get_validator_infoGet Blueprint validator profile: identity, vote account, commission, active stake, APY, performance, software, location. Live data from StakeWiz API.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds that data is 'Live data from StakeWiz API,' which implies an external call and potential latency, but it does not disclose details like rate limits, caching behavior, or failure modes. This adds some value beyond annotations but remains limited.
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, well-structured sentence that front-loads the purpose ('Get Blueprint validator profile') and then enumerates the included fields. Every phrase adds value, with no filler or repetition.
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, read-only information tool, the description is fairly complete. It lists what data will be returned and notes the live source. However, it does not describe the response format or any potential limitations, and with no output schema, a brief note on structure could improve completeness. Overall, it covers the essentials.
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 input schema has zero parameters, so per the calibration guidance the baseline is 4. The description does not need to explain parameters, and there is nothing else to add. The description appropriately focuses on the result content rather than inputs.
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 gets a 'Blueprint validator profile' and enumerates the specific data points (identity, vote account, commission, active stake, APY, performance, software, location). This uses a specific verb and resource, and the field list distinguishes it from sibling tools like get_staking_apy or get_performance_metrics, which focus on individual 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?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, use cases, or exclusions. The only additional context is the data source ('Live data from StakeWiz API'), which hints at external dependency but does not clarify when to choose this over sibling tools.
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.