Recent news headlines for a ticker via Yahoo Finance's news feed.
Returns structured articles (title, publisher, url, published_at,
summary) for you to read and synthesize. Not a scraper -- uses Yahoo's
aggregated feed, so coverage is strongest for large-cap US and Indian
names.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| ticker | Yes |
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?
With no annotations, the description carries full transparency burden. It discloses the data source (Yahoo's aggregated feed), coverage limitations (strongest for large-cap US and Indian names), and the non-scraper nature. This is valuable context beyond the tool name. It does not mention rate limits or pagination, but for a news read tool, it 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 two sentences, front-loaded with the core purpose, followed by return format and a caveat. Every sentence adds value with no fluff, making it highly efficient and well-structured.
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 2-parameter tool with no output schema, the description covers the purpose, how to use it (read and synthesize), the return fields, and a coverage limitation. It lacks explicit mention of sorting/ordering or handling of edge cases, but given the tool's simplicity, it is quite complete for an agent to invoke 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 description coverage is 0%, so the description must compensate. The description clarifies the 'ticker' parameter implicitly by saying 'for a ticker', but the 'limit' parameter is entirely undocumented. Without any schema descriptions, the agent gets no help on parameter meaning, formats, or examples, leaving a significant gap.
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 returns recent news headlines for a ticker via Yahoo Finance's news feed, with a specific verb and resource. It also distinguishes itself from a scraper, which adds precision. While no sibling news tool exists, this is a complete and specific purpose statement.
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 in the context of reading and synthesizing news, but it does not explicitly state when to use this tool versus alternatives. The 'not a scraper' note hints at a constraint but does not name an alternative for full news scraping. Usage guidance is present but implied rather than explicit.
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.
Several tools overlap significantly: plot_charts is an explicit alias for generate_charts, generate_chart_pack and generate_charts have similar purposes, and backtesting tools like backtest_macd_momentum vs backtest_macd_trend_follower or backtest_mean_reversion_rsi_bb vs backtest_rsi_mean_reversion are easily confused. The sector tools also have fuzzy boundaries.
Most tools follow a clear verb_noun pattern (analyze_*, backtest_*, get_*, generate_*). However, two tools use a 'tool' suffix (analyze_sector_intelligence_tool, find_sector_stock_pipeline_tool) which deviates from the otherwise consistent naming style.
At 25 tools, the server is at the heavy end of the acceptable range. The scope is broad (analysis, backtesting, charting, portfolio optimization, alerts), but redundant chart tools and overlapping backtest strategies inflate the count and hurt focus.
The toolset covers the core domain well: technical analysis, backtesting, trade planning, portfolio optimization, quotes, news, and alerts. Minor gaps exist, such as no watchlist management tool (scanning only) and no direct historical data fetch, but these are workable around the existing tools.