Look up a stock or asset by ticker or name. Returns every tokenised version the record holds, grouped by legal wrapper, plus tokens seen on chain that are not yet resolved. Versions are unranked and listed alphabetically.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Ticker (e.g. "NVDA") or company name (e.g. "NVIDIA") |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses several behavioral traits: results are grouped by legal wrapper, unranked, listed alphabetically, and include unresolved on-chain tokens. This goes beyond a simple 'look up' statement and gives the agent useful expectations about output structure.
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 dense sentences, each earning its place. The purpose is front-loaded, and the output behavior is summarized without extraneous wording. It is structured logically from invocation to result details.
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 lookup tool with no output schema, the description covers both input and output well: search method, grouping, ordering, and inclusion of unresolved tokens. Some domain terms like 'legal wrapper' are not expanded, but the overall description is sufficient for correct invocation and interpretation.
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 input schema already fully documents the query parameter with examples, and schema coverage is 100%. The description only repeats that lookups can be by ticker or name, adding no new semantic detail beyond the schema. Baseline 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 opens with a clear verb and resource: 'Look up a stock or asset by ticker or name.' It then explains the output specifics (tokenised versions, grouping by legal wrapper, unresolved on-chain tokens), which sets it apart from sibling tools like lookup_token by focusing on stocks/assets and their token representations.
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 clearly states when to use the tool: when you need to look up a stock or asset by ticker or name. It does not explicitly mention alternatives or exclusions, but the usage context is unambiguous and self-contained.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.