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archy_graph_summary

Identifies the most structurally important modules in a codebase by ranking fan-in, fan-out, and PageRank, plus top external dependencies, for quick architectural insight.

Instructions

Whole-project structural overview sized for LLM context. Returns top-N modules by fan-in, fan-out, and PageRank (importance weighted by importance of dependents), plus the top external dependencies. Cheaper than dumping the full graph; use for 'where is the gravity in this codebase' questions. Call archy_cycles separately for cycle detail.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
top_nNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
module_countYes
internal_edge_countYes
external_edge_countYes
parse_errorsYes
top_fan_inYes
top_fan_outYes
top_pagerankYes
top_edit_riskYes
external_depsYes
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool returns a summary with specific metrics (fan-in, fan-out, PageRank, external dependencies) and that it's cheaper than the full graph. There is no mention of side effects or permissions, but as a read-only summary, this is adequate.

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 two sentences with no filler. The first sentence states the action, the second provides usage guidance and sibling reference. Every word adds value.

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 complexity (structural overview with multiple metrics and external dependencies), the description covers the output type sufficiently. An output schema exists, so return details are not needed. However, the lack of parameter explanation is a significant gap for completeness.

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?

Schema description coverage is 0%, so the description must explain parameters. It indirectly explains 'top_n' by saying 'returns top-N modules', but it does not explain 'path', which likely refers to the project root. The lack of explicit parameter description leaves ambiguity, especially for 'path'.

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 returns a whole-project structural overview with top-N modules by fan-in, fan-out, and PageRank, plus external dependencies. It distinguishes itself from siblings by emphasizing it's a summary, not the full graph, and mentions using archy_cycles separately for cycle detail.

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

Usage Guidelines4/5

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

The description suggests using this tool for 'where is the gravity in this codebase' questions and explicitly directs to call archy_cycles for cycle detail. It hints at cheaper computation compared to dumping the full graph. However, it does not explicitly state when not to use it, such as when full graph details are needed.

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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