AgentZone MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but get_analytics and get_stats could cause some confusion as both relate to network data. The descriptions help differentiate them: get_analytics focuses on time-series data, while get_stats provides quick statistics, but the overlap in domain might lead to occasional misselection by an agent.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case, such as check_health, discover_agents, and register_agent. This predictability makes it easy for agents to understand and use the tools without confusion from mixed conventions.
Tool Count5/5
With 8 tools, the count is well-scoped for the AgentZone domain, covering key operations like health checks, agent discovery, registration, and analytics. Each tool appears to serve a specific function without redundancy, making the set manageable and purposeful.
Completeness4/5
The tool surface covers core workflows for agent management and network interaction, including discovery, registration, and analytics. A minor gap is the lack of update or delete operations for agents, which might limit lifecycle management, but agents can still perform essential tasks effectively.
Average 3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'discovery with filtering' but doesn't describe what the tool returns (e.g., list of agents, metadata), whether it's paginated, rate-limited, or requires authentication. For a discovery tool with 5 parameters, this leaves significant behavioral gaps unaddressed.
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, efficient phrase that front-loads the core purpose without unnecessary words. Every word earns its place: 'Machine-to-machine' specifies the context, 'agent discovery' states the action, and 'with filtering' adds the key constraint. No waste or redundancy is present.
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?
Given the tool's complexity (5 parameters, no output schema, no annotations), the description is insufficient. It doesn't explain what 'discovery' entails (e.g., returns agent profiles, availability), how results are structured, or any behavioral traits like rate limits. For a filtering tool with multiple options, more context is needed to guide effective use.
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 has 100% coverage with detailed parameter descriptions. The phrase 'with filtering' implies the parameters are for filtering purposes, but this is already evident from the schema. No additional context about parameter interactions, default behaviors, or semantic meaning is provided, meeting the baseline for high schema coverage.
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 tool's purpose as 'Machine-to-machine agent discovery with filtering', which specifies the verb (discovery) and resource (agents) with a key constraint (filtering). It distinguishes from siblings like 'get_agent' (single agent retrieval) and 'search_agents' (likely broader search), though it doesn't explicitly name these alternatives. The purpose is specific but could be more differentiated.
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?
The description provides no guidance on when to use this tool versus alternatives like 'search_agents' or 'get_agent'. It mentions filtering but doesn't clarify if this is the primary discovery method or when other tools might be more appropriate. No context about prerequisites, exclusions, or typical use cases is 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a read operation ('Get'), but doesn't describe what 'detailed information' includes, whether authentication is required, rate limits, error conditions, or response format. This leaves significant gaps for a tool that presumably returns structured data.
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, efficient sentence that immediately communicates the core functionality without any wasted words. It's appropriately sized for a simple lookup tool and front-loads the essential 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?
For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what constitutes 'detailed information', the response structure, potential error cases, or how this differs from similar sibling tools. The agent would need to guess about the return format and behavioral characteristics.
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 description coverage is 100%, so the schema already fully documents the single 'wallet_address' parameter with format examples. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score for high schema coverage.
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 ('Get detailed information') and target resource ('about a specific agent by wallet address'), making the purpose immediately understandable. However, it doesn't differentiate this from sibling tools like 'discover_agents' or 'search_agents' that might also retrieve agent information, so it doesn't reach the highest score.
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?
The description provides no guidance on when to use this tool versus alternatives like 'discover_agents' or 'search_agents'. It mentions retrieving information for a 'specific agent', but doesn't clarify prerequisites, exclusions, or comparative use cases with sibling tools.
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 carries the full burden of behavioral disclosure. It states the tool retrieves data but doesn't specify if it's read-only, requires permissions, has rate limits, or what the output format looks like (e.g., JSON structure, pagination). This leaves significant gaps for a tool that likely involves data access.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy to parse quickly, though it could benefit from more detail given the lack of annotations and sibling context.
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?
Given the complexity of analytics tools, no annotations, no output schema, and multiple sibling tools, the description is incomplete. It doesn't explain what 'network analytics' entails, how results are returned, or differentiate from similar tools, leaving the agent with insufficient context to use it effectively without trial and error.
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, with the 'time_range' parameter well-documented via enum and description. The tool description adds no additional parameter semantics beyond what the schema provides, such as explaining how the time range affects analytics or default behavior, so it meets the baseline for high schema coverage without extra value.
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 ('Get') and the resource ('network analytics and time-series data'), making the purpose understandable. However, it doesn't differentiate this tool from sibling tools like 'get_stats' or 'check_health', which might also provide analytics or health metrics, leaving some ambiguity about its specific 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?
The description provides no guidance on when to use this tool versus alternatives like 'get_stats' or 'check_health'. It lacks context about prerequisites, such as whether network data must be available or if authentication is needed, and offers no explicit when-not-to-use scenarios or comparisons with siblings.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'requires API key' which adds some context about authentication needs, but fails to describe what the registration does (e.g., creates a persistent record, returns an agent ID), potential side effects, or error conditions. This is inadequate for a mutation tool with zero annotation coverage.
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, efficient sentence that states the core purpose upfront with no wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.
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?
For a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after registration (e.g., returns an agent ID, confirmation), error handling, or system implications. Given the complexity (5 parameters including nested objects) and lack of structured behavioral data, more context is needed.
