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EmmanuelFortressAI

consciousness-mcp-tools

by EmmanuelFortressAI
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JavaScript

Server Quality Checklist

42%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.1

  • Disambiguation3/5

    The two UEF tools ('uef_analyze_decision' and 'uef_assess_alignment') have overlapping purposes—one analyzes decisions and the other assesses alignment, both against UEF principles. An agent might struggle to choose correctly without deeper context. The third tool is more distinct.

    Naming Consistency2/5

    Naming is inconsistent: two tools use the 'uef_*' prefix with verb_noun structure, while the third uses 'consciousness_evolution_metrics' with a different prefix and no verb. This breaks the pattern.

    Tool Count2/5

    With only three tools, the server feels underdeveloped for the broad domain of consciousness and ethics. A more comprehensive toolkit would require additional tools for different operations or data handling.

    Completeness2/5

    The tool set covers only a narrow slice of ethical analysis and consciousness metrics. Missing are tools for raw data input, comparison, visualization, or step-by-step reasoning, leaving significant gaps for an agent working in this domain.

  • Average 2.7/5 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • 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?

    No annotations are provided, and the description does not disclose any behavioral traits such as side effects, output format, required permissions, or constraints. The agent cannot infer what happens when this tool is invoked.

    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 a single sentence, which is concise but underinformative. It could be restructured to provide more value without adding much length.

    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?

    Given 4 parameters, no output schema, and no annotations, the description is far too sparse. It lacks essential information such as expected output, example usage, or required formatting. The agent is left with significant ambiguity.

    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 100%, meaning each parameter already has a description. The tool description adds no additional parameter information. Baseline of 3 is appropriate as the schema handles semantics adequately.

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

    Purpose4/5

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

    The description clearly states the tool analyzes decisions against a known framework (Universal Ethical Framework). It uses a specific verb-resource pair ('Analyze decisions') that distinguishes it from the sibling 'assess_alignment', though it doesn't elaborate on what analysis entails.

    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 on when to use this tool versus the siblings. The description does not provide context, prerequisites, or exclusions. The agent must infer when analysis is appropriate.

    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 full responsibility for behavioral transparency. It fails to disclose any behavioral traits: no mention of output format, side effects, idempotency, permissions, or limitations. The agent has no insight into what the tool actually does beyond 'assess'.

    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 a single sentence, making it concise. However, it is under-specified and lacks structure; it does not provide enough information to stand on its own. It earns its place but barely.

    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 three parameters and no output schema, the description is incomplete. It does not explain what the assessment returns (e.g., a score, report), how results are presented, or how to interpret the output. This is insufficient for an agent to use the tool correctly.

    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 description coverage is 100%, so each parameter (system_name, description, capabilities) has a brief description in the schema. The tool description adds no further semantics beyond what the schema already provides. Baseline score of 3 is appropriate as the description neither enhances nor detracts.

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

    Purpose4/5

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

    The description clearly states the tool's function: assessing alignment with UEF principles. It uses a specific verb ('assess') and resource ('alignment'), making the purpose understandable. However, it does not differentiate from sibling tools like 'uef_analyze_decision' or 'consciousness_evolution_metrics', so a slight deduction applies.

    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 usage guidelines are provided. The description does not specify when to use this tool versus its siblings (uef_analyze_decision, consciousness_evolution_metrics) or any prerequisites. This leaves the agent without context for correct invocation.

    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, the description must fully disclose behavior. It only states that metrics are 'comprehensive' and combine two analyses, but omits traits like side effects, idempotency, auth requirements, or output nature. This is insufficient for a tool with no output schema.

    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 a single sentence, very concise. It front-loads the verb and resource. However, brevity comes at the cost of completeness, and the sentence could be structured to include more context without significant lengthening.

    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 abstract tool name, lack of output schema, and no parameter context, the description is insufficient. It does not explain what the metrics contain, how to interpret them, or any usage context, leaving the agent with minimal understanding.

    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?

    There are zero parameters, so schema coverage is 100%. According to guidelines, the baseline is 4 even without param info. The description does not need to add parameter details, and it does not contradict or mislead.

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

    Purpose4/5

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

    The description uses a specific verb 'Get' and identifies the resource as 'consciousness evolution metrics', clearly distinguishing it from sibling tools like uef_analyze_decision and uef_assess_alignment. However, the terms 'UEF' and 'recursive doubt analysis' are domain-specific and not explained, slightly reducing clarity.

    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 on when to use this tool versus alternatives. The description does not mention when not to use it, prerequisites, or context for selection, leaving the agent without explicit direction.

    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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