Top-N tickers ranked by a factor on the latest available date. Common factors: mom (momentum), meanrev, comp_score, rsi, z_52w, beta_spy.
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | ||
| factor | No | mom | |
| direction | No | desc |
| Name | Required | Description | Default |
|---|---|---|---|
| rows | Yes | ||
| factor | Yes | ||
| direction | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds the 'latest available date' constraint, which is useful behavioral context. However, it does not discuss edge cases like invalid factors or data availability, but with annotations covering safety, this is acceptable.
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?
Two concise sentences. The first states the core purpose, and the second lists factor values. No fluff or redundancy. Every word adds value.
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?
Output schema exists, so return format is defined elsewhere. The description covers the tool's core behavior and the most ambiguous parameter (factor). It's complete for a simple query tool, though it could mention what happens if fewer than N tickers exist, but that's a minor gap.
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 0%, so the description must compensate. It does for the 'factor' parameter by listing common examples (mom, meanrev, etc.). The 'n' and 'direction' parameters are self-explanatory from their types and defaults, so no extra description needed. This is a solid improvement over the bare 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 states the tool returns Top-N tickers ranked by a factor on the latest available date, which is a specific verb+resource+scope. It distinguishes from siblings like get_alerts or get_features by focusing on ranking by factor. The list of common factors adds specificity.
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 usage: use when you want a ranked list of top tickers by a factor. It clarifies context without explicit exclusions or naming alternative tools. No sibling directly competes, so the guidance is adequate but lacks an explicit 'when not to use' statement.
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.
Most tools target distinct resources (features, embeddings, labels, market context, risk clusters), but minor overlap exists: get_market_context includes a regime reading that get_market_regime also provides, and get_report_card bundles features that get_features offers separately. Descriptions are clear enough to resolve these overlaps.
The predominant pattern is get_<noun> (get_features, get_labels, get_manifest, etc.), with two exceptions: find_similar (find_) and list_futures (list_). This is a small deviation but still follows a predictable verb-noun structure for retrieval, search, and enumeration actions.
14 tools is well within the ideal range for a quantitative data server. Each tool serves a distinct purpose, from basic data retrieval (features, labels) to advanced analytics (similarity, risk clusters) and user management (alerts, usage). No tool feels redundant or missing.
The toolset covers the core data access and analytics needs for factor-based market analysis: retrieval, search, market context, and backtesting labels. Minor gaps include no generic ticker search or list (beyond futures), and no direct way to browse available factors beyond documentation, but these can be worked around via get_top and get_manifest.