get_data_qualityReturn public read-only data-quality warnings. These are review signals, never automatic corrections.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| threshold | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context beyond annotations by clarifying that the warnings are review signals and never automatic corrections. This complements the annotations (readOnlyHint, destructiveHint) with practical usage intent.
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 short sentences with no filler text. Front-loaded with the core purpose, and 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?
While annotations and an output schema exist, the description fails to cover the parameters, which is a significant gap. The tool is simple, but missing parameter semantics reduces completeness.
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 has 2 parameters (limit, threshold) with 0% schema description coverage. The description does not mention or explain these parameters, leaving the agent without guidance on how to configure the request.
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 'public read-only data-quality warnings' and distinguishes them from 'automatic corrections'. The verb 'return' and resource 'data-quality warnings' are specific and differentiate from sibling tools that retrieve other types of data.
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 indicates these are review signals, not corrections, implying the tool is for observational use. However, it lacks explicit guidance on when to use this tool versus siblings, and no alternative tools are mentioned.
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 have distinct purposes, but there is potential confusion between get_confluence_signals and get_signal_history (both dealing with signals) and between get_fund and get_stock (both about holdings). Descriptions help clarify, but minor overlap remains.
All tool names follow a consistent verb_noun pattern (e.g., get_fund, list_funds, preview_watchlist) and use snake_case throughout, making them predictable and easy to understand.
With 13 tools, the server is well-scoped for its domain of 13F filings and research. Each tool serves a clear function, and the count is neither too sparse nor overwhelming.
The tool set covers key operations like listing funds, getting holdings, signal history, and quality warnings. However, it lacks a tool for searching tickers or funds by name, which could be a minor gap for some use cases.