Current price snapshot for one or more tickers (US and Indian).
Returns last price, day change %, day and 52-week ranges, position within
the 52-week range, and volume vs 3-month average, with an `as_of`
timestamp. US quotes are near-real-time; NSE/BSE quotes are ~15 minutes
delayed (disclosed in the response). For to-the-second Indian prices
during market hours, supplement with a live web search.
| Name | Required | Description | Default |
|---|---|---|---|
| tickers | 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 provided, the description carries the full burden of behavioral disclosure. It honestly discloses the delay for Indian quotes ('NSE/BSE quotes are ~15 minutes delayed') and notes that this is 'disclosed in the response.' It also specifies the return fields (last price, day change, ranges, volume context) and the `as_of` timestamp, providing useful behavioral context beyond a simple 'get quote.'
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 extremely concise: two short paragraphs, front-loaded with the core purpose, followed by a compact list of return fields and a note on data latency. No sentence is wasted, and the most important information appears first. It is an excellent example of efficient description writing.
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 a single array parameter and no output schema, this description is remarkably complete. It explains exactly what data is returned, the staleness characteristics for different markets, and the timestamp field. It also gives a practical tip for getting fresher Indian data. The tool's behavior is fully contextualized without needing an output schema.
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 schema provides zero description for the 'tickers' parameter, so the description must compensate. It states 'one or more tickers' and mentions 'US and Indian' markets, adding some meaning beyond the bare schema. However, it does not specify the expected ticker format (e.g., exchange prefixes like 'NSE:RELIANCE'), leaving ambiguity about how to represent Indian securities.
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 opens with 'Current price snapshot for one or more tickers (US and Indian)' – a clear, specific verb-resource pairing that immediately communicates the tool's function. It also distinguishes itself from sibling analysis tools (momentum, technical levels, etc.) by focusing on snapshot data rather than derived analytics.
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 clearly states when to use the tool (getting current price snapshots) and provides an explicit supplement: 'For to-the-second Indian prices during market hours, supplement with a live web search.' It does not explicitly name alternative tools for analysis, but the context of the sibling list makes the distinction obvious.
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.