list_sourcesList the topic inboxes this person feeds their briefing with: each one has a dedicated email address for newsletter subscriptions, plus RSS feeds and monitored web searches. Shows how much each produced recently, its priority and whether it is included in the briefing.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint: true, so the description reinforces a read operation. It adds behavioral context beyond annotations by specifying exactly what data is returned (email address, RSS, web searches, production stats, priority, inclusion). No side effects or authorization details are mentioned, but the description aligns with the annotations and complements them well.
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 two sentences, front-loaded with the core purpose, and every word contributes value. No redundancy or unnecessary detail. Efficient and clear.
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?
Given no output schema and low complexity (0 parameters), the description adequately covers what the tool returns: each source's email, RSS, web searches, recent production, priority, and inclusion status. It could be slightly more explicit about scope (e.g., 'the current user's topic inboxes'), but overall it is sufficiently complete for correct invocation.
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 coverage is 100%. With 0 parameters, the baseline is 4. The description does not need to explain parameters, and it adds no parameter-related meaning beyond the schema, which is appropriate.
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 lists 'topic inboxes' (information sources) for a person's briefing, detailing what each includes (email address, RSS feeds, web searches) and what performance metrics are shown (recent production, priority, inclusion). The verb 'list' and resource 'information sources' are specific, and the description distinguishes this from siblings like list_briefings, list_deep_dives, and list_read_later.
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 usage context—when needing to view available sources for a briefing—but does not explicitly state when to use this tool versus alternatives like discover_sources or get_source. No exclusion criteria or conditions are mentioned. While the purpose is clear, explicit guidance on when not to use it would improve selection accuracy.
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.