Skip to main content
Glama
andresolbach

nodriver-mcp-server

by andresolbach
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Stop performance trace

performance_stop_trace

Stop an active performance trace and collect its buffered data, optionally saving the raw trace JSON to a local file. Use when a trace was started with auto_stop disabled.

Instructions

Stop the running performance trace and collect its data.

Only needed when performance_start_trace was called with auto_stop=false — otherwise the trace has already ended and this returns an error.

Waits up to 30 seconds for Chrome to flush its buffered events.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
browserNoWhich browser to act on. Each name is an independent Chrome, created on first use. Omit for the shared default; parallel agents each need their own name.default
file_pathNoWrite the raw trace JSON to this local path (e.g. "trace.json"), loadable in DevTools -> Performance. Omit to get only the event count.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.5.0
    • addedInput schema / properties / browser
      Added value: +{
+  "default": "default",
+  "description": "Which browser to act on. Each name is an independent Chrome, created on first use. Omit for the shared default; parallel agents each need their own name.",
+  "type": "string"
+}
  2. First observedv1.7.2

TDQS

A4.4/5.0
Behavior4/5

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

Adds real behavioral context beyond the annotations: the error condition when the trace already ended and the 30-second flush wait. These are exactly the kind of runtime behaviors descriptions should disclose. No contradiction with annotations.

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?

Three short sentences, each earning its place: core action, usage condition/error, and timeout behavior. Front-loaded with the primary purpose and zero filler.

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?

Complete for the tool's complexity: purpose, when-to-call, error case, and blocking behavior are all covered, and the output schema handles return-value details. Only minor extras like resource implications are absent.

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 100%, so the schema already fully documents both parameters. The description contributes no param-level detail, but none is needed given the baseline-3 rule for full coverage.

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?

States a specific verb (stop) and resource (performance trace), plus the consequence (collect its data). It clearly differs from the sibling performance_start_trace and the first line makes the action unmistakable.

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

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly names the sibling tool, the triggering condition (auto_stop=false), and the failure mode when used incorrectly ('returns an error'). An agent knows exactly when to call this vs. when not to, with nothing left to inference.

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

Deploy Server

Other Tools