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 description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 where the schema does the heavy lifting.
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 ('Register') and resource ('a new AI agent'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'discover_agents' or 'get_agent', but the verb 'register' implies creation rather than retrieval, providing basic distinction.
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?
The description mentions 'requires API key' which hints at a prerequisite, but provides no guidance on when to use this tool versus alternatives like 'search_agents' or 'get_agent'. There's no explicit when/when-not context or comparison to sibling tools, leaving usage ambiguous.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions the API key requirement, but fails to describe other critical traits such as whether this is a read/write operation, potential side effects (e.g., database updates), error handling, or rate limits. This leaves significant gaps for an agent to understand the tool's behavior.
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, efficient sentence with zero waste. It is appropriately sized and front-loaded, clearly stating the core purpose and a key requirement without unnecessary elaboration.
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?
Given the complexity of a payment transaction tool with no annotations and no output schema, the description is incomplete. It lacks details on what the tool returns, error conditions, or how it interacts with the system, making it insufficient for an agent to use effectively without additional context.
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, so the schema already documents all parameters (agent_id, amount_usdc, tx_hash, chain_id) adequately. The description adds no additional parameter semantics beyond what's in the schema, resulting in a baseline score of 3.
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 verb ('Report') and resource ('an x402 payment transaction'), making the purpose specific and understandable. However, it doesn't differentiate from sibling tools like 'get_analytics' or 'get_stats' that might involve payment data, leaving room for improvement.
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?
The description mentions 'requires API key' as a prerequisite, but provides no guidance on when to use this tool versus alternatives like 'register_agent' or other siblings. It lacks explicit when/when-not instructions or comparisons to similar tools.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions search criteria but doesn't describe what the search returns (list of agents? metadata?), pagination behavior, performance characteristics, or authentication requirements. For a search tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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, efficient sentence that communicates the core functionality without waste. It's appropriately sized for a search tool and front-loads the essential 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?
For a search tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the search returns, how results are structured, whether there are limitations or constraints, or how it differs from sibling tools. The agent lacks sufficient context to use this tool effectively.
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 description coverage is 100%, so all parameters are documented in the schema. The description adds minimal value beyond what's already in the schema - it mentions search criteria that map to the 'query' parameter but doesn't provide additional context about how 'capability, chain, or trust threshold' should be formatted or used. Baseline 3 is appropriate when schema does the heavy lifting.
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 verb 'search' and the resource 'AI agents', with specific search criteria (query, capability, chain, trust threshold). It distinguishes this as a search operation rather than retrieval or discovery. However, it doesn't explicitly differentiate from sibling 'discover_agents' which might have overlapping functionality.
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?
The description provides no guidance on when to use this tool versus alternatives like 'discover_agents' or 'get_agent'. There's no mention of prerequisites, use cases, or limitations. The agent must infer usage from the name and parameters 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 are provided, so the description carries the full burden of behavioral disclosure. It states the tool checks health status but does not describe what 'health status' entails (e.g., uptime, response metrics, error rates), how the check is performed, or what the output might look like. This leaves significant gaps in understanding the tool's behavior beyond its basic purpose.
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, efficient sentence with no wasted words. It is front-loaded with the core purpose and appropriately sized for a simple health-check tool. Every part of the description earns its place by clearly stating the tool's function.
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?
Given the tool's simplicity (0 parameters, no output schema), the description is minimal but incomplete. It lacks details on what 'health status' means, expected output format, or any behavioral traits. Without annotations or output schema, the description should provide more context to fully inform usage, but it does not compensate for these gaps.
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?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not add parameter details, which is appropriate. Baseline is 4 for zero parameters, as the description correctly avoids unnecessary parameter information.
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 'Check AgentZone API health status' clearly states the action (check) and target resource (AgentZone API health status), which is specific and unambiguous. It doesn't explicitly differentiate from sibling tools like 'get_stats' or 'get_analytics', but the focus on 'health status' provides reasonable distinction. This avoids tautology and is not misleading.
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?
The description provides no guidance on when to use this tool versus alternatives like 'get_stats' or 'get_analytics', which might also provide system status information. There is no mention of prerequisites, context, or exclusions. Usage is implied only by the tool name and description, with no explicit instructions.
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?
With no annotations provided, the description carries full burden but only states 'quick' network statistics without clarifying what 'quick' means (e.g., cached data, limited scope, or performance implication). It doesn't disclose behavioral traits like rate limits, authentication needs, data freshness, or error handling.
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, efficient sentence with zero wasted words. It's front-loaded with the core purpose ('Get quick network statistics') and doesn't include extraneous details, making it easy for an agent to parse quickly.
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?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'network statistics' includes, the format of the return value, or any side effects. Given the lack of structured data, more context is needed for the agent to use this tool effectively.
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?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a baseline score of 4 for not adding unnecessary information beyond the schema.
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 'Get quick network statistics' clearly states the action (get) and resource (network statistics). It distinguishes from siblings like 'check_health' or 'get_analytics' by specifying network focus. However, it doesn't explicitly differentiate from all siblings (e.g., 'get_agent' also retrieves data).
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?
The description provides no guidance on when to use this tool versus alternatives like 'get_analytics' or 'check_health'. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
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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