Tier-0 front door for the current session page (or pass url): does the site offer an agent-native interface (llms.txt / OpenAPI / ai-plugin)? Prefer it over scraping.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | optional: probe this url instead of the current page | |
| browser_id | Yes | from browse_open |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and the description aligns perfectly, adding context that the tool probes for interface files. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that conveys the tool's purpose, usage priority, and target without any unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple discovery tool, the description and annotations cover safety and purpose well. The return type (likely boolean or interface list) is not specified, but given no output schema, the description is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds that url is optional and browser_id comes from browse_open, going beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it is a 'Tier-0 front door' that checks if a site offers an agent-native interface (llms.txt, OpenAPI, ai-plugin), distinguishing it from scraping tools like browse_read.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises to prefer this tool over scraping, implying it should be used first to detect native interfaces. It could be more detailed about when not to use it (e.g., if the site is known to have no interfaces), but the guidance is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools serve similar purposes (e.g., web_read vs browse_read, web_discover vs browse_discover, research vs web_search + browse). Descriptions help differentiate, but the overlap is notable.
Most tools use a consistent verb_noun snake_case pattern (e.g., archive_message, browse_navigate). Minor deviations like standalone 'browse' and 'identity' are acceptable.
57 tools is very high for a single server, even with discover_tools. The broad domain coverage does not justify the count; it feels overloaded.
Covers identity, memory, browsing, human tasks, errands, messaging, and research comprehensively. Minor gaps might exist (e.g., no explicit agent-to-agent contract tools), but core workflows are well-supported.