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

With no annotations present, the description carries the full transparency burden and does so well. It reveals the tool is paid, states the exact cost, clarifies the data is from the last poll rather than real-time, and describes the provenance included in the result.

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 three tightly packed sentences, with cost, purpose, exclusions, and input semantics all included. Every sentence earns its place, and the warning-oriented structure makes it easy for an agent to decide when to call this tool.

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 output schema, no annotations, and only one required parameter, the description fully covers the necessary context: current snapshot state, provenance, payment, valid input sourcing, optional key narrowing, and intended use cases. The agent can select and invoke the tool correctly from this description alone.

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 description significantly compensates for 0% schema coverage by explaining dataset ('exact name from anansi_search') and key ('optionally narrows to one row'). However, the payment_header parameter is neither named nor explained in connection with the x402 payment, leaving a small but real gap.

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 what the tool does: returns the current state of every tracked key in a dataset as of the last poll, with provenance and licence. This specific snapshot description distinguishes it from siblings like anansi_data_changes and anansi_price_current.

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?

Usage guidance is explicit: use it for the newest whole-dataset snapshot, with concrete examples like prediction-market probabilities and gas by chain. It also gives explicit exclusions, naming anansi_price_current for LLM prices and anansi_data_changes for change-over-time, and requires dataset names from anansi_search.

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