e.g., numeric range constraints). For a basic calculation tool, this represents the minimum viable description but with clear gaps in completeness.

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%, with both parameters 'a' and 'b' clearly documented as 'the first number' and 'the second number' respectively. The description adds no additional parameter semantics beyond what the schema already provides. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no parameter information in the description.

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's purpose with a specific verb ('calculates') and resource ('sum of two numbers'). It distinguishes from the sibling tool 'get-weather' by focusing on mathematical calculation rather than weather data retrieval. However, it doesn't explicitly differentiate from potential mathematical sibling tools, which prevents a perfect score.

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 guidance on when to use this tool versus alternatives. There's no mention of when-not-to-use scenarios or comparison with other calculation tools. The only implied usage is for summing two numbers, but this is basic and doesn't help an agent make informed decisions about tool selection.

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

get-weatherC

Get weather forecast for a city

ParametersJSON Schema
NameRequiredDescriptionDefault
cityYesThe city name

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Get weather forecast' but doesn't specify details like data freshness, rate limits, error handling, or authentication needs. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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 the lack of annotations and output schema, the description is incomplete. It doesn't explain what the weather forecast includes (e.g., temperature, conditions), potential limitations, or return format. For a tool with no structured behavioral data, more context is needed to fully understand its operation.

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 input schema has 100% description coverage, with the 'city' parameter fully documented. The description implies the parameter's use ('for a city') but adds no additional meaning beyond what the schema provides, such as format examples or constraints. This meets the baseline for high schema coverage.

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's purpose with a specific verb ('Get') and resource ('weather forecast for a city'), making it immediately understandable. However, it doesn't differentiate from sibling tools, as 'calculate-sum' is unrelated, so no sibling distinction is needed or provided.

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 guidance on when to use this tool versus alternatives, such as other weather-related tools or data sources. It only states what the tool does, without context for its application or prerequisites.

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. Dates show when Glama detected each change.

  1. 2 tool updates
    • First observedcalculate-sum
    • First observedget-weather
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TDQS

C2.9/5.0
Disambiguation5/5

The two tools have completely distinct purposes: calculate-sum performs a mathematical operation on numbers, while get-weather retrieves weather data for a city. There is no overlap or ambiguity between these functions, making it impossible for an agent to confuse them.

Naming Consistency3/5

The tool names use a verb-noun pattern (calculate-sum, get-weather), which is consistent in structure. However, they use different verb styles (calculate vs. get) and a hyphen delimiter, which is readable but shows minor deviation in convention.

Tool Count2/5

With only 2 tools, the server feels thin and under-scoped for a generic purpose. This minimal set lacks coverage for typical operations in any specific domain, making it inappropriate for most agent workflows.

Completeness1/5

The tool surface is severely incomplete for a generic server. There are major gaps in functionality, such as missing basic CRUD operations, data processing tools, or domain-specific coverage, which will likely cause agent failures in practical use.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

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