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login_accounts

Authenticate learning accounts via HTTP and reuse logged-in sessions. Specify account IDs to log in, optionally force re-login or activate browser, avoiding implicit account switching.

Instructions

HTTP login only by default; never switches another account's browser implicitly.

Reuses logged-in sessions. After a platform rejection, change credentials or inspect verification rather than retrying the same password repeatedly. setup_learning_accounts explicitly activates its first account.

Input Schema

NameRequiredDescriptionDefault
forceNo
account_idsNo
activate_browserNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.9.4

TDQS

A3.6/5.0
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 non-obvious traits: HTTP-only login by default, reuse of already-logged-in sessions, and no implicit switching of another account's browser. What it omits is the effect of the force flag and any destructive/side-effect or credential-handling context, which matters for a login tool with zero annotation coverage.

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?

Four short sentences, no filler, and the most decision-relevant constraint (HTTP-only, no implicit browser switching) is front-loaded. It leads with a caveat rather than the action, which slightly delays the reader's orientation.

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?

An output schema exists, so return values need no explanation, and the behavioral notes cover session reuse and activation defaults. However, for a 0%-coverage three-parameter tool, leaving force, account_ids, and activate_browser solely to inference is a real completeness gap an agent could trip on.

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 0% across three parameters (force, account_ids, activate_browser) and the description never names or explains any of them. 'HTTP login only by default' hints that browser activation is off by default, but force and account_ids — the parameters that most change behavior — are entirely unexplained in both schema and description.

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 establishes that this performs an HTTP-based login and reuses existing sessions, and it distinguishes itself from setup_learning_accounts (which explicitly activates its first account) and from browser-activation siblings by stating it never implicitly switches browsers. The core verb 'login' is carried by the name and only echoed, not restated with a resource object, so it falls short of a fully self-contained purpose statement.

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

Usage Guidelines4/5

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

It gives a clear negative-side guideline: after a platform rejection, change credentials or inspect verification instead of retrying the same password, which routes the agent to update_account_credentials or inspection siblings. It also contrasts with setup_learning_accounts for explicit activation. There is no explicit positive trigger ('call this when you need authenticated sessions for accounts X'), so a small inference remains.

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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