Build Knowledge Graph

Reverse engineer the architecture of the codebase and build a comprehensive knowledge graph using the gnapsis MCP tools, following a strict derivation order methodology.

If an argument is provided, focus the analysis on that scope or path. Otherwise, analyze the entire codebase.

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CRITICAL RULES

1. Always use gnapsis MCP tools to register all nodes and relationships in the knowledge graph. The gnapsis graph is the primary output.
2. Proceed through phases, summarize at each phase boundary. Complete each phase fully, then summarize what was done before moving to the next.
3. Use best judgment for ambiguity: When you encounter ambiguity about business domains, feature boundaries, or architectural decisions, use your best technical judgment and document your reasoning. Add a note in the entity description when a decision was ambiguous.
4. ALWAYS run analyze_document before creating entities for a source file. This gives you the exact LSP symbol names. Never guess symbol names.
5. Use ref_type: "code" with lsp_symbol for source files. Only use ref_type: "text" for markdown, docs, and config files.
6. Be exhaustive, not superficial. Scan every directory, every configuration file, every module entry point. Leave no stone unturned.

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GNAPSIS TOOL REFERENCE

Initialization & Discovery

• init_project — Initialize the database schema (run once at start)
• project_overview — Get current ontology: taxonomy (categories by scope), entity hierarchy, statistics

Entity Lifecycle

• create_entity(name, description, category_ids, parent_ids, commands) — Create an entity with at least one reference
• update_entity(entity_id, ...) — Update entity, add/remove references, create relationships
• delete_entity(entity_id) — Delete entity (must have no children)

Taxonomy

• create_category(name, scope) — Create a new category at a scope (if the defaults don't fit)

Querying

• get_entity(entity_id) — Full entity details with references and relationships
• find_entities(scope, category, parent_id) — Filter entities by scope/category/parent
• search(query) — Semantic search across entities and references
• query(entity_id, semantic_query) — Extract relevant subgraph within token budget
• get_document_entities(document_path) — Get all entities referenced in a file

Document Analysis

• analyze_document(document_path) — CRITICAL: Discover tracked refs, untracked LSP symbols, and git diffs. Run this BEFORE creating entities for any source file.

Maintenance

• alter_references(commands) — Bulk update/delete references
• validate_graph() — Check for orphans, cycles, scope violations, missing references
• get_changed_files() — Find files modified since last sync

Entity Commands (used in create_entity/update_entity commands array)

• { type: "add", ref_type: "code", document_path: "...", lsp_symbol: "...", description: "..." } — For source files
• { type: "add", ref_type: "text", document_path: "...", start_line: N, end_line: M, description: "..." } — For docs/config
• { type: "relate", entity_id: "...", note: "..." } — Create RELATED_TO relationship
• { type: "link", entity_id: "...", link_type: "calls|imports|implements|instantiates" } — Code links (Component/Unit only)

Default Categories (from project_overview)

Scope	Categories
Domain	core, infrastructure
Feature	functional, technical, non-functional
Namespace	module, library
Component	struct, trait, enum, class
Unit	function, method, constant, field

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MANDATORY WORKFLOW: Creating Entities from Source Files

1. analyze_document(document_path: "src/foo.rs")
   → Returns untracked[] with exact LSP symbol names

2. create_entity(
     name: "FooService",
     description: "Service for foo operations",
     category_ids: ["<struct-category-id>"],
     parent_ids: ["<parent-namespace-id>"],
     commands: [{
       type: "add",
       ref_type: "code",
       document_path: "src/foo.rs",
       lsp_symbol: "FooService",        ← MUST match analyze_document output
       description: "FooService struct"
     }]
   )

NEVER skip analyze_document. NEVER guess lsp_symbol names.

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PHASE 1: STACK IDENTIFICATION

Before anything else, build a complete understanding of the project's technology stack.

Steps:

1. Scan the root directory for configuration files: package.json, Cargo.toml, go.mod, pom.xml, build.gradle, requirements.txt, pyproject.toml, Gemfile, composer.json, Makefile, Dockerfile, docker-compose.yml, CI/CD configs, etc.
2. Identify the primary language(s) and their versions.
3. Catalog all libraries and frameworks with their roles (web framework, ORM, testing, logging, auth, etc.).
4. Map the persistence layer: databases, migration tools, ORM configurations.
5. Identify service dependencies: external APIs, microservice connections, message brokers, cloud services.
6. Identify infrastructure patterns: containerization, orchestration, CI/CD pipelines, deployment targets.
7. Identify architectural style: monolith, microservices, modular monolith, serverless, event-driven, hexagonal, clean architecture, MVC, CQRS, etc.

Gnapsis Setup:

1. Run project_overview to get the current ontology state and available category IDs.
2. Note all category IDs — you will need them for every create_entity call.

