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gpambrozio

onshape-mcp

by gpambrozio

Check the pending Onshape login

onshape_login_status
Read-only

Check the progress of a browser-based Onshape login. Waits up to a specified time and reports whether sign-in is complete, enabling you to confirm authentication before using CAD tools.

Instructions

Wait for, and report on, the login started by onshape_login. Call this repeatedly while the user is in the browser; each call waits up to wait_seconds before returning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wait_secondsNoSeconds to wait for the browser before returning (default 20).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations only provide readOnlyHint=true; the description adds genuine behavioral value beyond that by disclosing the blocking/polling nature ('each call waits up to wait_seconds before returning'). This is useful, non-contradictory context that an agent needs to know before calling repeatedly. It does not contradict the read-only annotation since waiting/reporting is a safe read operation.

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 compact sentences with zero filler. The core purpose is front-loaded ('Wait for, and report on...'), followed immediately by critical usage guidance about repeated calls. Every word earns its place.

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 complete for a simple polling tool: it explains the polling loop and wait duration. However, with no output schema, it does not describe what the agent should expect on timeout or how it distinguishes 'still pending' from 'login complete' — a meaningful gap since the agent needs the loop-termination condition to know when to stop calling.

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% — the single parameter wait_seconds is fully documented with min, max, and default value. The description reinforces the wait behavior but adds little semantic value beyond what the schema already provides. Baseline 3 is appropriate when the schema does the heavy lifting for a fully-documented single parameter.

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?

The description states a specific verb-resource pair ('Wait for, and report on, the login started by onshape_login') and explicitly ties it to the login flow. 'Call this repeatedly while the user is in the browser' frames it as a polling tool, distinguishing it from siblings like onshape_auth_status and onshape_login without ambiguity.

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?

The description gives clear contextual usage ('Call this repeatedly while the user is in the browser; each call waits up to wait_seconds before returning'), which precisely instructs the agent on the polling loop. However, it does not explicitly contrast with onshape_auth_status (the closest sibling) or state when NOT to use this tool, leaving the exclusion implicit.

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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