Skip to main content
Glama
devinwang

cloak-browser-mcp

by devinwang

cloak_set_fingerprint

Apply a fingerprint profile to the active browser context to set seed, GPU, screen, and user-agent fields, reopening current pages if recreation is required.

Instructions

Apply a fingerprint profile to the active context. Some fields (seed, GPU, screen) require recreating the context — current pages will be reopened on the same URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoDeterministic canvas/WebGL/audio seed.
uaBrandNoBrowser brand for navigator.userAgentData (e.g. 'Google Chrome').
platformNo
gpuVendorNo
gpuRendererNo
screenWidthNo
screenHeightNo
deviceMemoryGBNo
storageQuotaMBNo
uaBrandVersionNo
hardwareConcurrencyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose a genuine side effect beyond the schema: certain fields force context recreation and current pages are reopened on the same URL. This is exactly the kind of destructive/reloading consequence an agent needs. It stops short of stating which fields are cheap to change vs. expensive, beyond the parenthetical hint '(seed, GPU, screen)'.

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?

Two tight sentences, purpose front-loaded, then the costly caveat. No filler and nothing duplicated. Slightly under-specified rather than verbose, but structurally sound.

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?

For an 11-parameter mutation tool with no annotations and no output schema, the description covers the single most important behavioral risk (context recreation) but leaves parameter semantics almost entirely undocumented against an 18% schema coverage. The description is complete on behavior, incomplete on inputs.

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

Parameters2/5

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

Schema description coverage is 18% across 11 parameters, so the description must compensate and largely does not. The parenthetical '(seed, GPU, screen)' groups three of the eleven parameters but adds no meaning to them or to platform, deviceMemoryGB, storageQuotaMB, hardwareConcurrency, or uaBrandVersion. Accepted formats and valid ranges remain unexplained.

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?

Specific verb + resource: 'Apply a fingerprint profile to the active context.' This is clearly distinct from sibling cloak_set_timezone, cloak_set_locale and cloak_set_geolocation, though the description never names those siblings to reinforce the distinction. An agent can tell what it does without opening the schema.

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?

No when-to-use guidance, no exclusions, no alternatives named (e.g. cloak_detection_test to verify the result, or persistent profiles for durable fingerprints). The phrase 'active context' is the only contextual cue and it is not elaborated. The agent must infer the whole usage model.

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