Volume confirmation: obv, cmf, mfi, ad, pvt.
Runs all volume 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?
No annotations are provided, so the description must carry the transparency burden. It explains that the tool runs all volume indicators or a named subset, and that period defaults to '2y' with enumerated values. However, it does not disclose output format, data frequency, ticker validation, or error behavior.
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 concise and front-loaded, with the purpose stated in the first line and parameter semantics in two clear sentences. No unnecessary words.
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 tool's action and parameters well for a 3-parameter tool, but with no output schema and no annotations, it omits what the return value looks like and any underlying assumptions (e.g., daily bars, adjusted prices). This leaves notable gaps for a tool in an analysis family.
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 compensates well: it enumerates the valid indicator names (obv, cmf, mfi, ad, pvt) and restates the period enum with default, adding meaning beyond the bare arrays and strings in the 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 runs volume confirmation indicators (obv, cmf, mfi, ad, pvt) for a ticker, distinguishing it from sibling analysis tools by its volume focus. The verb 'runs' and resource 'volume indicators' are specific, though the opening phrase is telegraphic.
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 a use case via 'Volume confirmation' but does not explicitly say when to use this tool over alternatives like analyze_momentum or analyze_trend. There are no exclusions or alternative tool references, so guidance is only implicit.
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.