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deploy_mock_from_spec

Deploy an inline OpenAPI 3.0 spec to a hosted, shareable mock with a persistent URL, stored in your account for team access.

Instructions

Create a HOSTED, SHAREABLE mock on mockzilla.org from an inline OpenAPI 3.0+ spec (YAML or JSON in the spec field, 4MB cap). The mock persists in the user's account and gets a stable URL anyone with the link can hit. Use this when the user pastes spec content AND wants a durable, team-visible result — NOT for ephemeral local exploration (use serve_locally for that). The returned sim is in deploying state — follow up with wait_for_deploy to receive the live URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
slugYes
specYes
domainNo
filenameNo
mount_pathNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.20

TDQS

A4.3/5.0
Behavior4/5

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

Beyond the readOnlyHint=false annotation, the description discloses that the mock persists in the user's account, receives a stable shareable URL, has a 4MB cap, and starts in the 'deploying' state requiring a follow-up call. It does not disclose side effects like slug collisions or overwrites, but the main behavioral traits are clearly surfaced.

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?

Three dense sentences with no filler. The core action and key constraints are front-loaded, usage guidance follows, and the state/follow-up note closes the loop. Every sentence 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?

For a tool with no output schema and sparse input schema descriptions, the description covers the key workflow: input format, persistence, state, and next step. It is still missing explanations for several parameters and edge-case behavior, but the agent has enough context to invoke the tool correctly and proceed to wait_for_deploy.

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%, so the description must compensate. It explains the `spec` parameter format (YAML/JSON, 4MB cap), but gives no guidance for the required `name` and `slug` parameters or the optional `domain`, `filename`, and `mount_path`. This leaves most parameters semantically underspecified.

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 names a specific verb ('Create'), a specific resource ('HOSTED, SHAREABLE mock on mockzilla.org'), and the input ('inline OpenAPI 3.0+ spec'). It clearly distinguishes itself from siblings like serve_locally, deploy_mock_from_url, and deploy_mock_from_catalog by emphasizing inline spec content and durable team-visible results.

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

Usage Guidelines5/5

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

The description states exactly when to use this tool ('when the user pastes spec content AND wants a durable, team-visible result') and when not to ('NOT for ephemeral local exploration'), explicitly naming serve_locally as the alternative. It also tells the agent the follow-up step with wait_for_deploy, leaving no ambiguity about the workflow.

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