get_indicator_history

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

With no annotations provided, the description carries the full behavioral disclosure burden and meets it thoroughly. It discloses immutability, public accessibility, the exact day-file archive format, the 5-minute point granularity, the never-rewritten guarantee, and the three-way verdict behavior (TRUE/FALSE/INSUFFICIENT_DATA). It also explicitly states that it never interpolates across gaps, which is essential for an agent choosing this for verification or settlement.

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 detailed but every section earns its place: a front-loaded purpose summary, then organized Args and Returns blocks with clear formatting. The longest content is behavioral and parameter semantics, which are necessary because schema coverage is zero. There is no filler or repetition.

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 six-parameter tool with no annotations and no schema-level descriptions, this description is complete. It covers purpose, inputs, constraints, return values, edge cases, and verification support. An agent can invoke it correctly and interpret its results without needing further undocumented context.

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 description coverage is 0%, so the description must fully compensate, and it does. Each parameter is clarified beyond the schema: feed_name format and example, inclusive date semantics, the 31-day limit, the conditional need for chain, the exact allowed condition values, and threshold units. The description adds meaning the schema's bare titles do not provide.

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 states a specific verb and resource ('Read Pythia's public, immutable indicator history for a feed over a date range') and immediately distinguishes its scope from live/current tools by emphasizing historical, closed-day, immutable data. The opening sentence alone makes it clear why an agent would select this over siblings like get_feed_value or get_vision_history.

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 gives explicit use cases: auditing, re-deriving conditions, reconstructing what a Vision/Event saw, backtesting before subscribing, and settling prediction-style questions. It also provides concrete operational guidance such as splitting ranges longer than 31 days and only supplying chain when the feed exists on multiple chains. It does not explicitly name an alternative tool for live data, so it stops short of a 5.

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