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jamesbrink

MCP Server for Coroot

get_db_instrumentation

Retrieve database instrumentation settings for MySQL, PostgreSQL, Redis, MongoDB, or Memcached to configure monitoring in Coroot observability platform.

Instructions

Get database instrumentation configuration.

Retrieves instrumentation settings for a specific database type.

Args: project_id: Project ID app_id: Application ID (format: namespace/kind/name) db_type: Database type (mysql, postgres, redis, mongodb, memcached)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
app_idYes
db_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler for 'get_db_instrumentation'. This is the entrypoint function decorated with @mcp.tool() that executes the tool logic by delegating to the implementation helper.
    @mcp.tool()
    async def get_db_instrumentation(
        project_id: str, app_id: str, db_type: str
    ) -> dict[str, Any]:
        """Get database instrumentation configuration.
    
        Retrieves instrumentation settings for a specific database type.
    
        Args:
            project_id: Project ID
            app_id: Application ID (format: namespace/kind/name)
            db_type: Database type (mysql, postgres, redis, mongodb, memcached)
        """
        return await get_db_instrumentation_impl(project_id, app_id, db_type)  # type: ignore[no-any-return]
  • Helper implementation that wraps the CorootClient call and formats the response with success indicator.
    async def get_db_instrumentation_impl(
        project_id: str, app_id: str, db_type: str
    ) -> dict[str, Any]:
        """Get database instrumentation config."""
        client = get_client()
        config = await client.get_db_instrumentation(project_id, app_id, db_type)
        return {
            "success": True,
            "config": config,
        }
  • Underlying CorootClient method that makes the actual HTTP API request to retrieve DB instrumentation configuration.
    async def get_db_instrumentation(
        self, project_id: str, app_id: str, db_type: str
    ) -> dict[str, Any]:
        """Get database instrumentation configuration.
    
        Args:
            project_id: Project ID.
            app_id: Application ID.
            db_type: Database type (mysql, postgres, redis, mongodb, memcached).
    
        Returns:
            Database instrumentation configuration.
        """
        # URL encode the app_id since it contains slashes
        from urllib.parse import quote
    
        encoded_app_id = quote(app_id, safe="")
    
        response = await self._request(
            "GET",
            f"/api/project/{project_id}/app/{encoded_app_id}/instrumentation/{db_type}",
        )
        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. It states this is a retrieval operation ('Get', 'Retrieves'), implying it's read-only, but doesn't confirm if it's safe or has side effects. It lacks details on authentication needs, rate limits, error conditions, or what the output contains (though an output schema exists). For a tool with no annotations, this is a significant gap in transparency.

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 front-loaded with the main purpose, followed by parameter details. It uses two sentences for the purpose and a bullet-like list for parameters, with no wasted words. However, it could be slightly more concise by integrating parameter explanations more seamlessly, but overall it's efficient.

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 (3 required parameters) and the presence of an output schema, the description is partially complete. It explains what the tool does and the parameters, but with no annotations, it misses behavioral context like safety or performance. The output schema reduces the need to describe return values, but the description should still cover usage guidelines and more transparency to be fully adequate.

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?

Schema description coverage is 0%, so the schema provides no parameter descriptions. The description adds value by explaining each parameter's purpose: 'project_id: Project ID', 'app_id: Application ID (format: namespace/kind/name)', and 'db_type: Database type (mysql, postgres, redis, mongodb, memcached)'. This clarifies semantics beyond just the parameter names, but it doesn't fully compensate for the lack of schema coverage (e.g., no validation rules or examples), so a baseline 3 is appropriate.

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 database instrumentation configuration' and 'Retrieves instrumentation settings for a specific database type.' It specifies the verb ('Get', 'Retrieves'), resource ('database instrumentation configuration'), and scope ('for a specific database type'). However, it doesn't explicitly differentiate from sibling tools like 'update_db_instrumentation' or other configuration tools, which prevents 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.

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 'update_db_instrumentation' for modifying settings or other 'get_' tools for different resources. There's no context about prerequisites, such as whether the database must be configured first, or exclusions for when not to use it.

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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