Screen London Stock Exchange (LSE) — FTSE 100, FTSE 250, AIM.
Args: criteria: filter dict (sector, market cap, dividend yield, etc.) limit: max rows to return
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| criteria | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds the meaning of the 'limit' parameter but no further behavioral traits like rate limits or result details. Consistent 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?
The description is extremely concise: one line for purpose and a bullet-style Args section. No unnecessary words, front-loaded with market info.
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?
Given no output schema, the description omits return format, pagination, or field details. It covers the input clearly but leaves the agent guessing about output structure, which is a gap for a screening 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?
Schema description coverage is 0%, so the description compensates by listing example filter keys (sector, market cap, dividend yield) and defining 'limit' as max rows. This adds meaningful semantics 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 screens London Stock Exchange markets (FTSE 100, FTSE 250, AIM), which is specific and distinguishes it from siblings like screen_us_finviz or screen_japan_tse.
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?
While no explicit when-to-use or when-not-to-use guidance is given, the market names in the description and sibling tool names make it obvious this is for UK equities. Implicitly clear but lacks formal exclusions.
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.
Each tool targets a distinct geographic exchange, with no overlap in markets screened. The descriptions specify the exchange and indices, making it easy for an agent to select the correct tool for a given country.
All tools follow the exact same naming pattern: 'screen_<country>_<exchange>' in snake_case, providing a clear and predictable structure. There are no deviations or mixed conventions.
26 tools is on the high side, but appropriate for a global equities screener covering many countries. Each tool serves a distinct market, so the count is justified by the breadth of coverage.
The set covers most major global equity markets, including US, China, India, Japan, and many others. However, some notable markets like Russia or some European exchanges are missing, and there is no tool for screening ETFs or indices directly.