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Render a code node's relationships in compact Grafema DSL notation, condensing verbose edge listings into archetype-grouped operators. Control detail depth for optimal context window usage.

Instructions

Render a node's neighborhood as compact Grafema DSL notation.

Reduces verbose edge listings to archetype-grouped visual operators: o- dependency/import

outward flow (calls, delegates, passes) < inward flow (reads, extends, receives) => persistent write (db, file, redis) x exception (throws, rejects) ~>> event/message (emits, publishes) ?| conditional guard (if, case) |= governance (governs, monitors)

Containment edges ({ }) define nesting structure.

Example output: login { o- imports bcrypt > calls UserDB.findByEmail, createToken < reads config.auth => writes session >x throws AuthError ~>> emits 'auth:login' }

Use depth to control detail: 0 = names only (children listed, no edges) 1 = edges (default — shows all relationship lines) 2 = nested + folded (compressed view — repetitive siblings collapsed) 3 = nested (exact — every node expanded, no folding)

10-30 lines vs 500+ lines of raw edge data. Ideal for LLM context windows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYesSemantic ID, file path, or node name to describe
depthNoLevel of detail: 0=names, 1=edges (default), 2=nested+folded (compressed), 3=nested (exact, no folding)
perspectiveNoArchetype filter preset: "security" (write,exception), "data" (flow_out,flow_in,write), "errors" (exception), "api" (flow_out,publishes,depends), "events" (publishes)
Behavior4/5

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

With no annotations, the description carries full transparency burden. It thoroughly explains the tool's behavior: generates DSL notation with specific operators, depth-controlled detail, compression, and example output. It lacks explicit mention of side effects or auth needs, but given it's a read-like operation, the coverage is strong.

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 well-organized with bullet points, an example output, and a clear breakdown of depth levels. Every sentence is informative and concisely communicates key details without verbosity.

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?

Given no annotations or output schema, the description provides a self-contained explanation of the tool's purpose, output format, parameters, and usage context (e.g., line count savings). It is complete enough for an AI agent to understand and use correctly.

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 coverage is 100%, so baseline is 3. The description adds significant extra meaning by elaborating depth levels (0-3) with precise definitions and an example. The perspective parameter is only listed in schema, but depth gets rich context. Overall adds value.

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's function: 'Render a node's neighborhood as compact Grafema DSL notation.' It specifies the verb (render), resource (node's neighborhood), and output format. This distinctly differentiates it from sibling tools like analysis, querying, or exploration tools.

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 provides clear context on when to use the tool (reducing verbose edge listings, ideal for LLM context windows) and explains depth parameter behavior. However, it does not explicitly state when not to use it or compare to alternatives, which would improve guidance.

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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