Historical hit-rate and alpha of Catalyst Edge pick lists — the evidence an agent uses to weight the signal. Optional: list_name filter.
| Name | Required | Description | Default |
|---|---|---|---|
| list_name | No | filter to one pick list (substring match) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses that data is historical and notes the optional list_name filter, but it does not describe return structure, sorting, pagination, or any side effects. It adds some context beyond the schema but leaves gaps.
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?
The description is two concise sentences, front-loaded with the core purpose and followed by the optional filter note. Every word earns its place; no fluff or redundant 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 simple single-parameter read tool without an output schema or annotations, the description covers the key elements: what data is returned, its purpose, and the available filter. It lacks an explicit return format or example, but the simplicity of the tool makes this sufficient.
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 coverage is 100% for the single parameter, list_name, which already includes a description ('filter to one pick list (substring match)') and optionality. The description only repeats that the filter is optional, adding no additional semantics beyond the schema.
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 identifies the resource: historical hit-rate and alpha of Catalyst Edge pick lists. It distinguishes the tool from siblings by focusing on track record rather than current picks or market context. However, it lacks an explicit verb like 'retrieves' or 'returns', instead using an implied verb from the tool name.
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 provides implied usage context ('the evidence an agent uses to weight the signal'), suggesting when the tool would be relevant. However, it does not explicitly contrast with sibling tools or state when not to use it, so alternatives and exclusions are missing.
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.
Each tool addresses a clearly distinct purpose: list picks, per-ticker signal, narrative thesis, sector context, options context, and historical track record. While get_thesis and get_ticker_signal both focus on a single ticker, one is qualitative and the other is quantitative, so an agent can easily choose based on need.
All tool names follow the same get_<noun> pattern, using snake_case consistently. The nouns are descriptive (picks, options_context, sector_lean, thesis, ticker_signal, track_record), making the API predictable and easy to navigate.
Six tools is a well-scoped size for a specialized financial analytics server. Each tool earns its place by covering a distinct aspect of the domain without redundancy or bloat.
The tool surface covers the core lifecycle of catalyst analysis: discover top picks, drill into any ticker's signal, read a narrative thesis, assess sector direction, obtain options context for trade construction, and evaluate historical reliability. There are no obvious dead ends or missing operations for a read-only analytics use case.
