Statistical behavior: log_return, zscore, skew, kurtosis, entropy.
Runs all statistical 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 discloses behavioral traits: it runs all indicators by default, the period parameter restricts the lookback window, and the allowed period values are enumerated. It does not explicitly state that the operation is read-only or describe the output format, but the nature of 'statistical behavior' implies computation without side effects. The description provides enough behavioral context for an agent to understand what the tool does and how to scope it.
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 exactly two sentences. The first sentence front-loads the core purpose by listing the indicators, and the second sentence explains the key parameters. Every word earns its place; there is no redundancy or fluff.
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 tool has 3 parameters, no annotations, and no output schema. The description covers the parameters and default behavior, which is sufficient for basic usage. However, since there is no output schema, the description would benefit from stating what the return value looks like (e.g., a dict mapping indicator names to values). This minor gap prevents a perfect score.
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. It does so effectively: it explains `indicators` (subset of named indicators, default all) and `period` (with enum values and default '2y'). The `ticker` parameter is self-explanatory as the required stock symbol. The description adds meaning to all parameters beyond the raw schema.
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 computes statistical indicators (log_return, zscore, skew, kurtosis, entropy) and explicitly says it 'runs all statistical indicators for the ticker'. It uses a specific verb ('runs') and resource ('statistical indicators'), and distinguishes itself from sibling tools like analyze_momentum or analyze_volatility by naming distinct statistical metrics.
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 explains how to use the tool: run all indicators by default or specify a subset via `indicators`, and set the `period` for the history window. It implies this tool is for statistical behavior analysis, which separates it from momentum/trend/volatility tools, but it does not explicitly state when not to use it or mention alternatives.
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.