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mock_create_from_entry

Create a Map Local mock from a live or historical capture entry, applying optional JSON patches and archiving the previous mock.

Instructions

Turn a captured response into a Map Local mock, optionally editing it. Take entry_id from query_live_capture_entries (pass capture_id) or a history summary (pass recording_path). The mock is written for the entry's host and path; a previous mock at that path is archived, never deleted. Read warnings: Map Local always answers 200 and ignores method and query.

Input Schema

TableJSON Schema

Name	Required	Description
`source`	Yes
`patches`	No	Edits applied to the captured JSON response, addressed by JSON Pointer: {"op": "set", "path": "/data/balance", "value": 0} or {"op": "remove", "path": "/data/banner"}. Use "/items/-" to append and "/items/[id=42]/status" to pick an array element by a field.
`entry_id`	Yes
`capture_id`	No
`recording_path`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mock`	Yes
`warnings`	No
`next_step`	Yes
`patches_applied`	No
`source_entry_id`	No
`archived_previous`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 14, 2026

TDQS

A4.4/5.0

Behavior4/5

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

No annotations are provided, so the description carries full behavioral burden. It discloses important side effects: a previous mock at the same path is archived, never deleted, and Map Local always answers 200 while ignoring method and query. It also points the agent to read the warnings field.

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 sentences, each earning its place: main purpose, source selection, and behavioral caveats. The most important information is front-loaded, and there is no filler.

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 5 parameters, no annotations, and an output schema, the description covers the critical usage decisions, side effects, and response caveats. It doesn't cover error cases or exact entry_id format, but it is sufficient for correct invocation in common flows.

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

Parameters4/5

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

Schema description coverage is only 20%, so the description must compensate. It explains how entry_id is obtained from either capture_id or recording_path, and clarifies the source-dependent parameter usage. Patches are already well documented in the schema with JSON Pointer examples.

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 and resource: 'Turn a captured response into a Map Local mock, optionally editing it.' It also clarifies the source of the entry_id, which distinguishes this tool from rule-based mock creation siblings like mock_rule_create_from_entry.

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 clear context for when to use it: after query_live_capture_entries or a history summary, and explains which parameter to pass for each source. It doesn't explicitly name alternatives or exclusions, but the source-selection guidance is actionable.

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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