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tonny-lec

codex-advisor

by tonny-lec
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Python
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.0

  • Disambiguation5/5

    The two tools are completely distinct: one performs a consultation, the other manages configuration. No overlap or ambiguity exists between them.

    Naming Consistency3/5

    Tool names use different patterns: 'consult_advisor' follows verb_noun, while 'advisor_config' is noun_noun. The inconsistency is noticeable but both names are clear and self-explanatory.

    Tool Count3/5

    With only 2 tools, the set feels thin but is appropriate for the narrow scope of advisor consultation and configuration. It covers the essential actions without unnecessary bloat.

    Completeness4/5

    The domain of consulting and configuring an advisor is well-covered. A minor gap is the lack of consultation history or logging, but for the stated purpose (strategic guidance and settings) the surface is essentially complete.

  • 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
    • 24 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

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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, the description carries the burden of disclosing behavior. It adds a key behavioral detail: 'The current session transcript is attached automatically,' which clarifies input handling. However, it does not disclose the output format, potential side effects, or any restrictions, leaving the safety profile somewhat opaque.

    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 concise and front-loaded, with the core purpose in the first sentence. The subsequent usage guidance adds valuable, non-redundant detail without wasting 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?

    The tool is simple, has an output schema to cover return values, and the description provides purpose, usage triggers, and a key behavioral trait. It is slightly incomplete due to the undocumented 'context_hint' parameter, but overall it is sufficiently complete for effective use.

    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 0%, so the description must compensate. It explains the 'question' parameter well ('put the specific decision to evaluate in question'), but the optional 'context_hint' parameter is not mentioned at all, leaving a gap for the second parameter.

    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: 'Consult the configured advisor model for strategic guidance.' It uses a specific verb and resource, and distinguishes itself from the sibling 'advisor_config' by focusing on querying the advisor rather than configuring it.

    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 explicitly lists when to call the tool: at decision points, after repeated errors, or before declaring a complex task complete. It provides clear context but does not mention when not to use it or name alternative tools, so it stops short of a full 5.

    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?

    With no annotations provided, the description carries the full burden. It clearly discloses the three supported actions, the exact model format with examples, and the notable fact that any model is accepted with no allowlist. It does not detail persistence or side effects of 'set'/'off', but the core behavior is transparent and useful.

    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, but the sentence 'Use when the user asks' is vague filler that doesn't earn its place. The inline examples, while long, are valuable for establishing the model format. Overall it remains appropriately concise.

    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?

    Given the tool's complexity (multiple actions, a model parameter with format requirements) and the presence of an output schema, the description covers the essential usage points well. It could be more complete by explicitly referencing the sibling tool's role, but nothing critical is missing for correct invocation.

    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?

    Schema description coverage is 0%, yet the description fully compensates. It explains the valid values for 'action', the required model format for 'set', and that the model parameter is needed only in that case, plus examples of acceptable formats. This adds substantial meaning beyond the raw schema.

    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 a specific verb+resource: 'Get or change advisor settings,' which clearly identifies the tool's domain. It further details distinct actions ('get', 'set', 'off') and confirms this is about configuration, which distinguishes it from the sibling 'consult_advisor' without being explicit.

    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 phrase 'Use when the user asks' is generic and provides no concrete decision guidance. The action descriptions imply usage (get vs. set vs. off), but the tool never explicitly contrasts itself with the sibling 'consult_advisor' or states when not to use it, leaving the comparison to inference.

    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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  • Evaluate tool definition quality.

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