Get the latest available stock price and market data. Returns current price, daily change, volume, market cap, P/E ratio, dividend yield, 52-week high/low, open, and previous close. Use this for "what's the stock price of X?", "how is AAPL doing?", "check the market", "what's Apple trading at?", or any stock/equity price question.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Stock ticker symbol (e.g., "AAPL", "MSFT", "TSLA") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, non-destructive behavior, so the description is not solely responsible for safety disclosure. It adds 'latest available' freshness context and a detailed list of returned fields, but does not disclose delay, market-hour behavior, or error conditions. No contradiction with annotations.
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?
Two concise sentences: first states action and outputs, second lists example queries. The most important information is front-loaded, and there is no filler or repetition of the schema.
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 one-parameter, read-only tool with no output schema, the description is largely complete: it lists the return metrics and gives concrete triggering examples. It could be slightly more complete by noting the single-symbol scope versus batch/history siblings and by describing the daily-change format, but these are minor for a simple quote tool.
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 only parameter, symbol, is fully described in the schema with example tickers (AAPL, MSFT, TSLA), and the description reinforces those examples in its usage phrases. With 100% schema coverage, the description adds no additional format or syntax beyond the schema, so baseline 3.
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 uses a specific verb ('Get') and resource ('latest available stock price and market data') and enumerates the exact metrics returned, so an agent knows it is a single-stock quote tool. It does not explicitly differentiate from siblings like stock_quote_batch, stock_history, or stock_compare, but the single-ticker examples make the scope clear.
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?
It gives explicit trigger phrases and states to use it for 'any stock/equity price question,' which is clear when-to-use guidance. It does not mention when to prefer stock_quote_batch or stock_history instead, so there are no exclusions or alternative routing.
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.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.