get_top_namespacesLargest namespaces (rollups) on Celestia by total blob size, live from Celenium. Shows who is posting the most data to the DA layer.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many namespaces (default 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that data is live and sorted by total blob size, but does not mention return format, pagination, or any side effects. For a read tool, this is moderately adequate but lacks depth.
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 sentences with zero waste, front-loading the primary purpose and adding a helpful clarifying detail. 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?
The tool has low complexity: one optional parameter, no output schema, and no annotations. The description covers the main purpose and data source. While it doesn't describe the response structure, the simplicity of the tool makes this acceptable.
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% as the 'limit' parameter has a clear description ('How many namespaces (default 10)'). The tool description adds no extra parameter context, so the baseline of 3 is appropriate.
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 the verb and resource: 'Largest namespaces (rollups) on Celestia by total blob size' directly indicates what the tool returns. It distinguishes from siblings like get_top_validators (different resource) and get_namespace (singular focus).
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?
Provides context ('live from Celenium', 'who is posting the most data to the DA layer') implying when to use it, but does not explicitly mention when not to use it or point to alternative tools among the siblings.
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.
Each tool targets a distinct aspect of Celestia data: daily briefs, metric history, namespace details, network state, full reports, signals, top entities, trends, listing, and search. There is minimal overlap, and descriptions clarify the differences.
All tool names follow a consistent verb_noun pattern using lowercase snake_case (e.g., get_daily_brief, list_reports, search_reports). No mixing of conventions.
11 tools is well-scoped for a blockchain intelligence server, covering current state, historical data, lists, and search without being overwhelming or too sparse.
The tool surface provides comprehensive coverage for querying Celestia network status, historical metrics, anomalies, top namespaces/validators, and report retrieval. No obvious gaps for the stated purpose of intelligence gathering.