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cuba_vigia

Analyze knowledge graph health, detect drift, identify communities and bridges, and compute structural centrality metrics for backbone analysis.

Instructions

Knowledge graph analytics: summary (counts + token estimate), health (staleness, entropy, DB size), drift (chi-squared on errors), communities (Leiden), bridges (betweenness centrality). v0.9: 'structural' returns harmonic + closeness + k-core ranking for backbone identification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricYesMetric to compute. v0.9: 'structural' adds harmonic + closeness centrality (Boldi-Vigna 2014, Bavelas 1950) + k-core decomposition (Seidman 1983).
Behavior2/5

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

No annotations are provided, so the description must fully convey behavioral traits. It mentions version v0.9 and that 'structural' returns specific centralities, but does not disclose whether the tool is read-only, has side effects, requires authorization, or any other behavioral aspects. The description is insufficient for a agent to understand the tool's safety profile.

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, listing all metrics in a clear, scannable format. Every sentence adds value, and there is no extraneous text. The structure is appropriate for quick understanding.

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 single enum parameter and no output schema, the description gives a reasonable idea of what each metric returns. However, it lacks details on output format, return structure, or error handling. For a tool with multiple analytic capabilities, an agent might need more context on how to interpret results.

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 the single parameter 'metric' with detailed enum values, including the v0.9 note. The tool description adds an overview of each metric's output, but this is largely redundant. Since schema coverage is 100%, the description provides minimal additional value beyond a summary.

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 that the tool performs knowledge graph analytics and lists all specific metrics (summary, health, drift, communities, bridges, structural) with brief explanations. It distinguishes itself from sibling tools which have different purposes (e.g., cuba_proyecto, cuba_alma).

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 does not provide any guidance on when to use this tool versus alternatives. It only lists what metrics are available without context about appropriate use cases or exclusions. No comparison with sibling tools is made.

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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