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analyze_graph_pagerank

Identify the most important entities in the knowledge graph by calculating PageRank scores based on their connections and influence.

Instructions

Calculate PageRank scores for entities in the knowledge graph. PageRank identifies the most 'important' or 'central' entities based on their connections. Higher scores indicate entities that are more connected and influential in the knowledge network.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_typesNoFilter graph to specific entity types (optional)
min_occurrencesNoMinimum entity occurrences (default: 2)
top_nNoNumber of top entities to return (default: 20)
alphaNoDamping parameter for PageRank (default: 0.85)
Behavior2/5

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

No annotations provided, so description should disclose behavioral traits. Only mentions it calculates scores, nothing about side effects (read-only), permissions, or performance implications. For a graph analysis tool, this is insufficient.

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?

Two sentences, front-loaded with action, no filler. All words add value.

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?

No output schema, but description does not specify return format (list of entities with scores?). Lacks details on result structure, which is critical for a computation tool.

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?

Schema has 100% coverage with descriptions for all 4 parameters. Description adds conceptual value (explaining PageRank importance) but no additional parameter-level details beyond schema.

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?

Clear verb-resource pair ('Calculate PageRank scores for entities') and states purpose (identify important entities). However, does not distinguish from sibling tool 'calculate_graph_centrality' which may overlap.

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 vs alternatives like calculate_graph_centrality or other analytics tools. Missing context about prerequisites or typical scenarios.

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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