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jamesbrink

MCP Server for Coroot

get_traces_overview

Analyze distributed tracing data to monitor trace volume, error rates, latency percentiles, and service dependencies across applications.

Instructions

Get distributed tracing overview.

Returns a summary of distributed traces across all applications:

  • Trace volume and trends

  • Error rates

  • Latency percentiles

  • Service dependencies

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

  • The primary handler for the 'get_traces_overview' MCP tool. Includes the @mcp.tool() decorator for registration, error handling, the implementation logic that invokes the CorootClient, and the tool docstring serving as input schema definition.
    @handle_errors
    async def get_traces_overview_impl(
        project_id: str,
        query: str | None = None,
    ) -> dict[str, Any]:
        """Get traces overview."""
        overview = await get_client().get_traces_overview(project_id, query)
        return {
            "success": True,
            "overview": overview,
        }
    
    
    @mcp.tool()
    async def get_traces_overview(
        project_id: str,
        query: str | None = None,
    ) -> dict[str, Any]:
        """Get distributed tracing overview.
    
        Returns a summary of distributed traces across all applications:
        - Trace volume and trends
        - Error rates
        - Latency percentiles
        - Service dependencies
    
        Args:
            project_id: Project ID
            query: Search/filter query (optional)
        """
        return await get_traces_overview_impl(project_id, query)  # type: ignore[no-any-return]
  • Supporting utility in CorootClient that makes the authenticated HTTP GET request to the Coroot API endpoint '/api/project/{project_id}/overview/traces' and returns the parsed JSON response.
    async def get_traces_overview(
        self,
        project_id: str,
        query: str | None = None,
    ) -> dict[str, Any]:
        """Get distributed tracing overview.
    
        Args:
            project_id: Project ID.
            query: Search/filter query.
    
        Returns:
            Traces overview data.
        """
        params = {}
        if query:
            params["query"] = query
    
        response = await self._request(
            "GET",
            f"/api/project/{project_id}/overview/traces",
            params=params,
        )
        data: dict[str, Any] = response.json()
        return data
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 it mentions what data is returned (trace volume, error rates, etc.), it doesn't cover important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, data freshness, or pagination. For a tool with no annotations, this leaves significant gaps in understanding how the tool behaves.

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 efficiently structured with a clear purpose statement followed by bullet points detailing return values and a parameter section. Every sentence earns its place, with no redundant information. The front-loaded purpose statement immediately communicates the tool's function.

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?

Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description provides good coverage. The output schema exists, so the description doesn't need to explain return values in detail (it helpfully summarizes them). The main gap is the lack of behavioral context, but for a read-oriented tool with output schema, this is somewhat mitigated.

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?

With 0% schema description coverage, the description must compensate for the lack of parameter documentation in the schema. It successfully explains both parameters: 'project_id' is clearly identified as 'Project ID', and 'query' is described as an 'optional search/filter query'. This adds meaningful context beyond the bare schema, though it doesn't provide examples or format details for the query parameter.

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 specific action ('Get distributed tracing overview') and resource ('distributed traces across all applications'), distinguishing it from sibling tools like 'get_application_traces' which likely focuses on specific applications rather than an overview across all. It provides concrete details about what the overview includes (trace volume, error rates, latency percentiles, service dependencies).

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_application_traces' or 'get_applications_overview', nor does it specify prerequisites, timing considerations, or exclusions. The agent must infer usage from the tool name and description alone.

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

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