Skip to main content
Glama

list_network_activity

List observed network requests from a browser session. Filter by state, URL substring, time window, and sort by speed or age. Returns stable references for follow-up inspection.

Instructions

List observed network requests buffered during this browser session. Supports temporal filtering by seq window, request-state filters, URL substring filtering, and adjective-based sorting such as slowest/fastest or newest/oldest. Returns stable @rN refs for follow-up inspection with inspect_request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNoall
limitNo
patternNoURL substring filter
sinceNoStart of time window. 'all' = entire session, 'last' = since last action (default), or a seq number from a previous action response.
sort_byNoSort order. First element = primary, rest = tiebreakers. Default: ['oldest']
untilNo'now' = up to present (default), or a seq number (exclusive upper bound).
Behavior4/5

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

With no annotations, the description carries the full burden. It clearly states the tool lists buffered requests and supports various filters. It doesn't specify side effects (likely none) or provide details on default limits or edge cases, but for a list operation, this is fairly transparent.

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 concise sentences: first states primary purpose, second enumerates capabilities and return value. No redundant information.

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?

Adequate for a list tool with 6 parameters and no output schema, but lacks detail on the returned data structure (fields, pagination) and default behaviors like limit. Mentions @rN refs but not the full response format.

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 coverage is 67%; the description adds context for the filter, pattern, since, sort_by, and until parameters beyond their schema descriptions. It explains temporal filtering via 'seq window' and sorting via adjectives. Limit and filter lack schema descriptions but are referenced in the text.

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 clearly states the verb 'List' and the resource 'observed network requests buffered during this browser session', distinguishing it from siblings like inspect_request (which inspects individual requests) and intercept_network (for interception). It also specifies that it returns @rN refs for follow-up.

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

Usage Guidelines3/5

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

The description implies usage for viewing network activity but does not provide explicit when-to-use or when-not-to-use criteria. It mentions integration with inspect_request but lacks comparison to similar list tools like list_websocket_activity.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Mingye-Lu/AgenticCrawler'

If you have feedback or need assistance with the MCP directory API, please join our Discord server