search_tickersResolve a company name or partial symbol to tickers the platform covers. Use this first when the user names a company rather than a symbol.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Company name or partial symbol. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds the scoping constraint ('the platform covers'), which is useful, but does not mention output format, pagination, or edge cases. It meets the baseline but does not go beyond.
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 sentences, front-loaded with the core purpose, and no wasted words. Every sentence contributes value, and the structure is ideal for quick parsing by an AI agent.
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 simple one-parameter, read-only lookup tool with no output schema, the description provides enough context: what it does, its scope, and when to use it. No critical information is missing.
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 100%, so the schema already fully documents the 'query' parameter. The description restates the same meaning without adding new semantic detail, so the baseline of 3 is appropriate.
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 has a specific verb ('resolve'), a clear resource ('tickers'), and a defined scope ('the platform covers'). It clearly distinguishes itself from sibling tools by being the only search/resolution tool in the list.
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?
Explicitly states when to use this tool: 'Use this first when the user names a company rather than a symbol.' This provides clear context for tool selection, even though alternatives are not named.
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.
get_composite and get_consensus both return per-ticker consensus with dimension breakdowns, creating ambiguity; agents might call the wrong one. Other tools are clearly distinct, but this overlap requires extra care.
All tools follow a verb_noun pattern: get_ for single entities, list_ for collections, and rank/search/screen as action verbs. The pattern is consistent and predictable across the entire set.
15 tools is well-scoped for a market intelligence server, covering single-ticker queries, lists, discovery, and market-level signals without unnecessary bloat.
The surface covers core workflows: ticker resolution, consensus, evidence, trades, positioning, screening, and market signals. Minor gaps exist (e.g., historical consensus or direct ticker comparison), but they are not critical.