Hand-curated token ↔ listed-equity links (e.g. RENDER↔NVDA on the GPU/AI-compute demand thesis) with theme, confidence, latest token divergence and related equity revenue growth.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the returned data fields and implies freshness via 'latest token divergence', and 'hand-curated' hints at a manual, possibly non-real-time data source. However, it does not state data-source recency, update cadence, or operational constraints, which is a notable gap for a tool with zero annotation coverage.
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?
A single efficient sentence that front-loads the core purpose ('Hand-curated token ↔ listed-equity links') followed by a clarifying example and a concise field list. No filler or redundancy; every clause contributes information.
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 no-parameter read tool with an output schema, the description adequately conveys what is returned and the cross-asset nature of the data. The 'theme' concept is slightly underspecified, but the existing output schema presumably carries the detailed return structure, so the description covers what an agent needs to select 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?
The tool has zero parameters, which sets a baseline of 4 per the rubric. There is nothing to document for parameters, and the description appropriately spends its words describing the output content (theme, confidence, divergence, revenue growth) rather than schema fields.
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 returns 'Hand-curated token ↔ listed-equity links' and enumerates the fields (theme, confidence, divergence, revenue growth), with the RENDER↔NVDA example concretely illustrating output. The verb is implied rather than explicit, and differentiation from siblings like compare_assets and divergence_radar is implicit via the cross-asset (token↔equity) focus 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?
No guidance on when to use this tool versus alternatives. The purpose implies use for cross-asset analysis, but overlapping siblings such as compare_assets, divergence_radar, and get_asset exist with no routing or exclusions provided. An agent must infer the appropriate use case from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.