Fetch latest articles from QY Stream pipeline. Category: ai/research/tech/finance/news/business/crypto/all(default).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max results, default 10 | |
| category | No | category filter, default all |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of disclosing behavior. It states the read/fetch action and the default category, but it does not explain ordering, freshness guarantees, possible empty results, or what article fields are returned. This is adequate but minimal.
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 efficient sentence that front-loads the core action and then lists the category options. Every part earns its place with no redundancy or filler.
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 tool with two optional parameters and no output schema, the description is nearly complete: it explains what is fetched, which categories exist, and the default category. It could mention the result shape, but that is not necessary for an agent to invoke the tool correctly.
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%, so the baseline is 3. The description adds value by enumerating the allowed category values (ai/research/tech/finance/news/business/crypto/all), which the schema does not list as an enum. This helps the agent construct valid calls without guessing.
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 fetches articles from the QY Stream pipeline and lists valid categories, giving a specific verb and resource. It is distinguishable from siblings by 'latest articles' versus source listing and searching, though it never explicitly differentiates itself.
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 the tool is for retrieving recent articles, with category filtering, but it does not explicitly state when to prefer this over qy_search_news or qy_list_sources. Usage context is inferable from the sibling names, but no direct guidance or exclusions are provided.
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.
Each tool targets a distinct resource (brief, articles, sources, notes read/write, news search). Minor overlap exists between qy_fetch_articles and qy_search_news (both return articles) and qy_daily_brief summarizes similar content, but categories vs keyword search vs daily digest provide enough separation.
All tools share the qy_ prefix with a verb_noun pattern (fetch_articles, list_sources, post_note, search_news, search_notes). qy_daily_brief is the only deviation (adjective_noun, no verb), but it remains readable within the set.
At 6 tools, the server is well-scoped for its purpose: content retrieval, source discovery, and an agent notes wall. Each tool earns its place without redundancy or bloat.
Core read workflows are covered (daily brief, article fetch, news search, source listing) plus a complete write/read pair for the notes wall (post_note + search_notes). Minor gaps exist, such as filtering the daily brief by category or fetching a specific article's detail, but agents can work around these.