Zilliqa Insights MCP Server

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by Zilliqa

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct validator metric or listing function. There is no overlap between cosigner/proposer success rates, top lists, earnings, stake, and information lookups.
Naming Consistency5/5
All tools use 'get_' prefix except 'list_validators', which is a common exception. The pattern is uniform and predictable, aiding agent selection.
Tool Count5/5
11 tools cover the core validator insights domain adequately without being excessive. Each tool serves a clear purpose within the scope of performance and earnings monitoring.
Completeness4/5
The set covers individual lookups, top rankings, and earnings breakdowns. Minor gaps exist (e.g., no tool for validator commission rates or historical performance trends), but core workflows are supported.
Average 3.6/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 2 of 2 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on May 5, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
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MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the default time range behavior (last hour) but does not mention other traits like data freshness, caching, or whether earnings are cumulative.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences, no wasted words. The purpose is front-loaded, and the default behavior is clearly stated in the second sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with 3 parameters and no output schema, the description adequately explains the default time range. However, it does not describe the return format or what 'total earnings' represents, which would aid the agent in interpreting results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for all three parameters. The description adds minimal value beyond the schema, merely restating the default behavior for 'startTime' and 'endTime'. No new semantic information is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('gets') and resource ('total earnings for a specific validator'). However, it does not differentiate from sibling tools like 'get_validator_earnings_breakdown' or 'get_top_validators_by_earnings', missing an opportunity to clarify scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as 'get_validator_earnings_breakdown'. The description lacks explicit context for selection, leaving the agent to infer from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must convey behavioral traits. It only states 'retrieves' which implies a read operation, but lacks details on mutability, auth requirements, rate limits, or side effects. Minimal behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single informative sentence, no wasted words. Could potentially add a brief usage note without harming conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with one parameter and no output schema, the description adequately states what is returned (total amount of ZIL). No mention of output format, but this is acceptable given the simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description of the 'validator' parameter. The description does not add any extra meaning beyond what the schema already provides, so it meets the baseline but adds no additional value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'Retrieves' with clear resource 'total amount of ZIL currently delegated to a validator' and adds contextual meaning about consensus weight. Among siblings like get_validator_info and list_validators, this tool is explicitly distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. Does not state prerequisites or scenarios where this is the preferred choice over siblings like get_top_validators_by_stake.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description carries full burden. Only states it 'gets' data (implied read-only) but lacks detail on side effects, auth requirements, rate limits, or return format. Minimal disclosure beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no extraneous words. First sentence states purpose, second gives defaults. Efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, so description should hint at response structure (e.g., fields like validator, success rate). It does not. Also lacks explanation of 'cosigner success rate' metric. Simple tool but left incomplete for agent to infer output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter descriptions including defaults. The description reinforces defaults ('last hour', 'top 5') but adds no new meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'Gets', resource 'top N validators with highest cosigner success rate', and scope 'within a time frame'. It differentiates from siblings like 'get_cosigner_success_rate' (single validator) and 'get_top_proposer_success_rate' (proposers).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives (e.g., 'get_cosigner_success_rate'). Defaults are mentioned but no conditions for when not to use this tool. Usage is implied but not explicitly framed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral traits. It mentions time frame and defaults but does not explain what 'current stake' means (e.g., snapshot at end, average), ordering, or pagination. Significant gaps remain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with main action. Efficient but could include more behavioral details without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With three optional parameters and no output schema, the description covers basic purpose and defaults but lacks return format details, error handling, and precise behavioral semantics. Adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with each parameter described. The description reiterates defaults (last hour, top 5) but adds no new semantic meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets the top N validators by highest current stake within a time frame, with defaults. This distinguishes it from sibling tools like get_top_validators_by_earnings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives (e.g., get_top_validators_by_earnings). The context is implied by the resource name and description, but no direct comparison or selection advice is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions defaults but lacks details on response structure, ordering direction, or potential limitations (e.g., API rate limits). Only basic behavioral traits are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately conveys the core purpose and defaults. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description does not explain the output format or what 'total earnings' means (e.g., currency, period aggregation). Given no output schema, this lack of detail could hinder effective tool use. It also does not mention potential prerequisites.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal value beyond the input schema, which already fully describes parameters including defaults and formats. Schema coverage is 100%, so the baseline is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves top validators by earnings in a time frame, specifying defaults. The name and description distinguish it from siblings like get_top_validators_by_stake, which focuses on stake.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like get_top_validators_by_stake or get_total_validator_earnings. Usage is implied but not directly addressed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry transparency. It discloses default time range behavior and the nature of the breakdown, but does not mention data freshness, error handling, or authorization requirements. Some transparency exists but gaps remain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences. The first sentence conveys the core purpose, and the second adds a useful default behavior note. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's purpose and default behavior but does not hint at the return format or structure, which is relevant since no output schema exists. This leaves some uncertainty.