Server Quality Checklist

Tool Scores

• improve_writingB
  3.4/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  Annotations are absent, so description must disclose behavior. It mentions 'rule-based style checks' and 'No API calls needed', giving insight into how it operates. However, it does not address potential limitations or side effects.

  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 very concise, consisting of a few short sentences that each add unique value. No redundancy or wasted words.

  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 simple tool with two parameters and no output schema, the description provides a good overview of what it does and what it returns. However, it lacks details on the exact structure of the suggestions, which would improve completeness.

  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?

  Both parameters are fully described in the input schema (100% coverage). The description adds no new semantic information about the parameters beyond what the schema provides.

  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 analyzes text for writing quality using rule-based checks and returns suggestions. It distinguishes from siblings implicitly (adjust_tone, check_grammar, translate) but does not explicitly differentiate.

  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 on when to use this tool vs alternatives. The description does not mention when not to use it or provide context for selection.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• adjust_toneA
  3.6/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations, the description must fully convey behavior. It discloses the key behavioral trait that the host AI performs the rewrite, not the tool itself. However, it does not disclose other aspects like idempotency, side effects, or required permissions.

  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 three sentences, with the first two providing core functionality. The third sentence ('Powered by BeLikeNative') is extraneous but not harmful. It is front-loaded and relatively concise.

  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 no output schema and 2 parameters, the description explains the output nature (guidelines + prompt) but does not detail structure or provide examples. It is adequate but leaves gaps for an agent to use it effectively.

  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 coverage is 100%, so baseline is 3. The description does not add additional meaning beyond the schema's parameter descriptions (e.g., text max length, tone enum values). No further elaboration on usage or format.

  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 explicitly states that the tool returns structured tone adjustment guidelines and a prompt, and clarifies that the actual rewrite is performed by the host AI. This clearly distinguishes it from sibling tools like check_grammar (grammar) and translate (language).

  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 use for tone adjustment but does not provide explicit guidance on when to use this tool over siblings like improve_writing or check_grammar. No prerequisites or exclusions are mentioned.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• translateA
  3.6/5.0
  Behavior3/5

  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 discloses that the tool does not perform translation itself but returns a prompt for the host AI, which is a key behavioral trait. However, it does not describe any side effects, authentication requirements, rate limits, or error conditions, leaving gaps 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 consists of two efficient sentences. The first sentence immediately states the core function, and the second provides context about the MCP server's role. No redundant words or unnecessary details are present.

  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 tool with three required parameters and no output schema or annotations, the description is mostly complete. It explains the output (structured translation prompt) and the division of labor with the host AI. However, it could benefit from mentioning the prompt format or an example, especially given the lack of output schema.

  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 provides meaning for all three parameters (text, source_language, target_language). The description adds no additional parameter-specific information beyond what the schema states, resulting in a baseline score of 3.

  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 returns a structured translation prompt and that the MCP server provides formatting/context while the host AI performs the actual translation. It differentiates from sibling tools (adjust_tone, check_grammar, improve_writing) which address different tasks. However, it does not use a single verb+resource phrase, slightly reducing clarity.

  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 the tool should be used when translation is needed, but the host AI handles the actual translation. It does not explicitly state when to use versus alternatives or provide case exclusions. Usage context is implied rather than explicitly guided.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• check_grammarA
  4/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations provided, so description carries full burden. It discloses it is rule-based and local, and provides L1-aware explanations, but lacks details on limitations (e.g., language support beyond default) or return format specifics.

  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?

  Three sentences, each adding value: what it does, what it returns, and key differentiators (no API, powered by BeLikeNative). No unnecessary words.

  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 simple two-parameter tool with no output schema, the description is nearly complete. It explains the return structure (structured JSON with errors, corrections, explanations) and the purpose of each parameter.

  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?

  Schema coverage is 100%, and description adds value by explaining the 'language' parameter is used for 'L1-aware explanations' and 'text' is the content to check. This goes beyond the schema description.

  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 checks grammar, spelling, and punctuation using local rule-based analysis, and returns structured JSON with errors and corrections. It is distinct from sibling tools like adjust_tone and improve_writing.

  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?

  No explicit guidance on when to use this tool vs alternatives. The description mentions 'No API calls needed' which implies offline use, but does not provide when-to-use or when-not-to-use scenarios.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.