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jamesbrink

MCP Server for Coroot

get_application_profiling

Retrieve CPU and memory profiling data with flame graphs to identify performance bottlenecks and optimize application performance.

Instructions

Get CPU and memory profiling data for an application.

Retrieves profiling data including flame graphs for CPU usage and memory allocation patterns to help identify performance bottlenecks and optimization opportunities.

⚠️ WARNING: This endpoint can return extremely large responses (180k+ tokens) for applications with extensive profiling data. Consider using time filters to limit the response size to specific time windows.

Args: project_id: Project ID app_id: Application ID (format: namespace/kind/name) from_timestamp: Start timestamp (optional, strongly recommended) to_timestamp: End timestamp (optional, strongly recommended) query: Search query (optional)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
app_idYes
from_timestampNo
to_timestampNo
queryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, description carries full burden. It warns about extremely large responses (180k+ tokens) and recommends time filters, which is valuable behavioral information beyond schema.

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?

Description uses clear sections and a warning, but could be slightly more concise. Good front-loading with purpose.

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?

Covers purpose, parameter details, and a critical warning. Output schema exists so return values not needed. Could add permissions or prerequisites, but overall adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description's Args block explains all five parameters, includes format hint for app_id and strong recommendation for timestamps, fully compensating for schema gaps.

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?

Description clearly states verb 'Get' and resource 'CPU and memory profiling data for an application', distinguishing from sibling tools like get_application_logs or configure_profiling.

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

Usage Guidelines3/5

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 vs alternatives; usage context is implied by the tool's purpose. The warning about large responses provides some operational guidance but not tool selection advice.

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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