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

With no annotations, the description carries the full burden, and it does disclose real behavior beyond the schema: it fetches README.md and GOVERNANCE.md from the dpyc-community repo and assembles a context answer. It still omits freshness/caching behavior and confirms nothing about read-only safety or failure modes, but the sourcing disclosure is substantive.

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 tight sentences: the purpose is front-loaded in the first line and the data-sourcing mechanism follows immediately. Nothing is redundant or padded.

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 zero-parameter read tool with an output schema and no annotations, the description supplies the essentials an agent needs: what the answer covers and where it comes from. Return-value detail is legitimately offloaded to the output schema, so remaining gaps are minor.

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 tool takes zero parameters, which is the baseline-4 case, and the empty schema (additionalProperties: false) leaves nothing for the description to clarify. No parameter syntax or format is needed here.

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?

States a specific resource and scope: extended narration about DPYC, the Social Contract, and the Oracle, and names the mechanism (assembling content from README.md and GOVERNANCE.md). This distinguishes it as the general deep-dive tool, though it does not explicitly differentiate itself from topic-specific siblings like get_rulebook or economic_model.

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 word 'Extended' implies this is the comprehensive overview option rather than a targeted lookup, which gives an agent a usable signal. However, there is no explicit when-to-use/when-not guidance and no named alternative against a sibling set that clearly overlaps (get_rulebook, how_to_join, economic_model, network_advisory).

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