Cross-source news, finance, AI and tech search across 29 sources for agents (BBC, NYT, CNBC, HF).
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 |
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.
List data sources in QY Stream with reliability(reputation) + category.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | optional category filter |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It conveys that the operation is a read-only listing and that returned sources include reliability/reputation and category. It does not cover pagination, sorting, or result shape, but for a simple listing tool this is reasonably transparent.
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 concise sentence that leads with the verb and object. There is no filler or redundant restatement of the tool name, despite minor awkwardness in 'reliability(reputation)'.
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 tool with one optional parameter and no output schema, the description covers the main need: what the tool lists and what kind of data is included. It is brief on output shape, but the low complexity and the schema's parameter documentation make this adequate.
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 already documents the only parameter with 100% coverage ('optional category filter'). The description mentions category but adds no additional meaning about accepted values, behavior, or how the filter interacts with the listed sources, so it stays at the baseline.
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 states a specific verb ('List') and resource ('data sources in QY Stream'), and adds what is included (reliability/reputation and category). It is clear on its own, though it does not explicitly contrast itself with the sibling tools qy_fetch_articles and qy_search_news.
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?
There is no guidance on when to use this tool versus the siblings. The description only says what the tool does, not when an agent should prefer qy_list_sources over qy_fetch_articles or qy_search_news.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Cross-source search over 29 international news/AI/finance/tech sources (BBC,NYT,HackerNews,ArXiv,CNBC,FT). English keyword. Returns articles + source reliability(reputation). 100 free queries/day; key from POST /pipeline/v3/register.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max results, default 10 | |
| keyword | Yes | English search keyword, e.g. OpenAI, Fed rate, quantum |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It transparently discloses auth needs (key from POST /pipeline/v3/register), rate limits (100 free queries/day), and result content (articles + source reliability). The search wording also implies a read-only operation.
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?
Three dense sentences front-load the core operation and then add source scope, return content, quota, and auth acquisition without filler. Every sentence earns its place.
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?
With only 2 simple parameters and no output schema, the description sufficiently covers source scope, input language, expected result, quota, and key acquisition. An agent can select and invoke the tool accurately without external documentation.
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 description coverage is 100%, with keyword format/examples and limit range/default already documented. The description adds little beyond repeating 'English keyword', so the baseline 3 applies.
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?
States a specific verb ('search'), a bounded resource (29 international news/AI/finance/tech sources), and a clear output (articles + source reliability). This differentiates it from sibling tools like qy_fetch_articles and qy_list_sources.
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?
Provides clear usage context: cross-source search, English keyword required, named source domains, and a daily query budget. It does not explicitly name alternatives or exclusions, but the search-vs-fetch-vs-list distinction is reasonably clear from scope and sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
qy_fetch_articlesqy_list_sourcesqy_search_newsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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qy_fetch_articles and qy_search_news both return articles, which could cause some ambiguity, but the former is clearly category-based latest fetching while the latter is keyword-based cross-source search. qy_list_sources is completely distinct. Overall the boundaries are mostly clear.
All tool names use the consistent qy_ prefix followed by a clear verb_noun pattern: fetch_articles, list_sources, search_news. This makes the tool set predictable and easy to navigate.
Three tools is a tight, focused set for a read-oriented news/data pipeline server. Each tool covers a distinct access pattern: latest articles, source discovery, and keyword search. The count feels appropriate for the stated scope.
The core read operations are covered well: fetch by category, search by keyword, and inspect available sources. Minor gaps exist such as no direct article-by-ID retrieval or source detail endpoint, but agents can likely work around them using search and fetch.
