get_performance_metricsGet Blueprint validator performance: vote success rate, uptime, skip rate, epoch credits, delinquency status.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is established. The description adds context about the specific metrics included, which is useful. However, it does not disclose additional behaviors such as data freshness, failure handling, or whether results are per-validator or aggregated. Given the strong annotation coverage, the added value is moderate.
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, focused sentence that directly states the purpose and key output fields, with no unnecessary words. It is well-structured and front-loaded.
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 zero-parameter tool with clear annotations and no output schema, the description sufficiently communicates the return value by listing the performance metrics. It could specify whether data is for a single validator or all validators, but this is a minor gap given the context.
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?
There are zero parameters in the input schema, so schema coverage is trivially complete. The description does not need to elaborate on parameter semantics, and with no parameters, a baseline of 4 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 tool retrieves Blueprint validator performance with specific metrics (vote success rate, uptime, skip rate, epoch credits, delinquency status). The verb 'Get' and resource are specific, and it distinguishes from sibling tools like get_validator_info by focusing on performance metrics.
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?
No guidance is provided on when to choose this tool over alternatives. Sibling tools like get_validator_info or get_infrastructure exist, but the description does not mention them or offer comparisons. Usage is only implied by the name and description.
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 have distinct purposes, but there is some overlap between the 'stake' and 'create_stake_transaction' tools, as well as between 'unstake' and 'create_unstake_transaction', and 'withdraw' and 'withdraw_stake', which could cause confusion. However, descriptions clearly differentiate between automated and advanced manual use cases, mitigating ambiguity.
Tool names follow a consistent verb_noun pattern throughout, with clear and descriptive naming (e.g., check_balance, get_staking_summary, register_webhook). There are no deviations in style or convention, making the set highly predictable and readable.
With 26 tools, the count is borderline high for the staking domain, potentially overwhelming for agents. While many tools provide specialized functionality, some could be consolidated (e.g., multiple transaction-building tools), making the set feel slightly heavy but still within a reasonable scope.
The toolset comprehensively covers the staking lifecycle, including address checking, balance queries, staking, unstaking, withdrawing, monitoring, simulation, and verification. There are no obvious gaps, and tools provide clear guidance and alternatives, ensuring agents can handle all necessary operations without dead ends.