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cdeust

hypernmnesia-mcp-viz

by cdeust
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Server Quality Checklist

67%
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  • Latest release: v3.1.1

  • Disambiguation3/5

    With only one tool exposed in this server, the ambiguity risk is with tools referenced in the description (open_visualization, list_domains, get_causal_chain) that may or may not actually exist in other servers. The single tool is well-described and clearly distinguished from the referenced alternatives, but the actual tool set provides no way to verify these distinctions.

    Naming Consistency3/5

    The single tool 'get_methodology_graph' uses a clear get_noun_graph pattern, which is readable and consistent. However, with only one tool, there is no pattern to evaluate across the set, and the referenced tools (open_visualization, list_domains, get_causal_chain) suggest mixed conventions that cannot be assessed in this server alone.

    Tool Count2/5

    A single tool feels extremely thin for what appears to be a visualization-oriented MCP server. The description references multiple related tools (open_visualization, list_domains, get_causal_chain) that would be natural companions in the same server, making the lone tool seem like an incomplete surface.

    Completeness2/5

    The server only exposes graph-export functionality for visualization. Based on the description's references to browser launching, domain listing, and causal chain traversal, there are clear gaps: no way to launch the visualizer, no text overview, and no entity-graph traversal from this server. The JSON export alone is a fragmented piece of a larger intended workflow.

  • Average 4.5/5 across 1 of 1 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 193 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.

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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

  • Behavior5/5

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

    The description goes far beyond the annotations by disclosing side effects (HTTP server process, browser tab, idle auto-shutdown), lazy graph build, performance timelines, and the no-DB degradation mode. It also states the returned fields and the nature of the call, all without contradicting the readOnly/openWorld/idempotent hints.

    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 long but well-organized, front-loaded with the core purpose and then detailing side effects, performance, and fallback. While every sentence adds value, the length could be trimmed slightly without losing essential information.

    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?

    It covers purpose, usage context, side effects, performance characteristics, fallback behavior, and return value shape. Given the tool's complexity and the presence of an output schema, this description provides complete contextual coverage for an agent to select and invoke the tool confidently.

    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?

    The schema has 0% coverage (no parameter descriptions), and the description does not explain what `view` or `domain` expect. It mentions several visualization views (e.g., Wiki, Atlas, Graph) but never maps them to the `view` parameter values, and `domain` is entirely unaddressed. This is a clear gap for correct invocation.

    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 opens the bundled Cortex visualization in the user's default browser and enumerates the included views. It also explicitly distinguishes it from sibling tools `get_methodology_graph` and `list_domains`, making the purpose unambiguous.

    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?

    It provides explicit usage guidance: 'Use this for visual exploration, screenshots, or presenting Cortex state.' It also names alternatives and contrasts them, and explains the no-DB fallback scenario, giving the agent clear conditions for when to use this tool.

    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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which cover safety. The description adds valuable behavioral detail beyond annotations: output caps (200 nodes/500 edges), highest-quality-first truncation, latency <100ms, and read-only on profiles.json + memories. It doesn't note any side effects or error conditions, but the read-only nature plus caps is solid coverage given annotations.

    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?

    Four dense sentences, each earning its place: purpose, output structure, safety, and performance/cap limits. The truncation detail and latency are front-loaded with the purpose. No fluff, no repetition. Highly efficient.

    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?

    Despite having an output schema, the description thoroughly explains the return shape ({nodes, edges, meta}, with optional truncation fields), the caps, and the visualization use case. With annotations covering safety and idempotency, and the description covering purpose, alternatives, caps, latency, and read-only scope, there is nothing significant left unexplained.

    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?

    There is only 1 parameter (domain, optional, nullable) with 0% schema description coverage. The description mentions 'domains' as node types but does not explicitly explain what the 'domain' parameter filters or how null vs a value changes behavior. Since the parameter is optional and there's only one, the baseline expectation is that the description explains its effect—it doesn't, though the tool works without it.

    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 builds a methodology map as JSON graph data for force-directed visualization, listing node and edge types specifically. It explicitly distinguishes from three sibling tools with specific differentiators, making the purpose unambiguous.

    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?

    The description explicitly names three alternatives (open_visualization, list_domains, get_causal_chain) and explains what each does differently, giving the agent clear when-to-use and when-not-to-use guidance. It also states this feeds a custom client visualizer, establishing the appropriate context.

    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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