Search Noon Barbari's Q&A knowledge base of direct, data-grounded answers about backtesting, overfitting, validation, indicators, risk management and crypto markets. Returns the top matching questions with their full answers.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Free-text query, e.g. 'why do backtests fail' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds that it returns top matching questions with full answers but does not disclose details like result ordering, count limits, or pagination. 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?
Two sentences, no repetition or fluff. First sentence defines scope, second states return. Perfectly concise and front-loaded.
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 simple search tool with one parameter and no output schema, the description is nearly complete. It covers purpose and return value. Minor gaps: no mention of result count or ordering, but not critical.
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 input schema covers the single parameter 'query' with full description. The description provides an example ('why do backtests fail') but does not add significant meaning beyond the schema. Baseline 3 due to high coverage.
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 searches a specific Q&A knowledge base and returns top matching questions with answers. It uses specific verbs ('Search') and resources ('Noon Barbari's Q&A knowledge base'), distinguishing it from sibling tools like search_glossary or query_dataset.
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 for questions about backtesting, overfitting, etc., but does not explicitly state when to use this tool vs alternatives or provide when-not guidance. Sibling tools exist but are not referenced.
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.
