Filter and screen stocks based on financial criteria like market cap range, sector, P/E ratio thresholds, dividend yield, or revenue growth. Returns matching ticker symbols with key metrics. Use for value investing, growth stock identification, or portfolio rebalancing analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| criteria | No | Filtering criteria as key-value pairs (e.g. {'sector': 'technology', 'market_cap_min': 1000000000, 'pe_ratio_max': 25}) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the tool is known to be safe. The description adds that it 'returns matching ticker symbols with key metrics,' which discloses the return behavior beyond the annotation. It doesn't describe edge cases like empty results, but the annotation lowers the burden.
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 two sentences, front-loaded with the core function, and every clause adds value—examples of criteria, return behavior, and use cases. No unnecessary filler.
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 single-parameter screening tool with readOnly annotation and no output schema, the description adequately covers purpose, examples, expected return, and usage contexts. It is sufficiently complete for an agent to decide when and how to invoke it.
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 has 100% coverage with a description for the 'criteria' parameter, but the tool description enriches it with concrete examples (e.g., {'sector': 'technology', 'market_cap_min': ...}). This adds practical meaning beyond the schema's generic key-value description.
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 'Filter and screen stocks based on financial criteria' with a specific verb and resource. It lists concrete examples (market cap range, sector, P/E ratio, dividend yield, revenue growth) and differentiates from siblings like get_stock_data by focusing on screening rather than single-stock retrieval.
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 provides explicit use cases: 'value investing, growth stock identification, or portfolio rebalancing analysis.' While it doesn't explicitly exclude alternatives or mention sibling tools, this guidance clearly implies when the tool is appropriate.
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.
The tools are mostly distinct, but get_crypto_prices and track_crypto_portfolio both deal with cryptocurrency data, which could cause confusion. However, the descriptions clarify that one is for general price lookup and the other for portfolio tracking, so the overlap is manageable.
All tool names follow a consistent verb_noun pattern in snake_case: get_crypto_prices, get_exchange_rates, get_stock_data, screen_stocks, track_crypto_portfolio. There is no mixing of naming conventions.
With 5 tools, the server is well-scoped, covering crypto, forex, stocks, and screening without unnecessary bloat. The count is appropriate for the domain.
The server covers the main market data needs for crypto, forex, and equities, plus stock screening. Missing historical data or stock portfolio tracking are minor gaps that agents can work around.