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Joblogic MCP Server and API by Cotlet

Schedule engineer in Joblogic

joblogic_schedule_engineer
Read-onlyIdempotent

Schedule engineer in the user's own Joblogic account from ChatGPT, Claude or any AI agent. Returns how to do it in Joblogic step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's Joblogic Cloud Browser. Joblogic's official API: Joblogic has a documented REST API, but it is a paid "API Access" Marketplace module meant for developers (checked 9 Nov 2026).

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Goes beyond the readOnly/idempotent annotations by disclosing that the tool returns screens/buttons/fields and performs no action on the account, so the agent knows it is advisory only. The main behavioral gap is that it never says what information the agent must supply (engineer identity, job, date) to get a useful runbook, given there are no parameters.

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?

Front-loads what it does, then what it does not do, then the follow-up tooling, which is a sensible order. The closing note about the paid Joblogic API is somewhat tangential but earns its place by justifying the guide-based approach.

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 a zero-parameter, read-only guide tool with no output schema, the description covers the return shape and the safety profile adequately. It is incomplete on the input side: with no arguments, an agent needs to know what context to embed in its request (which engineer, which job, which date) to get a usable walkthrough, and that is never addressed.

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

Parameters5/5

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

The tool takes zero parameters and the schema is trivially complete, so there is no parameter semantics to explain; baseline credit applies. The description does not waste space restating an empty schema.

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?

States a specific verb and resource ("Schedule engineer in the user's own Joblogic account") and clarifies the output is a step-by-step guide rather than a direct action. That distinguishes it from the joblogic_create_quote / joblogic_log_job API-style siblings without naming them explicitly.

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?

Explicitly says it opens nothing by itself and that the actual work must be done with cotlet_browser_* tools afterward, which tells the agent how to sequence calls. It also explains why the official REST API path is not an option (paid Marketplace module), giving a rationale for choosing this guide. No explicit "when not to use" statement, but the sequencing guidance is clear.

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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