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Pharaoh-so

Pharaoh - Your AI breaks things it can't see

get_module_context

Read-only

Analyze a module's complete structure, dependencies, and functionality before making changes or writing documentation to prevent breaking existing systems.

Instructions

Get everything you need to know about a module BEFORE modifying it or writing a PRD.

CALL THIS WHEN: • You're about to change code in a module — understand its full surface area first • You're writing a PRD or design doc and need ground-truth about what exists • You need to know what depends on this module (who breaks if you change it) • You want to see a module's DB tables, endpoints, cron jobs, or env vars at a glance

RETURNS: Complete module profile in ~2K tokens: file count, LOC, all exported function signatures with complexity, dependency graph (imports from + imported by), DB table access, HTTP endpoints, cron jobs, env vars, vision spec alignment, and external callers from other modules.

EXAMPLES: • "What does the slack module look like before I add a new notification?" • "What endpoints does the auth module expose?" • "Which modules depend on the db module?" • "What env vars does the crons module use?"

WHY NOT JUST READ FILES: A module can span dozens of files. Manual exploration burns 10K-40K tokens and still misses cross-module callers, DB access patterns, and vision spec alignment. This returns the complete picture in one call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations only declare read-only safety; description adds substantial behavioral context: token cost (~2K), specific return payload structure (file count, LOC, function signatures, dependency graphs, DB access, endpoints, cron jobs, env vars), and performance comparison to manual exploration (10K-40K tokens saved). No contradictions with annotations.

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?

Though lengthy, content is densely valuable with zero redundancy. Structured with clear headers (CALL THIS WHEN, RETURNS, EXAMPLES, WHY NOT JUST READ FILES) and bullet points enabling scannability. Length is justified by absence of output schema and zero parameters requiring description to carry full semantic load.

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?

Compensates excellently for missing output schema by exhaustively documenting return value structure (listing 10+ specific data points from file counts to vision spec alignment). With zero parameters and no output schema, the description provides complete contractual information necessary for invocation.

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?

Input schema contains zero parameters, triggering baseline score of 4 per rubric. Description correctly omits parameter discussion as none exist, focusing instead on behavioral contract and return value semantics.

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?

Opens with specific verb+resource ('Get everything... about a module') and scope constraint ('BEFORE modifying'). Distinguishes from siblings like get_codebase_map (high-level architecture) and search_functions (single function level) by emphasizing comprehensive surface-area analysis for pre-modification safety.

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?

Contains explicit 'CALL THIS WHEN:' section with four specific triggering contexts (changing code, writing PRDs, checking dependents, viewing resources). Includes 'WHY NOT JUST READ FILES' subsection that explicitly contrasts with manual exploration, establishing clear alternative avoidance criteria.

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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