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xiaobenyang-com

JSON-RPC

by xiaobenyang-com
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TypeScript

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: rpc_call executes specific JSON-RPC methods with parameters, while rpc_discover queries a server for available methods. There is no overlap in functionality, and an agent can easily differentiate between calling a method and discovering what methods exist.

    Naming Consistency5/5

    Both tools follow a consistent snake_case naming pattern with the 'rpc_' prefix, clearly indicating their domain. The verbs 'call' and 'discover' are distinct and appropriate for their respective actions, maintaining a predictable structure throughout the set.

    Tool Count3/5

    With only two tools, the server feels thin for a JSON-RPC domain, as it lacks operations like listing servers, handling authentication, or managing connections. While the tools cover basic calling and discovery, the count is borderline minimal for a server that could support more comprehensive RPC interactions.

    Completeness2/5

    The tool surface is significantly incomplete for a JSON-RPC server. It provides call and discovery but misses essential operations such as server management (e.g., add/remove servers), error handling, batch requests, or method documentation retrieval. This will likely cause agent failures when attempting more complex RPC workflows.

  • Average 2.9/5 across 2 of 2 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

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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 this is for 'discovery' and part of the OpenRPC Specification, but doesn't describe what the tool actually returns (method list format), error conditions, authentication requirements, rate limits, or other behavioral traits. The description is insufficient for a tool with no 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.

    Conciseness4/5

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

    The description is appropriately concise with two sentences. The first explains the technical implementation, and the second provides a user prompt example. Both sentences earn their place by providing different types of information. It could be slightly more front-loaded with the primary purpose.

    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 no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain what the tool returns (critical for a discovery tool), doesn't cover error cases, and provides minimal parameter guidance. For a tool that presumably returns method information, the lack of output description is a significant gap.

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

    Parameters2/5

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

    Schema description coverage is 0%, so the description must compensate. While it mentions the 'server url' parameter in the example prompt, it doesn't explain what format the server URL should be, whether it needs authentication credentials, or any constraints. The description adds minimal value beyond what the bare schema provides.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool 'uses JSON-RPC to call `rpc.discover`' which indicates it performs discovery for JSON-RPC servers. However, it's somewhat vague about what 'discovery' entails (listing available methods) and doesn't explicitly differentiate from the sibling tool 'rpc_call' beyond mentioning the specific RPC method name.

    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 provides clear context for when to use this tool: when a user wants to know 'What JSON-RPC methods does this server have?' and includes the required server URL parameter. It doesn't explicitly state when NOT to use it or mention the alternative 'rpc_call' tool, but the example prompt gives good usage guidance.

    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 mentions calling JSON-RPC methods but fails to describe key traits like error handling, authentication needs, rate limits, or what the response looks like. For a tool that performs remote calls with potential side effects, this is a significant gap in transparency.

    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 appropriately sized and front-loaded: it starts with a clear purpose statement and follows with a concise example prompt. Every sentence earns its place by directly supporting understanding, with no wasted words or redundancy.

    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 (performing JSON-RPC calls), lack of annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't cover behavioral aspects, parameter details, or return values, making it inadequate for safe and effective use by an AI agent.

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

    Parameters2/5

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

    Schema description coverage is 0%, so the schema provides no parameter details. The description lists parameters in the example ('method', 'server url', 'params') but adds minimal semantics—it doesn't explain what 'params' should contain (e.g., JSON string), format requirements, or valid values. This insufficiently compensates for the lack of schema documentation.

    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: 'Call any JSON-RPC method on a server with parameters.' It specifies the verb ('Call'), resource ('JSON-RPC method'), and scope ('on a server'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling 'rpc_discover' beyond the example prompt, which slightly limits distinction.

    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 provides an example prompt ('Call method <method> on <server url> with params <params>'), which implies usage context for invoking JSON-RPC methods. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'rpc_discover'), prerequisites, or exclusions, leaving usage somewhat inferred rather than clearly defined.

    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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