Quality report for a dataset spec before you backtest on it: coverage, per-bar quality distribution, gaps, and concrete warnings.
| Name | Required | Description | Default |
|---|---|---|---|
| tf | Yes | ||
| end | No | ||
| start | No | ||
| symbol | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It discloses that the tool produces a report and implies it is a pre-flight check, but it does not explicitly state side effects (e.g., read-only, no data modification) or any rate limits/requirements. This is adequate but not rich.
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 a single, front-loaded sentence that conveys purpose and output content without fluff. Every element earns its place.
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?
The description explains the high-level output categories and usage timing, which is helpful. However, with no output schema and minimal parameter information, the description is incomplete for an agent to safely and correctly invoke the tool across all contexts.
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%, and the description does not compensate. It refers to a 'dataset spec' but never explains individual parameters (symbol, tf, start, end) or their formats. An agent cannot infer how to correctly populate the parameters from the 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 clearly states it produces a 'Quality report for a dataset spec before you backtest on it' and enumerates report contents (coverage, per-bar quality distribution, gaps, warnings). This is specific and action-oriented, but it does not explicitly distinguish itself from siblings like audit_my_data or get_quality_receipts.
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 phrase 'before you backtest on it' provides clear context for when to use the tool. However, it does not mention alternatives or exclusions, such as when to choose a different validation tool like lookahead_check or survivorship_check.
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 clearly target a distinct data resource: bars, events, fundamentals, funding, open interest, order flow, and so on. A few adjacent tools like audit_my_data and validate_backtest_data, or get_market_pulse and get_regime_label, are somewhat similar, but their descriptions provide enough separation for an agent to choose correctly.
The dominant pattern is get_<data_type>, used consistently across most tools and all in lowercase snake_case. The non-get tools are mostly still readable verb-noun names like build_bundle and validate_backtest_data, though lookahead_check and survivorship_check are minor deviations.
With 22 tools, this is on the heavier side for a single MCP server, especially since many tools have fairly specialized data sources. Each tool is individually justifiable, but the overall surface is large and may push agents to spend extra work choosing among near-adjacent data options.
The server covers far more than plain OHLCV: it includes fundamentals, insider and institutional ownership, funding rates, open interest, order flow, events, context, regime labels, and backtest-quality validation. Minor missing areas like trade-by-trade quotes or a broader symbol catalog mechanism exist, but the common market-data workflows are very well supported.