Volatility and bands: atr, bbands, donchian, kc, stdev, ui.
Runs all volatility indicators for the ticker, or only the subset named in
`indicators`. `period` sets the history window: "1d","5d","1mo","3mo",
"6mo","1y","2y","5y","10y","ytd","max" (default "2y").
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | 2y | |
| ticker | Yes | ||
| indicators | No |
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 the full burden. It explains that the tool runs either all listed indicators or the subset specified, and that `period` selects the history window. However, it does not mention output format, error handling, or explicitly state that the operation is read-only (though it is implied). This is adequate but lacks some behavioral context.
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 only two sentences long, front-loaded with the indicator list. It is concise, with every sentence providing necessary information: what the tool does and how the parameters behave. No wasted words or 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?
The description covers the core purpose and all parameters, making it usable for a straightforward analysis tool. However, it does not describe the output structure or any edge cases, and there is no explicit comparison to sibling tools. Given the simplicity of the tool and lack of an output schema, this is reasonably complete but not exhaustive.
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 0%, so the description must compensate. It explains the `period` values and default, the `indicators` parameter as an optional subset selector, and the ticker via context ('for the ticker'). This adds meaningful semantics beyond the raw schema, which only lists types and enums.
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 'Runs all volatility indicators for the ticker, or only the subset named in `indicators`.' It names specific indicators (atr, bbands, donchian, kc, stdev, ui) and distinguishes itself from sibling tools like analyze_trend and analyze_momentum through the explicit focus on volatility and bands.
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?
Usage is implied by the name and indicator list, but there is no explicit guidance on when to use this tool versus alternatives such as analyze_trend or analyze_statistics. The description does not provide 'use this when...' or 'instead of...' guidance, so it falls into implied usage rather than clear context.
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.