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Polydev

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by polydev-ai
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JavaScript
Hybrid

Polydev status

get_auth_status

Check whether this machine is signed in to Polydev, which AI sources are ready, and configured models; opens browser sign-in when authentication is missing.

Instructions

Show whether this machine is signed in to Polydev, which sources on this machine are ready (Claude Code, Codex, ChatGPT plan) and the configured models. When this machine is not signed in, it opens Polydev sign-in in the browser.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_auth_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
user_tokenNoPolydev token (starts with pd_). Optional: checks this token if given, otherwise the saved one.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

The annotations declare readOnlyHint=false and openWorldHint=true, but they do not explain why a 'status' call is not read-only; the description closes that gap by disclosing that it launches Polydev sign-in in the browser when the machine is not signed in. That is exactly the kind of side effect an agent must know before invoking. It does not say whether the browser opening blocks or how failures surface, keeping it below a 5.

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 sentences, front-loaded with the primary outcome (sign-in status and ready sources) before the conditional side effect. Every clause carries information; the only minor inefficiency is the parenthetical source list, which is genuinely useful rather than padding.

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?

With no output schema, the description takes on the job of describing return content, and it does so by enumerating sign-in state, per-source readiness, and configured models. The one gap is that it does not mention the optional user_token override or what happens when a supplied token is invalid, but for a one-parameter status tool this is largely complete.

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% and the single user_token parameter is fully documented in the schema, including its pd_ prefix and optional fallback behavior. The description never mentions the token override at all, so it adds no meaning beyond the schema — the baseline 3 applies.

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 names a specific verb and resource: it reports sign-in state, which sources (Claude Code, Codex, ChatGPT plan) are ready, and configured models. That is concrete enough to separate it from siblings such as login or chatgpt_plan_status, though it never explicitly says how it differs from those.

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?

Usage is only implied — an agent can infer this is a pre-flight check before calling ask_model or connect_chatgpt_plan, but the description states no when-to-use condition and names no alternative. The one conditional it gives ('when this machine is not signed in...') describes side-effect behavior, not selection guidance.

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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