Server Quality Checklist

Tool Scores

• get_recent_errorsA
  4.5/5.0
  Behavior5/5

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

  Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds substantial context beyond that: return formats (markdown/json), token economics, error handling ('returns error if server_name not found'), and a full JSON response schema. This fully discloses behavior.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness3/5

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

  The description is well-structured with clear sections (trigger conditions, why use, token economics, workflow, args, returns, examples, error handling), but it is excessively long. The persuasion and token-economics sections, while informative, could be condensed. Not every sentence earns its place.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Despite having no output schema, the description itself provides a full JSON response schema, detailed parameter explanations, error handling behavior, and multiple examples. It is exceptionally complete for selecting and invoking the tool correctly.

  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 description coverage is 100%, so the schema already documents each parameter. The description adds value through concrete examples ('Slack tool failed -> IMMEDIATELY: { server_name: "slack-mcp-server", limit: 5 }') and contextual usage ('Repeated failures -> { server_name, tool_name }'), elevating the semantics beyond simple field descriptions.

  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 retrieves recent MCP server errors, with a specific verb ('Get'), resource ('Recent MCP Server Errors'), and scope ('from a specific server'). It distinguishes itself from siblings by focusing on error logs rather than details or server lists.

  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 extremely explicit trigger conditions and workflow instructions ('CALL THIS IMMEDIATELY when ANY MCP tool fails with vague/unclear error messages', 'BEFORE attempting to retry any failed operation'). However, it does not explicitly contrast with sibling tools (get_error_details, get_server_list) or state when NOT to use this tool, so it falls short of the highest bar.

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

• get_server_listA
  4.6/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint and idempotentHint, so the safety profile is known. The description adds behavioral context beyond annotations: returns an empty list when no logs exist, gracefully handles varied log formats, and defines the exact output structure for both markdown and JSON responses.

  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 long but well-organized with sections (WHEN TO USE, WHAT THIS TOOL SHOWS, WORKFLOW, USE CASES, Returns, Examples, Error Handling). The primary purpose is front-loaded in the first line. Minor redundancy exists (the 'use first' guidance appears twice), but overall structure makes the length acceptable.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  With no output schema, the description thoroughly explains return values, including a JSON schema for both markdown and json formats, examples, and error handling (empty list, malformed log files). Combined with the annotations and parameter descriptions, an agent has all necessary information to select and invoke this tool correctly.

  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 covers 100% of parameters with descriptions, giving a baseline of 3. The description goes further by explaining how 'include_stats' conditionally includes fields (total_errors, recent_error_count) and providing examples of parameter usage, adding practical meaning beyond the schema text.

  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 opens with 'List Available MCP Servers', a specific verb+resource phrase, and expands on exactly what is listed (server names, error counts, recent errors, log paths). It clearly distinguishes itself from siblings by positioning this as the first step before invoking get_recent_errors or get_error_details.

  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 'WHEN TO USE' section provides concrete scenarios (e.g., 'tool failed but you don't know the exact server_name') and an explicit workflow that names the sibling tool get_recent_errors as the next step. This offers clear guidance on when this tool should be used over alternatives.

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

• get_error_detailsA
  4.8/5.0
  Behavior5/5

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

  Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is known. The description adds rich behavioral context: it explains the tool performs root-cause analysis, provides pattern warnings, uses 'best-effort analysis even for unknown error patterns', and returns specific error messages like "Error not found". This goes far beyond the 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 with bolded section headers, bullet lists, and an example workflow, making it skimmable. It is front-loaded with the core purpose and usage. However, it is quite lengthy and somewhat repetitive: the 'WHEN TO USE' list and 'WORKFLOW' section overlap, and the 'WHAT THIS TOOL DOES' section duplicates content from the opening. Still, the structure prevents it from feeling disorganized.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  With no output schema, the description must fully document return values, and it does: it spells out both Markdown and JSON response shapes, including a JSON schema example. It also covers error handling, built-in pattern recognition, and examples for different use cases. This is a model of completeness for a complex analysis 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?

  Schema description coverage is 100%, so the baseline is 3. The description repeats parameter names and meanings in the 'Args' section, adding no new detail there, but it enriches understanding with example use cases (e.g., `{ server_name: "slack-mcp-server", error_message: "permission denied" }`) and explains the impact of `response_format` on return structure. This extra context justifies a 4.

  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 opens with 'get detailed error analysis' and expands with specific capabilities: 'Identifies ROOT CAUSE (not just symptoms)', 'Provides ACTIONABLE steps to fix the issue', and 'Finds RELATED errors to spot patterns'. It distinguishes itself from sibling get_recent_errors by stating 'Use this AFTER getting recent errors', making the purpose unmistakably distinct.

  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?

  An explicit 'WHEN TO USE' section lists four clear scenarios, including 'You got error logs but need to understand WHY it happened' and 'The same error occurred multiple times (pattern analysis)'. The workflow step 'Get recent errors using get_recent_errors' directly names the sibling tool as the preceding step, providing clear context and an implied alternative. No misleading exclusions.

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