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MagneticDogSon

FixFlow

by MagneticDogSon
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: resolve_kb_id searches the KB, read_kb_doc retrieves full content of a specific card, save_kb_card writes new cards or reports outcomes. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: resolve_kb_id, read_kb_doc, save_kb_card. The naming is predictable and clear.

    Tool Count4/5

    With only 3 tools, the server is minimal but covers the essential knowledge base operations: search, read, and write/report. While a few more tools (e.g., delete) could be considered, the count is appropriate for the focused scope.

    Completeness4/5

    The tool set forms a complete workflow for troubleshooting: search for a solution, read it, apply it, and report success/failure or save a new card. Missing operations like explicit deletion are minor gaps but do not hinder the primary use case.

  • Average 4.8/5 across 3 of 3 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • 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

  • Behavior4/5

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

    With no annotations, the description carries full burden. It discloses that the tool returns reliability metrics, content, and related cards, and what the agent must do after. It does not mention side effects or auth, but as a read operation this is sufficient.

    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 well-structured with sections for purpose, when-to-use, input, output, and actions. It is front-loaded with the workflow context and every sentence adds value without redundancy.

    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?

    Given the simple input (one parameter), no annotations, and existence of an output schema, the description provides complete context: workflow placement, input format, output nature, and required post-action. No gaps remain.

    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 input schema has no description for kb_id (0% coverage). The description compensates by specifying 'The exact ID of the card (e.g., 'CROSS_DOCKER_001')', providing a format example that adds meaning.

    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 it's the second step in the troubleshooting workflow and that it reads the content and solution of a specific Knowledge Base card. The verb 'read' and resource 'knowledge base card' are specific, and it distinguishes from siblings by referencing the workflow sequence.

    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?

    Explicitly states to call this ONLY after obtaining a valid kb_id from resolve_kb_id tool. It also outlines the required follow-up steps (apply solution, call save_kb_card), providing clear usage guidance within the workflow.

    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?

    No annotations provided, so description carries full burden. It describes search behavior, output structure, and that results are ranked, but does not mention any side effects or rate limits, which are expected for a search tool.

    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?

    Well-structured with clear sections (WHEN TO USE, INPUT, OUTPUT). Every sentence provides value; no superfluous text.

    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?

    Given the tool's complexity and lack of annotations, the description covers purpose, usage, parameters, output, and post-call actions. It also references sibling tools appropriately.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Adds significant meaning beyond the schema: explains that 'query' can be an error message or concept, and 'category' is an optional filter. With 0% schema coverage, the description fully compensates.

    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?

    Clearly states it is the first step in troubleshooting, performs full-text search across the Knowledge Base, and distinguishes itself from siblings like read_kb_doc and save_kb_card.

    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?

    Explicitly states 'ALWAYS call this first' for error messages, bugs, or design patterns, and instructs to follow up with read_kb_doc if a match is found.

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

  • Behavior5/5

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

    No annotations exist, so description fully covers behavior: it's a WRITE operation with two modes, overwrite ability, and detailed rules for actionable cards (depersonalization, verification, fallback). Discloses all relevant traits for safe invocation.

    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 very long but well-structured with sections, headings, and a template. Front-loaded with purpose and modes. Some redundancy (e.g., lengthy template) could be trimmed, but it's organized for easy scanning.

    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?

    The description is extremely thorough, covering two modes, all parameters, usage rules, a full template, and output expectation via schema. It compensates for missing annotations and low schema coverage, making the tool fully understandable.

    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 0%, but the description explains each parameter's role in context of the two modes (content for Mode 1 with template, overwrite for updates, kb_id and outcome for Mode 2, optional enrichment). However, parameter details are embedded in prose rather than listed, slightly reducing clarity.

    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: 'WRITE to the Knowledge Base' and distinguishes two modes (save new card, report outcome). It contrasts with sibling tools (resolve_kb_id, read_kb_doc) by specifying when to use each mode, thus avoiding confusion.

    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?

    Explicit when-to-use for each mode: Mode 1 after fixing a bug if no existing card covers it; Mode 2 always after applying a solution from read_kb_doc. Also implies when not to use (e.g., only save if no existing card). This provides clear guidance for the agent.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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