Skip to main content
Glama
jamesbrink

MCP Server for Coroot

get_deployments_overview

Track recent deployments to monitor success rates, rollback history, and performance impact for project oversight.

Instructions

Get deployment tracking overview.

Returns information about recent deployments:

  • Deployment timeline

  • Success/failure rates

  • Rollback history

  • Impact on application performance

Args: project_id: Project ID query: Search/filter query (optional)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
queryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Primary MCP tool handler function registered with @mcp.tool(). Handles tool execution, input validation via type hints/docstring, error handling, and delegates to the implementation helper which calls the Coroot client.
    @mcp.tool()
    async def get_deployments_overview(
        project_id: str,
        query: str | None = None,
    ) -> dict[str, Any]:
        """Get deployment tracking overview.
    
        Returns information about recent deployments:
        - Deployment timeline
        - Success/failure rates
        - Rollback history
        - Impact on application performance
    
        Args:
            project_id: Project ID
            query: Search/filter query (optional)
        """
        return await get_deployments_overview_impl(project_id, query)  # type: ignore[no-any-return]
  • Internal helper implementation that calls the CorootClient to fetch deployments overview data and formats the response.
    @handle_errors
    async def get_deployments_overview_impl(
        project_id: str,
        query: str | None = None,
    ) -> dict[str, Any]:
        """Get deployments overview."""
        overview = await get_client().get_deployments_overview(project_id, query)
        return {
            "success": True,
            "overview": overview,
        }
  • CorootClient utility method that makes the HTTP API request to retrieve deployments overview from the Coroot server.
    async def get_deployments_overview(
        self,
        project_id: str,
        query: str | None = None,
    ) -> dict[str, Any]:
        """Get deployments overview.
    
        Args:
            project_id: Project ID.
            query: Search/filter query.
    
        Returns:
            Deployments overview data.
        """
        params = {}
        if query:
            params["query"] = query
    
        response = await self._request(
            "GET",
            f"/api/project/{project_id}/overview/deployments",
            params=params,
        )
        data: dict[str, Any] = response.json()
        return data
  • FastMCP decorator that registers the get_deployments_overview tool with the MCP server, auto-generating schema from signature and docstring.
    @mcp.tool()
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 states the tool returns information, implying a read-only operation, but doesn't address permissions, rate limits, data freshness, or error handling. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 and appropriately sized, with a clear purpose statement followed by bullet points of returned information and a concise 'Args' section. Every sentence adds value, though the bullet points could be slightly more streamlined.

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 moderate complexity, no annotations, and an output schema (which reduces the need to describe return values), the description is partially complete. It covers the purpose and parameters but lacks usage guidelines and behavioral details, making it adequate but with clear gaps for effective agent use.

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 description adds meaningful context for parameters beyond the schema, which has 0% description coverage. It explains 'project_id' as 'Project ID' and 'query' as 'Search/filter query (optional)', clarifying their roles. However, it doesn't detail the format or examples for 'query', leaving some ambiguity for the agent.

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 'Get deployment tracking overview' and specifies what information is returned (deployment timeline, success/failure rates, etc.), making the verb+resource combination explicit. However, it doesn't distinguish this from sibling tools like 'get_applications_overview' or 'get_nodes_overview' that follow similar patterns, missing sibling differentiation.

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. It doesn't mention sibling tools like 'get_applications_overview' or 'get_project_status' that might overlap in context, nor does it specify prerequisites or exclusions for usage, leaving the agent without contextual direction.

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

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/jamesbrink/mcp-coroot'

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