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CaptainCrouton89

MCP Server Boilerplate

by CaptainCrouton89
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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 with no overlap. json_extract is for targeted data extraction and transformation, while json_read is for initial exploration and schema understanding. The descriptions explicitly guide when to use each tool, eliminating any ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'json_' prefix and descriptive actions (extract, read). The naming is perfectly uniform and predictable across the tool set.

    Tool Count2/5

    With only 2 tools, the server feels too thin for a general-purpose JSON handling domain. While the tools are well-defined, a complete JSON manipulation surface would typically include additional operations like validation, transformation, or writing capabilities. The count is insufficient for comprehensive JSON workflows.

    Completeness2/5

    There are significant gaps in the JSON manipulation surface. The server only provides read/extract capabilities with no tools for creating, updating, validating, or writing JSON data. This creates dead ends for agents needing to modify or generate JSON, making it incomplete for common JSON-related tasks.

  • Average 4.1/5 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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  • 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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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
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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 the full burden of behavioral disclosure. It describes the tool's capabilities (extraction methods) but lacks details on error handling, performance characteristics, or output format. The phrase 'Always use this tool when...' is somewhat prescriptive but doesn't contradict any annotations.

    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 well-structured and front-loaded with the core purpose. Both sentences earn their place by explaining capabilities and usage guidance. It could be slightly more concise by combining some concepts, but overall it's efficient and clear.

    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 context about what the tool does and when to use it. However, it lacks details about return values, error conditions, or behavioral constraints that would be important for a complex extraction tool. The high schema coverage helps compensate somewhat.

    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 9 parameters thoroughly. The description adds value by explaining the overall extraction approach and use cases, but doesn't provide additional parameter-specific semantics beyond what's in the schema. This meets the baseline 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 ('extract', 'retrieve', 'filter', 'search', 'slice') and resources ('JSON files', 'JSON elements'). It distinguishes from the sibling tool 'json_read' by emphasizing targeted extraction rather than general reading.

    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 ('when you need to retrieve particular values, filter arrays/objects by conditions, search for patterns, or slice data') and ideal use cases ('targeted data extraction, data transformation, focused analysis'). It implicitly suggests alternatives by highlighting its specialized nature versus the likely more general 'json_read'.

    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 provided, the description carries the full burden of behavioral disclosure. It describes the tool's exploratory nature and high-level analysis purpose, but doesn't mention performance characteristics, error handling, memory usage with large files, or output format details. It adds some context about the tool's role but lacks comprehensive 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.

    Conciseness5/5

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

    The description is perfectly concise with two well-structured sentences. The first sentence states the core purpose, and the second provides clear usage guidelines. Every word earns its place, and the information is front-loaded with no redundancy or unnecessary elaboration.

    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 read-only JSON analysis tool with no annotations and no output schema, the description provides good context about when and why to use it. However, it doesn't describe what the output looks like (structure, format, or content), which is a gap given the lack of output schema. The purpose and usage guidance are complete, but output expectations are missing.

    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 8 parameters thoroughly. The description doesn't add any specific parameter semantics beyond what's in the schema - it mentions general concepts like 'explore JSON structure' and 'understand data schema' but doesn't explain how individual parameters contribute to these goals. 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 with specific verbs ('read and analyze JSON') and distinguishes it from its sibling by specifying it's for exploration and understanding structure rather than extraction. It explicitly mentions 'use this for initial data exploration' and 'before extracting specific values', which differentiates it from json_extract.

    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 ('for initial data exploration', 'to understand shape and types of data before extracting specific values') and when to use alternatives ('use this... before extracting specific values', implying json_extract is for extraction). It clearly defines the tool's role in the workflow.

    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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