data_token_activityRecent transfer count, volume, unique senders/receivers, activity level. $0.005 per call.
| Name | Required | Description | Default |
|---|---|---|---|
| blocks | No | Look-back window in blocks (default 2000, ~1 hour on Base) | |
| address | Yes | ERC-20 token contract address on Base (0x-prefixed, 42 chars) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior, so the description does not need to repeat those. The description adds useful context: the returned data categories and the per-call cost ($0.005). However, it does not clarify return format, volume denomination, or how 'recent' relates to the blocks parameter, which would be valuable since no output schema exists.
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 concise sentences with no filler. The first sentence front-loads the tool's output, and the second provides cost transparency. Every word earns its place.
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 simple read-only tool with two parameters and rich annotations, the description is largely complete. It names the key output metrics, and the schema covers the parameters. Minor gaps remain: no explicit statement that 'activity level' is derived from the other metrics and no mention of output shape, but these are not critical for a successful call.
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 100%, with both parameters ('address' and 'blocks') explained in the schema. The description adds no additional parameter-level semantics, but that is acceptable given the schema carries the full burden. Baseline 3 applies because the schema fully documents the parameters.
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 the resource ('Token transfer activity') and the specific data points it provides (transfer count, volume, unique senders/receivers, activity level). It lacks an explicit verb like 'get' or 'retrieve', but the intent is unambiguous. It is distinguishable from siblings like data_token_supply, which focuses on supply, and token_verdict, which likely focuses on verification.
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 no guidance on when to use this tool versus the many sibling tools (e.g., data_token_supply, token_verdict, data_portfolio). There are no explicit conditions, exclusions, or alternative suggestions. Usage is only implied by the name and description, which is weak given the large sibling set.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are cleanly separated by domain prefix (data_, security_, validate_, verify_) and describe distinct outputs. A couple of pairs like token_verdict vs verify_token or data_portfolio vs wallet_dossier could be confused until descriptions are read, but the descriptions resolve the ambiguity.
The majority of tools follow a predictable prefix_subject pattern, e.g. data_block, security_tls, validate_iban, verify_payment. A few outliers like html_to_markdown, text_diff, wallet_dossier, and token_verdict use different conventions, but the overall system remains readable.
20 tools is on the heavy side for the typical MCP server and sits in the 16–25 'feels heavy' zone. Most tools have a legitimate purpose, but the five validate_* identifier tools plus two generic utilities could feel like surface area bloat.
The server covers a broad and coherent read/validation domain: chain data, token safety, payments, security checks, and identifier validators. Minor gaps exist, such as lack of detailed historical transaction/activity data and no general network/domain security scan beyond email, TLS, and typosquat.