MCP GitLab Server

gitlab_get_user_contributions_summary

Generate a summary of user contributions including commits, issues, and merge requests for performance reviews and team tracking.

Instructions

Summarize user's recent contributions across issues, MRs, and commits

Get a comprehensive overview of a user's activity and contributions over a specified time period, aggregating data from multiple sources.

Returns contribution summary including:

Commit statistics: count, additions, deletions
Issue activity: created, closed, commented
MR activity: created, merged, reviewed
Project involvement: active repositories
Trend analysis: activity patterns over time

Use cases:

Performance reviews and reports
Team contribution tracking
Identifying active contributors
Project health monitoring

Parameters:

user_id: Numeric user ID
username: Username string (use either user_id or username)
since: Start date for analysis (YYYY-MM-DD)
until: End date for analysis (YYYY-MM-DD)
project_id: Optional project scope filter

Example: Get user contributions for last month

{
  "username": "johndoe",
  "since": "2024-01-01",
  "until": "2024-01-31"
}

Input Schema

TableJSON Schema

Name	Required	Description
`user_id`	No	Numeric user ID
`username`	No	Username string
`since`	No	Start date for analysis (YYYY-MM-DD)
`until`	No	End date for analysis (YYYY-MM-DD)
`project_id`	No	Optional project scope filter

Implementation Reference

src/mcp_gitlab/tool_handlers.py:682-697 (handler)

Handler function that extracts parameters from arguments and delegates to GitLabClient.get_user_contributions_summary

def handle_get_user_contributions_summary(client: GitLabClient, arguments: Optional[Dict[str, Any]]) -> Dict[str, Any]:
    """Handle getting user contributions summary"""
    user_id = get_argument(arguments, "user_id")
    username = get_argument(arguments, "username")
    since = get_argument(arguments, "since")
    until = get_argument(arguments, "until")
    project_id = get_argument(arguments, "project_id")
    
    return client.get_user_contributions_summary(
        user_id=user_id,
        username=username, 
        since=since,
        until=until,
        project_id=project_id
    )

src/mcp_gitlab/tool_definitions.py:863-875 (schema)

Pydantic/MCP tool schema definition including input schema and description

types.Tool(
    name=TOOL_GET_USER_CONTRIBUTIONS_SUMMARY,
    description=desc.DESC_GET_USER_CONTRIBUTIONS_SUMMARY,
    inputSchema={
        "type": "object",
        "properties": {
            "username": {"type": "string", "description": "Username string"},
            "since": {"type": "string", "description": "Start date (YYYY-MM-DD)", "default": "30 days ago"},
            "until": {"type": "string", "description": "End date (YYYY-MM-DD)", "default": "today"}
        },
        "required": ["username"]
    }
),

src/mcp_gitlab/tool_handlers.py:1076-1076 (registration)
Registration of the tool name to its handler function in the TOOL_HANDLERS dictionary
```
TOOL_GET_USER_CONTRIBUTIONS_SUMMARY: handle_get_user_contributions_summary,
```
src/mcp_gitlab/constants.py:254-254 (helper)
Constant definition for the tool name
```
TOOL_GET_USER_CONTRIBUTIONS_SUMMARY = "gitlab_get_user_contributions_summary"
```

Tool Definition Quality

A3.9/5.0

Behavior3/5

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 describes the tool's function well and lists what data is returned, but doesn't mention potential limitations like rate limits, authentication requirements, data freshness, or error conditions. It adds value by specifying the aggregation scope but lacks operational details.

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 well-structured with clear sections (purpose, returns, use cases, parameters, example) and avoids unnecessary repetition. However, the 'Returns contribution summary including' section is somewhat verbose and could be more concise while maintaining clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only summary tool with 5 parameters and 100% schema coverage but no output schema, the description provides good context: it clearly explains what the tool does, what data it returns, use cases, and includes an example. The main gap is the lack of output schema, which the description partially compensates for by listing return data categories.

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 schema description coverage is 100%, so the schema already documents all parameters. The description adds minimal value beyond the schema by mentioning 'use either user_id or username' and providing an example, but doesn't explain parameter interactions, defaults, or constraints beyond what's in the schema descriptions.

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 clearly states the tool's purpose: 'Summarize user's recent contributions across issues, MRs, and commits' with a specific verb ('summarize') and resource ('user's contributions'). It distinguishes itself from siblings like gitlab_get_user_commits or gitlab_get_user_activity_feed by focusing on aggregated, multi-source summaries rather than individual activity streams.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool through 'Use cases' (performance reviews, team tracking, identifying contributors, project monitoring). However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools, such as gitlab_get_user_activity_feed for raw activity data instead of summaries.

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

Install Server

Other Tools

Latest Blog Posts

Tool Definition Quality Score (TDQS)
By punkpeye on April 3, 2026.
mcp
The Hackers Who Tracked My Sleep Cycle
By punkpeye on March 26, 2026.
security
Open Source Has a Bot Problem
By punkpeye on March 19, 2026.
open source

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Vijay-Duke/mcp-gitlab'

If you have feedback or need assistance with the MCP directory API, please join our Discord server