[DATA] Live economy snapshot from the dataset: agent count, total USDC in circulation, total transactions (2.5M+), treasury, Gini coefficient. ($0.001 USDC.)
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
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No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does disclose a $0.001 USDC cost and a live nature, which is useful. However, it does not state whether the call is read-only, how the snapshot is generated, or any caveats about the data source.
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 a single, front-loaded sentence: it labels the data category, lists the key metrics, and includes the cost. Every element earns its place with no redundant wording.
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 parameterless tool with an output schema, the description is largely complete: it names the data content and cost. It could be more complete by distinguishing itself from the related get_economy sibling, but this is a minor gap.
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 has zero parameters and the schema is empty, so there is minimal semantic burden. The description adds a cost note and the specific data points returned, which is appropriate context beyond the schema.
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 identifies a live economy snapshot with specific metrics: agent count, USDC in circulation, total transactions, treasury, and Gini coefficient. It does not explicitly differentiate itself from the sibling get_economy, but the subject and resource are unambiguous.
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?
There is no guidance on when to use this tool over alternatives like get_economy or data_transactions. The 'live snapshot' framing implies a current dataset view, but no explicit context, exclusions, or alternative routing is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.