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The description adds minimal semantic value beyond the schema, only restating the time default. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a detailed earnings breakdown for a validator, specifying it separates rewards from block proposals and cosignatures. This distinguishes it from sibling tools focused on success rates or stakes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions default time range behavior but does not explicitly state when to use this tool versus alternatives like success rate tools. Usage guidance is implied but not directly provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It does not disclose whether the operation is read-only, any potential side effects, or performance considerations. Only states it lists known validators.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is direct and contains no unnecessary words. Information is front-loaded with the action and output details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately details the returned fields. However, it omits any mention of ordering or whether the list is exhaustive, which would be helpful for a simple list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is 100%. Description adds value by explaining the output fields, though baseline for 0 parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool lists all known validators and specifies the fields returned. This distinguishes it from sibling tools that focus on specific aspects like earnings or stakes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like 'get_top_validators_by_stake' or 'get_validator_info'. The description only states what it does, not when it is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It does not mention that the operation is read-only, what happens if the identifier is not found, or any return format. The phrase 'all available information' is vague and lacks specifics about the output.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that conveys the essential information without any extraneous words. It is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description is adequate but lacks completeness. There is no output schema, so the description should provide more context on what 'all available information' includes, such as fields or data structure. It does not mention error handling or response format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description restates the parameter's allowed values but adds no additional semantics such as format or case sensitivity beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'gets' and the resource 'all available information for a validator', specifying the identifiers that can be used. It distinguishes from sibling tools which focus on specific metrics like stake or success rate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when general validator info is needed given any identifier, but does not explicitly state when not to use or name alternative tools. The context from sibling tools provides implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It discloses default time range and the metric's importance, but does not reveal the return format, side effects, or permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences, each serving a purpose: definition, context, and defaults. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple query tool with no output schema and no annotations, the description covers purpose, importance, and defaults. It lacks explicit return format, but the metric name implies a percentage, which is sufficient. Sibling context is handled by naming.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description restates default behavior for startTime/endTime (already in schema) and adds no new parameter-level detail beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool measures a validator's cosigning performance, explains that cosigning is the most frequent duty, and links success rate to uptime and rewards. This specificity distinguishes it from sibling tools like get_proposer_success_rate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains default behavior when startTime and endTime are omitted, but does not explicitly guide when to use this tool versus siblings like get_proposer_success_rate or get_top_cosigner_success_rate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided. The description adds behavioral context by specifying default values (last hour, top 5) and implies a read-only operation, but it does not disclose any side effects, authentication needs, or rate limits. Given the absence of annotations, the description provides minimal behavioral transparency beyond defaults.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence (14 words) that front-loads the core action and purpose. Every word is meaningful, with no redundancy or filler. It is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, no output schema, and sibling context, the description is nearly complete. It specifies the main parameters and defaults. However, it could briefly mention the output format (e.g., list of validators with success rates) for full clarity, but this is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters have descriptions in the input schema (100% coverage). The description's mention of defaults ('last hour', 'top 5') adds minimal value beyond the schema's default specifications. The parameter semantics are adequately handled by the schema, and the description does not significantly enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('gets'), the specific resource ('top N validators with the highest proposer success rate'), and the optional time frame. It succinctly conveys the tool's purpose and naturally distinguishes it from siblings like 'get_proposer_success_rate' (single validator) and 'get_top_cosigner_success_rate' (cosigner focus).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions defaults (last hour, top 5) but does not explicitly guide when to use this tool versus alternatives like 'get_top_cosigner_success_rate' or 'get_validator_info'. It lacks explicit when-to-use or when-not-to-use guidance, which is moderate for a tool with many siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses critical behavioral traits: defaults to last hour if times not provided, implications of missed proposals (delay, lost rewards), and the metric's importance as an indicator of node stability and latency. It does not mention any destructive aspects or required permissions, but none are expected for a read-only metric tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with six sentences, each adding meaningful information. It front-loads the core purpose and immediately provides context on implications and defaults. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple metric tool, the description is largely complete, covering purpose, implications, defaults, and parameter usage. However, it lacks explicit mention of the return format (e.g., percentage or ratio) and whether it is read-only, which would be helpful given no output schema or annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents each parameter. The description adds value by explaining the default behavior for startTime and endTime ('defaults to last hour') and clarifying the validator parameter scope. This goes beyond the schema's dry descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool measures a validator's proposer success rate, using specific verbs like 'measures' and specifying the resource as 'validator's performance when proposing a new block'. It differentiates from sibling tools like get_cosigner_success_rate by focusing on proposing rather than cosigning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for assessing validator node stability and network latency, and it provides default behavior for time parameters. However, it does not explicitly contrast with sibling tools or state when not to use it (e.g., for cosigner performance).
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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