FREE: submit YOUR OHLCV bars and get an accuracy verdict against the verified archive — score, worst windows, systematic-offset findings. Tells you what's wrong, not the corrected values (that's get_bars).
| Name | Required | Description | Default |
|---|---|---|---|
| tf | Yes | ||
| bars | Yes | columnar arrays: t (epoch s or ISO) required; o/h/l/c optional | |
| symbol | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosure. It discloses output type (score, worst windows, findings), states it does not return corrected values, and notes it is free. While it doesn't cover side effects or permissions, its read-only audit nature is reasonably transparent.
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 sentences, front-loaded with the core purpose and includes an explicit alternative. Every word earns its place with no fluff or redundancy.
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?
There is no output schema, but the description summarizes the result types (score, worst windows, findings) and explicitly states what is not returned. For a moderate-complexity tool with three parameters and a nested object, this provides sufficient context for an agent to select and invoke the tool correctly.
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 only 33% (only bars has a description). The tool description implies symbol and timeframe but does not explain their formats or constraints. The bars description in the schema clarifies the columnar structure, but overall parameter semantics are only partially supplemented. A score of 3 reflects the minimum viable compensation.
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 audits user-submitted OHLCV bars against a verified archive and returns an accuracy verdict. It explicitly differentiates from get_bars by noting it does not provide corrected values. This distinguishes it from sibling tools.
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 explicitly names get_bars as the alternative for corrected values, implying this tool should be used to identify inaccuracies while get_bars should be used to obtain correct data. This provides clear when-to-use and when-not-to-use guidance.
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.