M
mtop-devtools
Local@F-loatDeveloper Tools
About mtop-devtools
Let AI interact with Chrome in real time: fetch request history, console logs, RUM/tracking events, and dynamically set mock data
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
29Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| count | number | No | Number of requests to retrieve. Defaults to 10. Retrieves up to the most recent 1000 requests. |
| since | number | No | How far back to retrieve requests, in seconds. For example: 60 retrieves requests from the last minute. |
| fields | array | No | Extract specified fields from the response body using dot-separated paths. For example: ["data.result.list", "data.total"] returns only these fields, greatly reducing the amount of data. |
| filter | string | No | Regular expression for filtering API names (optional). For example: ".*\.cart\..*" returns only cart-related APIs. |
| source | enum | No | Data source. mtop: mtop APIs (default); requests: regular XHR/Fetch requests. If omitted, uses the current mode of the DevTools panel.mtop | requests |
| keysOnly | boolean | No | Return only the data structure (field paths and types), not the actual values. Set to true to preview the data structure before deciding which fields to extract. Defaults to false. |
| includeBody | boolean | No | Whether to include the request and response bodies. When false, the postData and responseBody fields are omitted to reduce the amount of data. Defaults to true. |
| maxBodySize | number | No | Maximum size in bytes for an individual request or response body. Bodies exceeding this size are truncated. Defaults to 50000 (about 50KB). |
| includeHeaders | boolean | No | Whether to include request and response headers. When false, the headers and responseHeaders fields are omitted to reduce the amount of data. Defaults to false. |
Details
The maintainer has not added details yet. Check the README in the repository.
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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.