FREE public scoreboard: last 30 daily fresh-data audits (cross-venue in-band vs Coinbase, 1m->1h rebuild exactness, depth sanity) — failures included, not hidden.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosure. It explicitly reveals that the tool is free and public, shows the last 30 audits, and includes failures rather than hiding them. It does not discuss rate limits or output format, but for a simple zero-parameter getter, this is adequate transparency.
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?
A single sentence delivers a wealth of specific information: public/free, time window (last 30 days), frequency (daily), audit dimensions, and failure visibility. Every clause adds value with no fluff.
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 zero-parameter tool with no output schema, the description is remarkably complete. It tells the agent exactly what the tool returns (a scoreboard of audit results), the scope (last 30 daily audits), the audit categories, and that failures are included, which fully covers the expected behavior.
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 tool has zero parameters, so there is no parameter semantics to explain. The baseline score of 4 is appropriate since the description correctly implies there are no configurable inputs.
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 provides a public scoreboard of the last 30 daily fresh-data audits, listing specific audit dimensions (cross-venue vs Coinbase, rebuild exactness, depth sanity) and notes failures are included. This distinguishes it from sibling data-retrieval and validation 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 makes it clear this is the tool to get recent audit results, and 'FREE public' implies no authentication barrier. However, it does not explicitly name alternative tools (e.g., audit_my_data) or state when not to use this, so usage context is clear but exclusions are absent.
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.