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xiaobenyang-com

temp-notes

Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: append_note adds to the note, clear_note empties it, read_note retrieves content, and write_note replaces it. There is no overlap or ambiguity between these operations.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case (e.g., append_note, clear_note). The naming is predictable and uniform throughout the set.

    Tool Count5/5

    With 4 tools, this server is well-scoped for managing a single note. Each tool serves a distinct and necessary function, making the count appropriate for the domain.

    Completeness5/5

    The tool set provides complete CRUD-like coverage for a note-taking domain: create/replace (write_note), read (read_note), update (append_note), and delete (clear_note). There are no obvious gaps for the intended purpose.

  • Average 3.2/5 across 4 of 4 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.

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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?

    No annotations are provided, so the description carries the full burden. It states the tool 'replaces' content, implying a destructive mutation, but doesn't disclose behavioral traits such as whether it creates a note if none exists, what happens to previous content, or any 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 directly states the tool's function without unnecessary words. It is front-loaded and appropriately sized for a simple tool, earning its place with zero waste.

    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 (a mutation with no annotations, 1 parameter at 0% schema coverage, and no output schema), the description is incomplete. It fails to address key aspects like what 'current note' refers to, whether the operation is idempotent, or what the return value might be, leaving significant gaps for agent understanding.

    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 0%, so the schema only indicates a required 'content' parameter of type string. The description adds minimal semantics by implying 'content' is the 'new string' that replaces the current note, but it doesn't elaborate on format, constraints, or examples. With one parameter, the baseline is 4, but the description provides only basic value 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 clearly states the action ('replaces') and the resource ('the current note'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'append_note' (which adds content) or 'clear_note' (which removes content), though the verb 'replaces' implies overwriting rather than appending.

    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 'append_note' or 'clear_note'. It lacks context about prerequisites (e.g., whether a note must exist) or exclusions, leaving the agent to infer usage from the verb 'replaces' 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns content, implying a read-only operation, but doesn't specify if it requires authentication, has rate limits, or what happens if no note exists. This is a significant gap for a 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 directly states the tool's function without any 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?

    Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'current content' entails (e.g., format, structure) or handle edge cases like missing notes. For a read operation with no structured support, more context is needed to be fully helpful.

    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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the input requirements. The description doesn't need to add parameter details, and it appropriately doesn't mention any, earning a baseline score of 4 for this context.

    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 ('Returns') and resource ('the current content of the note'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'append_note' or 'write_note' beyond the verb 'read' in the name, which is why it doesn't achieve 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?

    The description provides no guidance on when to use this tool versus alternatives like 'append_note' or 'write_note'. It doesn't mention prerequisites, context, 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.

  • 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 tool 'clears the current note, making it empty,' which implies a destructive mutation (erasing content). However, it doesn't disclose whether this action is reversible, requires specific permissions, affects other data, or has side effects like confirmation prompts. For a mutation tool with zero annotation coverage, 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 a single, clear sentence with zero waste: 'Clears the current note, making it empty.' It is front-loaded with the core action and outcome, appropriately sized for a simple tool, and every word earns its place by specifying the verb, resource, and result.

    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 low complexity (0 parameters, no output schema, no annotations), the description is minimally complete. It states what the tool does but lacks context on behavioral traits (e.g., irreversibility, permissions) and usage guidelines versus siblings. Without annotations or output schema, the description should compensate more for the mutation nature, but it only covers the basic purpose, leaving gaps in completeness.

    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 tool has 0 parameters, and schema description coverage is 100% (as there are no parameters to describe). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters. It efficiently avoids unnecessary details about inputs.

    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 ('clears') and resource ('the current note'), specifying the outcome ('making it empty'). It distinguishes from siblings like 'append_note' (adds content) and 'write_note' (sets content), though not explicitly named. However, it doesn't fully differentiate from 'read_note' (which is read-only) or explain what 'current note' means, keeping it from 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?

    The description provides no guidance on when to use this tool versus alternatives like 'write_note' (which could also set the note to empty) or 'append_note'. It lacks context on prerequisites, such as whether a note must exist to clear it, or exclusions for when not to use it. Usage is implied only by the action itself, with no explicit alternatives or conditions.

    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 describes the basic behavior of appending text with a new line and optional separator, but lacks details on permissions, error handling, or what happens if no note exists. For a mutation tool with zero annotation coverage, this is a significant gap in behavioral disclosure.

    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, well-structured sentence that efficiently conveys the core functionality and optional feature. It is front-loaded with the main action and wastes no words, making it highly concise and effective.

    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 (mutation with 2 parameters), no annotations, and no output schema, the description provides basic operational context but lacks completeness. It covers what the tool does and parameters but misses details on prerequisites, side effects, and return values, which are important for a mutation tool.

    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 schema description coverage is 0%, so the description must compensate. It explains that 'content' is the new text to append and 'include_separator' is an optional boolean for adding a separator line, adding meaningful semantics beyond the bare schema. However, it does not specify the format of the separator or any constraints on content, leaving minor gaps.

    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 verb 'appends' and the resource 'current note', specifying that it adds new text starting with a new line. It distinguishes from siblings like 'write_note' (which likely overwrites) and 'clear_note' (which clears content), making the purpose specific and differentiated.

    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 implies usage by mentioning 'current note' and the optional separator, suggesting it's for incremental additions rather than full replacements. However, it does not explicitly state when to use this tool versus alternatives like 'write_note' or 'read_note', leaving the guidance implicit rather than explicit.

    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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