validate_candle_data

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the tool's behavior well: it detects specific data quality issues, computes a quality score, auto-determines liquidity tiers, and applies appropriate default thresholds. It also mentions that thresholds can be manually adjusted. It doesn't mention side effects (likely none, as it's a validation tool) or performance characteristics, but the core behavior is well covered.

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 well-structured with a clear category tag, a one-line summary, and then detailed bullet points. It front-loads the core purpose and then provides necessary details about thresholds and adjustments. It's slightly verbose but every sentence adds value. The Japanese language is appropriate for the likely user base.

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 validation tool with no output schema, the description explains what checks are performed and what output to expect (quality score and grade). It also covers the automatic tier detection and threshold behavior. It doesn't describe the exact output format (e.g., JSON structure), but the description of the quality score and grade is sufficient for an agent to understand the tool's purpose and invoke it 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 coverage is 71%, and the description adds meaningful context for the key parameters. It explains that price_sigma and volume_multiplier have tier-based automatic defaults when omitted, which is not in the schema. It also clarifies the date parameter's timezone interpretation and its difference from get_transactions/get_flow_metrics. The description doesn't cover all parameters (tz, pair, type, limit are not explicitly described in the description), but the schema already covers those adequately.

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 purpose: data quality validation for OHLCV candlestick data. It specifies the exact checks performed (completeness, duplicates, OHLCV consistency, price anomalies, volume anomalies) and the output (0-100 quality score with A-F grade). This distinguishes it from sibling tools like get_candles (data retrieval) and analyze_candle_patterns (pattern analysis).

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 states when to use this tool: before analysis or backtesting, to verify data trustworthiness. It also explains the automatic tier-based threshold selection and manual adjustment options. However, it doesn't explicitly name alternative tools or state when NOT to use it, though the context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.