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LGTM MCP Server

by dongri
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

mcp-server-lgtm MCP Server

A Model Context Protocol server for LGTM images

This is a TypeScript-based MCP server that interacts with the LGTM API. It provides a tool to fetch random LGTM (Looks Good To Me) images that can be used in code reviews and other developer communications.

Features

Create server

npx @modelcontextprotocol/create-server mcp-server-lgtm

Tools

  • get_lgtm - Fetch a random LGTM image

    • Returns markdown code for embedding the image

    • Provides the direct image URL for use in various contexts

Related MCP server: mcp-dogceo

Development

Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

Dev Installation

To use with Claude Desktop, add the server config:

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "mcp-server-lgtm": {
      "command": "/path/to/mcp-server-lgtm/build/index.js"
    }
  }
}

Installing via Smithery

To install mcp-server-lgtm MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @dongri/mcp-server-lgtm --client claude

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

Smithery

https://smithery.ai/server/@dongri/mcp-server-lgtm

Available Tools

1 tool
get_lgtmB

Get LGTM image and show markdown code and imageurl.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

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 the full burden of behavioral disclosure. It mentions output types (markdown code and imageurl) but lacks details on rate limits, authentication needs, error handling, or whether it's a read-only operation. This leaves significant gaps in understanding the tool's behavior.

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 front-loads the core action ('Get LGTM image') and specifies the output format. There is no wasted text, making it highly concise and well-structured for its 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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose and output format, but lacks details on behavioral aspects like error handling or usage context, which could enhance completeness 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 input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately doesn't discuss parameters, aligning with the schema's completeness, which justifies a baseline score of 4 for this dimension.

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: 'Get LGTM image' specifies the verb and resource, while 'show markdown code and imageurl' indicates the output format. It's specific about what the tool does, though without sibling tools, differentiation isn't applicable.

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, such as what triggers its use, prerequisites, or alternative scenarios. It simply states what the tool does without context for its application.

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. 1 tool updatev0.1.0
    • First observedget_lgtm
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TDQS

B3.3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'get_lgtm' has a clearly defined and distinct purpose, so an agent cannot misselect between non-existent alternatives.

Naming Consistency5/5

The naming follows a consistent verb_noun pattern with 'get_lgtm', and since there is only one tool, there is no inconsistency to evaluate. The naming is clear and predictable for the single tool present.

Tool Count2/5

A single tool is too few for most server purposes, as it limits functionality and suggests a very narrow scope. While it might be appropriate for a trivial utility, it feels thin and underdeveloped for a server named 'LGTM MCP Server', which implies more comprehensive LGTM-related operations.

Completeness2/5

The server is severely incomplete for handling LGTM operations. With only a 'get_lgtm' tool, there are obvious gaps such as creating, updating, deleting, or listing LGTMs, and no coverage of related workflows. This will likely cause agent failures when more complex tasks are required.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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