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IamOumarIbrahim

SpectraMCP

by IamOumarIbrahim
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Python
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Server Quality Checklist

58%
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  • Latest release: v2.0.0

  • Disambiguation5/5

    Each tool targets a distinct aspect of the audit workflow: check_action evaluates an intended capability, recent_decisions lists past decisions, and verify_audit_chain checks log integrity. No two tools overlap in purpose, so an agent can easily select the right one.

    Naming Consistency4/5

    Two tools follow the verb_noun pattern (check_action, verify_audit_chain), but recent_decisions uses an adjective_noun pattern, which is a minor deviation. The names are still descriptive and predictable.

    Tool Count5/5

    With only 3 tools, the set is well-scoped for a focused audit and policy-decision utility. Each tool serves a clear and necessary function, and the count is within the ideal 3-15 range.

    Completeness4/5

    The core audit lifecycle is covered: evaluate an action, list decisions, and verify integrity. Minor gaps exist, such as no way to fetch a single decision by ID or perform detailed historical queries, but these are workable limitations.

  • Average 2.8/5 across 3 of 3 tools scored. Lowest: 1.6/5.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 12 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.

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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

  • Behavior1/5

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

    With no annotations, the description must disclose behavioral traits, but it only states that the tool 'evaluates and audits.' It does not mention whether it is read-only, what side effects it may have, what inputs are expected, or what the output schema contains. This is a minimal, non-informative description that fails to convey behavior.

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

    Conciseness2/5

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

    The description is a single short sentence, which is concise in length but under-specified. It does not provide useful detail, so it is not effectively structured to aid an agent. The succinctness is not a virtue here because it omits essential information.

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

    Completeness1/5

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

    Despite having an output schema, the description does not explain what the tool returns or how the output should be interpreted. With only 2 parameters, no parameter documentation, and no annotations, the description is far from complete for safe and correct use.

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

    Parameters1/5

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

    The input schema has 0% description coverage, and the description does not mention the 'capability' parameter (required) or 'context' parameter at all. The agent receives no explanation of what values to supply for these parameters or what they mean, making correct invocation difficult.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses the verbs 'evaluate and audit' with the object 'intended tool capability', which gives a general sense of verifying a capability. However, the phrase is vague and does not clearly define what 'capability' means or how it is audited, making it difficult to distinguish from sibling tools beyond the name.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines1/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided about when to use this tool versus alternatives like recent_decisions or verify_audit_chain. The description offers no context, prerequisites, or exclusions, leaving the agent without direction on appropriate scenarios.

    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 of behavioral disclosure, but it only mentions result ordering. It does not explicitly state that the operation is read-only, nor does it describe permissions, side effects, or rate limits.

    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, front-loaded sentence with no unnecessary words. It clearly conveys the core purpose and an important ordering detail.

    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?

    The description is sparse and omits key usage details, particularly the 'limit' parameter's effect. Although an output schema exists, the lack of parameter explanation and behavioral context leaves the description incomplete for an agent.

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

    Parameters1/5

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

    The schema has one parameter, 'limit', with no description, and the tool description does not mention it. Since schema coverage is 0%, the description fails to compensate by explaining how to control the number of results.

    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 returns recent local policy decisions with newest first ordering. The verb 'return' and resource 'local policy decisions' are specific, and the ordering distinguishes it from sibling tools like check_action and verify_audit_chain.

    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 alternatives. It does not mention any exclusions, prerequisites, or relationships to sibling tools.

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

  • Behavior3/5

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

    No annotations are present, so the description carries the full burden. It discloses the action ('check') and what it detects, but does not explicitly state whether it is read-only, what it returns, or any side effects. The verb 'check' implies non-mutating behavior, but this is implicit.

    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, front-loaded sentence with no redundant wording. Every word contributes to understanding the tool's purpose.

    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 no parameters and an output schema, the description sufficiently conveys the core purpose. It lacks details about detection methodology, but that is likely covered by the output schema.

    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 tool has zero parameters and the schema is complete, so the baseline 4 applies. There is no need for the description to explain 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 clearly states the tool checks the local audit log for modified or reordered rows, providing a specific verb and resource. It distinguishes itself from sibling tools like check_action and recent_decisions by focusing on audit log integrity.

    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 is provided about when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or alternative tool recommendations.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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