FREE: submit backtest signals ({t, price}) and get an execution-feasibility verdict — impossible fills outside the bar's traded range, fills pinned at bar extremes (classic lookahead bias).
| Name | Required | Description | Default |
|---|---|---|---|
| tf | Yes | ||
| symbol | Yes | ||
| signals | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It does explain the detection logic (impossible fills outside bar's traded range, pins at extremes), but it does not explicitly state whether the tool is read-only, has side effects, or how results are returned. This leaves some ambiguity about the tool's safety and behavior.
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, efficient sentence that front-loads the key action and includes specific detection criteria. The word 'FREE' is mildly extraneous but not wasteful. Overall, it is concise and well-structured.
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 covers the core purpose and provides some behavioral details, but it lacks information about the return format of the verdict, example usage, or how this tool relates to sibling validation tools. Given no output schema and no parameter descriptions, more context would be needed for full 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 schema has zero parameter descriptions, so the description must compensate. It partially explains the 'signals' parameter structure (t, price) but omits the 'side' field and does not explain 'symbol' or 'tf'. This leaves most parameters underspecified.
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's function: submitting backtest signals and receiving an execution-feasibility verdict. It also distinguishes itself from siblings like survivorship_check and validate_backtest_data by specifically detecting lookahead bias (impossible fills outside bar range, fills at extremes).
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 when one has backtest signals and wants to check execution feasibility. However, it does not explicitly state when to use this tool versus alternatives like validate_backtest_data or audit_my_data, nor does it mention any exclusions or prerequisites.
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.