The Crypto Overfitting Index: the monthly share (%) of default-parameter strategy configurations (10 templates × 50 coins) whose out-of-sample Sharpe turned negative — how much of what backtests promise fails on unseen data. Returns the current reading and full history.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare read-only and open-world false. Description adds context on metric calculation and return value (current reading and history), beyond annotations. No behavioral contradictions.
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 sentences that are front-loaded with key information: what the metric is, methodology, and return value. No extraneous content.
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?
Given no parameters, read-only annotations, and no output schema, the description sufficiently explains the tool's purpose and output (current reading and full history) with methodological context. Complete for this simple tool.
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?
No parameters in schema; schema coverage is 100%. Baseline 4 applies as description adds meaning by explaining what the tool returns without needing parameter details.
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 the monthly share of default-parameter strategy configurations with negative out-of-sample Sharpe, specifying methodology (10 templates × 50 coins). It distinguishes from sibling 'check_overfitting' by focusing on a specific index.
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?
No explicit guidance on when to use this tool vs. alternatives like 'check_overfitting' or other siblings. The description implies usage for checking overfitting but lacks when-not or context.
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.
Each tool has a clearly distinct purpose: statistical checks, strategy comparison, historical returns, live signals, DCA, overfitting index, strategy listing, dataset query, backtesting, Q&A search, and glossary. No overlap in functionality.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_overfitting, run_backtest, search_glossary). No mixing of conventions.
11 tools is well-scoped for a crypto backtesting server, covering the full workflow from strategy selection, backtesting, overfitting analysis, to educational queries without excess or deficiency.
The tool set covers core backtesting, overfitting diagnostics, data retrieval, and knowledge base searches. Minor gaps like strategy modification or saving results are present, but the main lifecycle is complete.
