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rudimentall1

agent-guardrail

Server Quality Checklist

83%
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  • Latest release: v1.0.3

  • Disambiguation5/5

    Each tool covers a distinct phase of the guardrail lifecycle: pre-action evaluation, post-action outcome recording, and historical audit retrieval. There is no functional overlap between check, record_outcome, and agent_history, making tool selection unambiguous.

    Naming Consistency4/5

    All tools share a consistent 'guardrail_' prefix, with two using a verb_noun pattern (guardrail_check, guardrail_record_outcome). The third, guardrail_agent_history, uses a noun phrase instead of a verb, which is a minor deviation but still predictable and readable.

    Tool Count5/5

    Three tools is well-scoped for a guardrail server, providing the core operations of checking, recording, and viewing history without unnecessary bloat. This is within the ideal range and earns its place.

    Completeness5/5

    The tool set covers the entire guardrail workflow: evaluate before action, record the outcome afterward, and retrieve an audit trail. There are no obvious dead ends or missing critical operations for this focused domain.

  • Average 3.7/5 across 3 of 3 tools scored. Lowest: 3.1/5.

    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
    • 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • This server has been verified by its author.

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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?

    With no annotations, the description carries the full burden of behavioral disclosure. It only adds 'a real, persisted audit trail,' but does not explain ordering, pagination (beyond schema's default limit), authentication requirements, or the structure of the returned history. This is minimal 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 concise sentence with no fluff. It front-loads the action and resource, and the dash-separated clarification adds value without excess length.

    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 has no output schema and no annotations, the description is too sparse. It does not describe the return format, what constitutes a decision, or any limitations/pagination behavior, making it incomplete for an agent to invoke correctly.

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

    Parameters2/5

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

    Schema description coverage is 0%, so the description must compensate. It only implies agent_id via 'given an agent' and never explains the 'limit' parameter or its default. No additional meaning is provided 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 clearly states 'Return recent decision history for a given agent,' providing a specific verb and resource. The phrase 'a real, persisted audit trail' differentiates it from potentially transient data, and the sibling tools (guardrail_check, guardrail_record_outcome) suggest this is a historical lookup, distinct from check/record actions.

    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 the sibling tools. It does not specify scenarios, exclusions, or alternative tools, leaving the agent to infer usage solely from the tool name.

    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. It identifies the action as recording for an audit trail but does not disclose whether it overwrites existing records, whether it requires a prior guardrail_check, what happens if request_id is invalid, or any error behavior. This is insufficient for a write operation.

    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 front-loads the action and purpose. Every word contributes meaning without redundancy.

    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 simple two-parameter schema and no output schema, the description is minimally adequate but could be more complete. It explains the audit trail purpose but omits important context such as whether the outcome is appended, overwritten, or how errors are handled. For a straightforward logging tool, this is acceptable but not comprehensive.

    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 does clarify that 'outcome' is success/error, matching the enum, and 'previously-checked action' implies request_id is an ID from a prior check. However, it does not explicitly define request_id's format or source, leaving some ambiguity.

    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 'Record' and the specific resource 'real-world outcome (success/error) of a previously-checked action, for the audit trail.' It distinguishes itself from siblings by focusing on post-check outcome logging rather than checking or history retrieval.

    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 phrase 'previously-checked action' provides clear context that this tool is for logging outcomes after a guardrail check has occurred, implying a temporal relationship with guardrail_check. It does not explicitly name alternatives or state when not to use it, but the context is clear.

    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, the description carries the transparency burden; it discloses that the tool returns ALLOW, WARN, or BLOCK with a concrete explanation and implies it does not execute the proposed action. It could add explicit no-side-effect or error behavior, but for a guardrail check the core behavior is clearly conveyed.

    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 two sentences: the first front-loads the purpose and output, the second gives a concrete usage rule. No words are wasted, and the structure makes the call conventions immediately findable.

    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 covers the essential aspects an agent needs: what it evaluates, when to call it, and the expected return values. Since there is no output schema, the explicit mention of ALLOW/WARN/BLOCK with explanations fills that gap well, making the description sufficient for this moderately complex tool.

    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?

    The input schema already describes all three parameters, and schema coverage is 100%, so the description adds little beyond labeling the call as 'proposed'. This matches the baseline of 3 for fully schema-documented parameters.

    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 pair: 'Evaluate a proposed tool call against the Guardrail policy', which clearly identifies the action and subject. It also distinguishes the tool from siblings by emphasizing 'BEFORE executing it' and enumerating the return values (ALLOW, WARN, or BLOCK), making its role distinct from guardrail_record_outcome and guardrail_agent_history.

    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 says 'Always call this before performing any action that spends money, deletes data, sends messages externally, or runs code', which is strong when-to-use guidance. It does not name sibling alternatives or state when not to use it, but the 'before' framing implicitly separates it from outcome recording.

    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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