Retrieve analyst ratings and insights for a stock symbol to support investment decisions. Filter by date and limit results to focus on relevant analyst activity.
Fetch analyst insights/ratings for a symbol from Polygon Benzinga feed (requires Polygon API key).
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ||
| limit | No | ||
| symbol | Yes | ||
| api_key | No |
Changes observed during successful MCP inspections.
v0.2.42Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. 'Fetch' signals a non-mutating read and the API-key note discloses an auth prerequisite. However, it does not mention failure behavior for missing/invalid keys, rate limits, data freshness/caching, or response shape — all relevant 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 front-loaded sentence: the verb and resource come first, and the parenthetical API-key prerequisite is the only extra information. Every word earns its place with zero wasted content.
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?
Adequate for a simple read tool — it states the resource, source, and key requirement. But with no output schema and 0% schema coverage, the agent is left guessing about return values and the date parameter's meaning. The relationship to the competing fmp_analyst_recommendations sibling is also unaddressed.
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 must compensate. It adds meaning to symbol ('for a symbol') and api_key ('requires Polygon API key'), but leaves date (format, optionality, filtering semantics) and limit (beyond the schema's self-explanatory default/min/max) completely unexplained. Half the parameters are undocumented in both schema and description.
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 names a specific verb ('Fetch'), a concrete resource ('analyst insights/ratings for a symbol'), and a data source ('Polygon Benzinga feed'), plus a key prerequisite. It does not explicitly contrast with similarly-purposed siblings like fmp_analyst_recommendations, though the Benzinga source naming gives implicit differentiation.
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?
Usage is implied — call this when analyst insights for a symbol are needed and a Polygon API key is available. The 'requires Polygon API key' note is a real precondition, but no alternatives are named and no when-not-to-use conditions are given, especially given the obvious competing sibling fmp_analyst_recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.