list_deep_divesList the deep-dive research requests waiting to be covered: topics the user asked the briefing to investigate with fresh web research.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds value by clarifying these are 'requests waiting to be covered' and that they involve 'fresh web research', but does not disclose additional behavioral traits like ordering, pagination, or potential staleness of the list.
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 sentence clearly stating what the tool lists and providing context about what deep dives are. Every word adds value with no redundancy.
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 parameterless read-only list tool with no output schema and clear annotation support, the description sufficiently explains the purpose and context of the returned items. It does not describe the output format, but given the simplicity and the fact that the tool name implies a list, this is a minor gap.
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?
The input schema has no parameters and schema description coverage is 100%, so there is no parameter information to supplement. The description does not need to add parameter meaning; the baseline is high and the tool is trivially parameterless.
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 it lists 'deep-dive research requests waiting to be covered', specifying both the resource type and status. However, it does not distinguish this from other list tools among siblings like list_briefings, list_sources, or list_read_later, which are distinct enough that confusion is unlikely.
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 implies this tool is used to see what topics are pending investigation, which provides contextual usage guidance. However, it offers no explicit direction on when to use this over alternatives (e.g., request_deep_dive, generate_briefing) or when not to use it.
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.
Most tools target distinct operations (e.g., add_feed vs. remove_feed, get_source vs. list_sources). However, there is some ambiguity between correct_briefing and tune_topic (both adjust content based on user feedback) and between request_deep_dive and correct_briefing (both can trigger deeper investigation). The descriptions help, but an agent could misselect.
Tool names follow a very consistent verb_noun pattern (e.g., add_feed, create_source, generate_briefing, list_sources). All use snake_case with clear, descriptive verbs and nouns. No mixing of conventions.
With 27 tools, the server covers a rich domain of news feed management, briefing generation, and user preferences. This is slightly above the typical sweet spot but still reasonable given the complexity. Each tool serves a distinct purpose; the count is justified.
The tool set provides full lifecycle coverage: sources can be created, read, updated, and deleted; feeds and searches can be added and removed; briefings can be generated, listed, read, rated, and corrected; deep dives and read-later items are supported. There are no obvious gaps—the domain is thoroughly covered.