search_stocksFind tokenized stocks by ticker or company name. Returns every match with its issuers and chains, alphabetical by ticker. If a ticker is a real stock but has not been tokenized, this reports that too, which is a correct answer rather than an empty result.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | A ticker (NVDA) or part of a company name (Nvidia). Case insensitive. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does disclose real behavior: it returns every match with issuers and chains, orders results alphabetically by ticker, and — most valuably — explains that a real-but-untokenized ticker produces a reported non-match rather than an empty result. It omits pagination/result limits, but the edge-case semantics are non-obvious and well covered.
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?
Three short sentences with zero filler: purpose first, then return shape, then the surprising edge case. The most decision-relevant constraint (no fake empty results) is deliberately saved for last where it will be read.
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?
With no output schema and no annotations, the description does the work of explaining return contents, ordering, and the untokenized-ticker outcome. It stops short of covering result limits or how it relates to get_stock, but it is sufficient to call the tool correctly.
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% and the schema already documents the ticker/company-name format and case insensitivity, so the description adds no new parameter meaning. Baseline 3 applies when the schema does the heavy lifting.
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?
States a specific verb (Find) and resource (tokenized stocks) with the matching key (ticker or company name), which is enough to separate it from a singular lookup like get_stock. It never names a sibling, so the differentiation is inferred from the plural 'search' framing rather than stated.
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 implies a discovery use case ('by ticker or company name') but never says when to pick this over get_stock or the list_* tools, nor what conditions make it the wrong choice. No alternatives or exclusions are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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