Trace MCP

trace_usage

Traces client code to detect API calls including MCP tool calls, HTTP requests, and GraphQL hooks, identifying how properties are accessed on responses.

Instructions

Trace how client code calls APIs. Detects: MCP callTool(), fetch/axios HTTP calls, Apollo Client hooks (useQuery/useMutation), Python requests/aiohttp/httpx, and tracks property access patterns on responses.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "rootDir": "<rootDir>"
    },
    "name": "trace_usage"
  }
}

Input Schema

Name	Required	Description
`rootDir`	Yes	Root directory of client/consumer source code
`include`	No	Glob patterns to include
`exclude`	No	Glob patterns to exclude

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedMay 10, 2026

changedInput schema / properties / rootDir / description

Previous value: -"Root directory of consumer source code"New value: +"Root directory of client/consumer source code"

First observedDec 28, 2025

TDQS

A4/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses detection capabilities clearly, but doesn't mention whether it modifies anything, requires network access, or handles errors. Still fairly transparent.

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?

Two efficient sentences, front-loaded with key action and specific examples. Every sentence earns its place with zero waste.

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?

Adequately covers the tool's capability for its complexity. No output schema, but description doesn't need to explain return values. Could mention output format or limitations, but still complete enough.

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 coverage is 100% with clear parameter descriptions. The description adds no extra meaning beyond what schema provides, achieving baseline 3.

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 the tool's purpose: tracing how client code calls APIs. It lists specific types of API calls detected (MCP, HTTP, Apollo, Python libraries), making it specific and distinguishing it from siblings like trace_file.

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 like trace_file or compare. The description implies usage for tracing API usage but lacks when-not-to or alternative recommendations.

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

Deploy Server