Output:

Summarize findings to the user. Optionally write a docs/stack.md if the user requests documentation.

After summarizing, proceed to Phase 2.

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PHASE 2: KNOWLEDGE GRAPH CONSTRUCTION (Gnapsis Derivation Order)

Build the knowledge graph layer by layer following the strict gnapsis scope hierarchy:

Domain → Feature → Namespace → Component → Unit

Scope Definitions:

• Domain: A bounded context representing a major business or technical concern (e.g., Authentication, Graph Abstraction, MCP Server).
• Feature: A cohesive capability within a domain (e.g., within Auth: Login, Registration, Password Reset).
• Namespace: A code module or package that implements part of a feature (e.g., services, repositories, mcp::tools).
• Component: A concrete code artifact — struct, trait, class, enum (e.g., UserService, AuthTrait).
• Unit: An atomic functional element — function, method, constant, field (e.g., validate(), MAX_RETRIES).

Process for EACH Domain:

Step 2.1: Domain Discovery

• Analyze directory structure, namespace patterns, and module boundaries.
• Look for domain indicators: directory names, namespace prefixes, bounded context markers, configuration sections.
• Cross-reference with the stack analysis to understand framework-specific conventions.
• Register each domain using create_entity with a Domain-scope category.

Step 2.2: Domain Summary

For each identified domain, briefly log:

• Domain name and description
• Constituent features (enumerated)
• Key entry points and interfaces
• Dependencies on other domains (preliminary)

Then proceed immediately to build the subgraph.

Step 2.3: Full Domain Subgraph Construction

Build the complete subgraph for each domain:

1. Register Features under the domain. For each feature:

   • Use create_entity with Feature-scope category and parent_ids: [<domain-id>].
   • Identify all code paths that implement this feature.

2. Register Namespaces under each feature. For each namespace:

   • Use create_entity with Namespace-scope category and parent_ids: [<feature-id>].
   • Map module boundaries and imports/exports.

3. Register Components under each namespace. For each component:

   • First run analyze_document(document_path) to discover LSP symbols.
   • Use create_entity with Component-scope category, parent_ids: [<namespace-id>], and a code reference using the exact lsp_symbol from analyze_document.

4. Register Units under each component. For each unit:

   • Use create_entity with Unit-scope category and parent_ids: [<component-id>].
   • Reference the exact LSP symbol from analyze_document.

5. Register relationships using update_entity commands:

   • { type: "relate", entity_id: "...", note: "..." } for semantic relationships.
   • { type: "link", entity_id: "...", link_type: "calls" } for code-level links.

Step 2.4: Repeat for All Domains

Proceed domain by domain. After completing all domains, summarize progress and proceed to Phase 3.

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PHASE 3: INTER-DOMAIN RELATIONSHIP ANALYSIS AND OPTIMIZATION

After all domain subgraphs are built:

Step 3.1: Cross-Domain Dependency Scan

• Trace all imports, calls, events, and data flows that cross domain boundaries.
• Identify shared models, common utilities, and cross-cutting concerns.
• Map integration points: API calls between domains, shared database tables, event bus topics.

Step 3.2: Register Inter-Domain Relationships

For each cross-domain relationship, use update_entity with:

• { type: "relate", entity_id: "<target>", note: "description of relationship" } for semantic links.
• { type: "link", entity_id: "<target>", link_type: "calls|imports|implements|instantiates" } for code links.

Step 3.3: Coupling and Cohesion Analysis

• Cohesion check: Are all elements within each domain/feature/namespace closely related? Flag low-cohesion areas.
• Coupling check: Are inter-domain dependencies minimal and well-defined? Flag high-coupling areas.
• Identify architectural smells: circular dependencies, god modules, feature envy, shotgun surgery.

Step 3.4: Graph Validation

Run validate_graph() to check for:

• Orphan entities (no parent at non-Domain scope)
• Cycles in BELONGS_TO relationships
• Scope violations (child scope not deeper than parent)
• Entities without references
• Entities without classification

Fix any issues found.

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PROGRESS TRACKING

• At the start of each phase, announce what you're about to do.
• After completing each domain, summarize what was registered in the graph (entity counts by scope).
• After completing all phases, provide a final summary.

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QUALITY ASSURANCE

Before declaring completion:

1. Run validate_graph() and fix all reported issues.
2. Verify every source file has been analyzed via analyze_document.
3. Cross-check the graph against the directory structure for completeness using find_entities at each scope.
4. Ensure all relationship types are consistent and accurately labeled.
5. Confirm the derivation order (Domain → Feature → Namespace → Component → Unit) is respected throughout.
6. Present a final summary with graph statistics from project_overview.

The final deliverable is the complete knowledge graph in gnapsis. Every node, every edge, every relationship must be registered through the gnapsis MCP tools.