Gitignore MCP Tool
Allows for the generation of .gitignore files to help manage untracked files in Git repositories.
Interfaces with the gitignore.io API to browse templates and generate standardized .gitignore files for various projects.
Connects to the Toptal-hosted gitignore.io API service to fetch and generate environment-specific .gitignore configurations.
Click on "Install 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., "@Gitignore MCP Toolcreate a .gitignore for python, visualstudiocode, and macos"
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.
A MCP server for generating
.gitignorefiles using gitignore.io API
π Features
Gitignore Generation: Generate
.gitignorefilesTemplate Listing: Browse all available gitignore templates
Related MCP server: GitLab MCP Server
π οΈ Getting Started
Environment Variables
Variable | Default | Description |
|
| Base URL for gitignore.io API |
Local Development
Install dependencies:
uv sync(Optional) Set custom API endpoint:
export GITIGNORE_API_BASE="https://your-custom-api.com/api"Run the MCP server:
uv run --with fastmcp fastmcp run mcp_tools/main.pyDocker
Build the Docker image:
docker build -t mcp-gitignore:latest .Run the container:
docker run -i --rm -p 8000:8000 mcp-gitignore:latestRun with custom API endpoint:
docker run -i --rm -p 8000:8000 -e GITIGNORE_API_BASE="https://your-custom-api.com/api" mcp-gitignore:latestRun MCP Server:
{
"mcpServers": {
"mcp-gitignore": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-p",
"8000:8000",
"mcp-gitignore:latest"
],
"env": {
"GITIGNORE_API_BASE": "https://www.toptal.com/developers/gitignore/api"
}
}
}
}π― API Endpoints
List Templates
GET /gitignore/templates/
List all available gitignore templates.
Generate .gitignore
POST /gitignore/generate/
Generate .gitignore file based on templates.
Request Body:
{
"templates": ["python", "visualstudiocode", "macos"],
"output_path": ".gitignore"
}π Documentation
Documentation is built using MkDocs and deployed to GitHub Pages.
To build the documentation locally:
chmod +x scripts/build_docs.sh
scripts/build_docs.sh
mkdocs buildAvailable Tools
2 toolsgenerate_gitignoreC
Generate a .gitignore file based on specified templates and save it to the specified path
Responses:
200 (Success): Successful Response
Content-Type:
application/jsonResponse Properties:
success: Whether the operation was successful
message: Status message
content: Generated .gitignore content
Example:
{
"success": true,
"message": "string",
"content": "string"
}422: Validation Error
Content-Type:
application/jsonResponse Properties:
Example:
{
"detail": [
"unknown_type"
]
}| Name | Required | Description | Default |
|---|---|---|---|
| templates | Yes | List of template names to generate .gitignore for (e.g., 'python', 'node', 'visualstudiocode') |
Output Schema
| Name | Required | Description |
|---|---|---|
| content | Yes | Generated .gitignore content |
| message | Yes | Status message |
| success | Yes | Whether the operation was successful |
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 mentions saving to a path but doesn't specify where (e.g., current directory, absolute path), file overwriting behavior, error handling, or rate limits. The response format is detailed in the description, but behavioral traits like side effects or constraints are under-specified for a tool that creates files.
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 front-loaded with the core purpose in the first sentence, but it includes extensive response documentation (200 and 422 cases with examples) that duplicates information likely in the output schema. This adds length without earning its place, as the output schema should cover response structures. The core description is concise, but the extra details reduce efficiency.
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 tool's moderate complexity (1 parameter, file generation), the description covers the basic action and includes response details. With an output schema present (implied by 'Has output schema: true'), the description doesn't need to explain return values extensively. However, it lacks context on file system interactions and sibling tool integration, leaving minor gaps.
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 100%, with the 'templates' parameter well-documented in the schema (description, example). The tool description doesn't add any parameter semantics beyond what's in the schema, such as explaining template interactions or validation rules. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the heavy lifting.
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 clearly states the tool's purpose: 'Generate a .gitignore file based on specified templates and save it to the specified path.' It includes a specific verb ('Generate'), resource ('.gitignore file'), and key actions (based on templates, save to path). However, it doesn't explicitly differentiate from its sibling 'list_gitignore_templates' beyond the obvious functional difference.
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?
No explicit guidance on when to use this tool versus alternatives is provided. The description mentions the action but doesn't clarify prerequisites, when-not-to-use scenarios, or how it relates to the sibling tool 'list_gitignore_templates' (e.g., whether templates should be listed first). Usage is implied from the purpose but lacks explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_gitignore_templatesB
List all available gitignore templates from gitignore.io
Responses:
200 (Success): Successful Response
Content-Type:
application/jsonResponse Properties:
templates: List of available gitignore templates
Example:
{
"templates": [
"string"
]
}| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| templates | Yes | List of available gitignore templates |
TDQS
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 this is a read operation ('List') and includes HTTP response details (200 success, JSON format, example), which adds useful context beyond basic functionality. However, it doesn't mention potential errors, rate limits, authentication needs, or other behavioral traits that might be relevant for an agent.
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 reasonably concise but includes unnecessary formatting details (HTTP response codes, content-type, example) that might be better handled by an output schema. The core purpose is stated upfront, but the response documentation adds bulk without proportional value for tool selection. It could be more streamlined for an agent's needs.
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 tool's simplicity (0 parameters, read-only operation, output schema provided), the description is mostly complete. It explains what the tool does and shows an example response. The output schema handles return values, so the description doesn't need to detail them. However, it lacks context about when to use it relative to the sibling tool, which is a minor gap.
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 tool has 0 parameters, and schema description coverage is 100% (empty schema is fully documented). The description doesn't need to explain parameters, so it meets the baseline expectation. No additional parameter information is provided or required, which is appropriate for a parameterless tool.
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 clearly states the tool's purpose: 'List all available gitignore templates from gitignore.io'. It specifies the verb ('List'), resource ('gitignore templates'), and source ('gitignore.io'), making the action unambiguous. However, it doesn't explicitly differentiate from its sibling 'generate_gitignore', which would be needed for 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.
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. It mentions the sibling tool 'generate_gitignore' in the context signals, but the description itself doesn't explain the relationship (e.g., use this to see available templates before generating one). There's no mention of prerequisites, limitations, or typical use cases.
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.
2 tool updates
v0.1.0- First observed
generate_gitignore - First observed
list_gitignore_templates
TDQS
The two tools have completely distinct purposes with no overlap. 'generate_gitignore' creates a file based on templates, while 'list_gitignore_templates' provides the available template list. An agent would never confuse these tools as they serve different stages of the workflow.
Both tools follow a perfect verb_noun pattern with consistent snake_case naming. 'generate_gitignore' and 'list_gitignore_templates' use clear, descriptive verbs that match their actions, creating a predictable naming convention throughout the tool set.
With only 2 tools, this server feels under-scoped for a gitignore management system. While the tools cover basic functionality, there are obvious missing operations like updating existing gitignore files, deleting them, or managing custom templates that would make the surface more complete.
The tools provide core read and generate functionality but lack full lifecycle coverage. There's no way to update or delete generated gitignore files, and while templates can be listed, there's no ability to manage them. The surface covers basic workflows but leaves significant gaps for more complex scenarios.
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