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ricleedo

JSON MCP Boilerplate

by ricleedo
OverviewSchemaRelated ServersScoreDiscussions
JavaScript

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: json_extract is for targeted data extraction and transformation, while json_read is for initial exploration and schema understanding. Their descriptions explicitly guide when to use each, eliminating any ambiguity or overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (json_extract and json_read) with the same prefix 'json_' and clear action verbs. This predictable naming makes it easy for agents to understand and select the appropriate tool.

    Tool Count2/5

    With only two tools, the server feels too thin for a JSON processing domain, lacking essential operations like json_write, json_validate, or json_transform. While the tools cover reading and extraction, the scope is incomplete for typical JSON workflows, making it borderline inadequate.

    Completeness2/5

    The tool surface has significant gaps: it supports reading and extraction but lacks write, update, validation, or advanced transformation capabilities. This incomplete coverage will likely cause agent failures when tasks require modifying or validating JSON data, limiting practical utility.

  • Average 4.2/5 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

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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

  • Behavior3/5

    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 describes what the tool does (extract data using various methods) and mentions use cases (data transformation, focused analysis), but doesn't address important behavioral aspects like error handling, performance characteristics, memory usage with large files, or what happens when multiple extraction methods are combined. It provides basic operational context but lacks depth on behavioral traits.

    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 with three sentences that each serve distinct purposes: stating the core functionality, providing usage guidelines, and describing ideal use cases. It's front-loaded with the main purpose and avoids redundancy. While efficient, it could be slightly more structured with clearer separation between mandatory and optional parameter usage.

    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 tool with 9 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the 'what' and 'when' well but lacks details on 'how' the extraction works, error conditions, return formats, or performance considerations. The description compensates somewhat for the lack of annotations by specifying use cases, but doesn't fully address the complexity of a multi-parameter extraction tool.

    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 9 parameters thoroughly. The description mentions the extraction methods (paths, filters, patterns, slices) which correspond to parameters, but doesn't add meaningful semantic context beyond what's in the schema descriptions. It doesn't explain how parameters interact or provide usage examples. 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.

    Purpose5/5

    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 extracting specific data from JSON files using multiple methods (paths, filters, patterns, slices). It distinguishes from the sibling 'json_read' by emphasizing targeted extraction rather than general reading. The verb 'extract' with the resource 'JSON files' is specific and actionable.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit guidance on when to use this tool: 'Always use this tool when you need to retrieve particular values, filter arrays/objects by conditions, search for patterns, or slice data.' It also distinguishes from the sibling 'json_read' by specifying this is for 'targeted data extraction' rather than general reading. The 'Ideal for' section further clarifies appropriate contexts.

    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 provided, the description carries the full burden of behavioral disclosure. It effectively communicates that this is a read/analysis tool (not destructive), provides context about its exploratory nature, and hints at capabilities like handling large JSON files and providing overviews. However, it doesn't mention potential limitations like file size constraints or performance characteristics.

    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 perfectly structured with two sentences that each earn their place. The first sentence establishes the core purpose, while the second provides specific usage guidelines. There's zero wasted language and it's appropriately sized for the tool's complexity.

    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 moderate complexity (8 parameters, no output schema, no annotations), the description provides excellent guidance on when and why to use it. However, without annotations or output schema, it could benefit from more explicit information about what the tool returns (e.g., formatted analysis vs. raw data) and any behavioral constraints.

    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 schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for high schema coverage.

    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's purpose with specific verbs ('read and analyze JSON') and resources ('JSON structure', 'data schema', 'large JSON'). It distinguishes from the sibling tool json_extract by emphasizing exploration and understanding rather than extraction.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly states when to use this tool ('Always use this tool to explore JSON structure', 'for initial data exploration', 'when you need to understand the shape and types of data before extracting specific values') and implies when not to use it (when you need to extract specific values, suggesting json_extract as an alternative).

    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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