get_metric_historyTime series of one snapshot metric across Orion's daily reports, oldest to newest. Metrics: height, total_fee_tia, total_blobs_tb, total_tx, total_validators, active_validators, tia_price, bonded_ratio, bonded_ratio_supply, nakamoto_halting, jail_count.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | How many recent days (default 30) | |
| metric | Yes | Which snapshot metric to chart |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the ordering (oldest to newest) and the 'one snapshot metric' constraint, but it does not mention response format, pagination, or error behavior. The read-only nature is implicit rather than explicit.
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 efficient sentence that front-loads the action and then provides the metric list. Every element is informative, with no wasted words.
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 tool with only two parameters and no output schema, the description covers the core purpose and available metrics. However, it does not specify the shape of the returned time series (e.g., array of date/value objects), which is a notable gap given there is no output schema to clarify it.
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 coverage is 100%, so the schema fully documents both parameters. The description adds slight context by clarifying 'time series' and listing valid metric values, but it does not substantially extend parameter semantics beyond the schema's own descriptions.
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 a specific action: retrieving a time series for a single snapshot metric from Orion's daily reports, ordered oldest to newest. It enumerates the available metrics, which distinguishes it from broader report-list tools like list_reports or search_reports.
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 implies usage for historical metric retrieval but does not explicitly contrast with sibling tools like get_network_state (current state) or get_trends. The context 'across Orion's daily reports' provides a clear setting, but there are no exclusionary statements or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.