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A2A MCP Server

Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: a2a_server_registry manages server URLs, call_agent invokes a specific agent, and list_agents retrieves agent information. The descriptions clearly differentiate between server management, agent invocation, and agent listing functions.

    Naming Consistency4/5

    The tools follow a consistent snake_case naming convention throughout. However, the naming pattern is mixed: a2a_server_registry uses a prefix+noun+noun structure, while call_agent and list_agents use verb+noun. This minor deviation prevents a perfect score.

    Tool Count3/5

    With only 3 tools, the server feels somewhat thin for an A2A (Agent-to-Agent) server that presumably facilitates multi-agent interactions. While the tools cover basic operations (registry management, agent calling, and listing), the scope suggests more functionality could be expected, such as agent configuration or monitoring tools.

    Completeness3/5

    The tools provide core operations for server registration and agent interaction, but there are notable gaps. For example, there's no way to update server details, manage agent states beyond listing, or handle error scenarios explicitly. The surface covers basic workflows but lacks comprehensive lifecycle management for the domain.

  • Average 3.1/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
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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 the full burden of behavioral disclosure. It states the action but lacks critical details: it doesn't specify if this is a read-only or mutative operation, what permissions or authentication are required, potential rate limits, error handling, or what the 'call' entails (e.g., synchronous/asynchronous). This leaves significant gaps for an AI 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is front-loaded with the core purpose in the first sentence, followed by structured sections for args and returns. It avoids unnecessary fluff, with each sentence serving a clear purpose. However, the 'Returns' section could be more informative, and overall it's slightly terse but 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?

    Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is minimally adequate. The output schema likely covers return values, reducing the need for detailed return explanations. However, it lacks context on behavioral aspects (e.g., side effects, error cases) and usage guidelines, making it incomplete for safe and effective tool invocation.

    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 lists the parameters ('agent_name' and 'prompt') and provides basic semantics (e.g., 'Name of the agent to call'), which adds value beyond the input schema's 0% description coverage. However, it doesn't elaborate on constraints (e.g., format of agent names, prompt length limits) or provide examples, so it only partially compensates for the schema's lack of detail.

    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 ('Call an agent') and the resource ('with a prompt'), making the purpose immediately understandable. It distinguishes this from sibling tools like 'list_agents' by focusing on interaction rather than listing. However, it doesn't specify what 'calling' entails (e.g., is it an API call, a simulation, or a direct execution?), which prevents a perfect 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?

    No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing to know agent names from 'list_agents'), nor does it differentiate from sibling tools like 'a2a_server_registry'. The description assumes the user already understands the context, offering no explicit usage 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 the full burden of behavioral disclosure. It mentions the return type ('Dict with agents and their cards'), which adds some value, but lacks details on permissions, rate limits, or other behavioral traits like pagination 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is brief and front-loaded with the main action, using only two sentences. However, the second sentence about returns could be integrated more smoothly, and there's minor room for improvement in flow.

    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?

    Given the tool has no parameters, an output schema exists, and no annotations, the description is minimally adequate. It explains the purpose and return value, but lacks context on usage guidelines and behavioral details, leaving gaps for an agent to understand when and how to use it 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?

    There are 0 parameters, and the schema description coverage is 100%, so no parameter information is needed. The description doesn't add param semantics, but this is acceptable given the lack of parameters, warranting a baseline score of 4.

    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 ('List') and resource ('available agents with their agent cards'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'a2a_server_registry' or 'call_agent', which prevents a perfect 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?

    No guidance is provided on when to use this tool versus alternatives like 'a2a_server_registry' or 'call_agent'. The description only states what it does, not the context or prerequisites for its use.

    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. It mentions that the tool performs 'add or remove' actions, implying mutation, but doesn't disclose behavioral traits like permissions needed, side effects, error handling, or rate limits. The return value is vaguely described as 'Dict with status and message,' lacking detail on structure or possible outcomes.

    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 appropriately sized and front-loaded, starting with the purpose in the first sentence. The parameter and return sections are structured clearly, but the return description could be more specific. Overall, it's efficient with minimal waste.

    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?

    Given the complexity (mutation tool with no annotations) and schema richness (0% coverage, but output schema exists), the description is partially complete. It covers parameters well but lacks behavioral context and detailed return values. The output schema existence means it doesn't need to fully explain returns, but more guidance on usage and effects would improve completeness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains each parameter: 'action' as either 'add' or 'remove,' 'name' as the server name, and 'url' as required for 'add' action. This compensates fully for the schema's lack of descriptions, making the parameters clear and actionable.

    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 purpose as 'Add or remove an A2A server URL,' which specifies the verb (add/remove) and resource (A2A server URL). However, it doesn't differentiate this tool from its siblings (call_agent, list_agents), which appear unrelated, so it doesn't fully address sibling 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 provides no guidance on when to use this tool versus alternatives or in what context. It lists parameters but doesn't explain prerequisites, such as when adding or removing is appropriate, or how it relates to other tools like call_agent or list_agents.

    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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