CoreStory Context API

openapi: 3.2.0
info:
  title: Crowdbotics API Documentation Context API
  description: '# CoreStory API Overview


    The CoreStory API provides programmatic access to structured insights derived from software source code. It is designed for engineering, architecture, and product teams who need to extract business logic, technical specifications, architectural relationships, and system-level context from complex or legacy codebases. By interfacing directly with CoreStory API, users can:


    - Generate full Product Requirements Documents (PRDs) from a codebase

    - Extract detailed Technical Specifications for modernization or migration

    - Query a codebase using natural language to surface implementation details, logic paths, dependencies, and architectural insights

    - Construct and visualize knowledge graphs of systems, components, and relationships

    - Ingest, store, and retrieve content from a vector store for AI-powered retrieval-augmented generation (RAG) workflows


    CoreStory is especially valuable in contexts where:


    - Codebases are large, unstructured, or under-documented

    - Product and engineering teams are onboarding to unfamiliar systems

    - Technical stakeholders require visibility into system design or feature coverage

    - Modernization, replatforming, or M&A due diligence is underway


    The API can be integrated into existing CI/CD pipelines, internal developer portals, architecture review boards, product planning processes, or documentation automation workflows. All endpoints support secure authentication and are designed for high-availability, asynchronous workloads. Output formats include JSON, Markdown, and PDF, with flexible formatting options for downstream consumption. For step-by-step usage instructions and workflows, refer to the CoreStory API Quick Start Guide. ## Features


    * **Document Generation**

    * Generate complete Product Requirements Documents (PRD)

    * Generate Technical Specifications

    * Generate individual document sections (executive overview, user personas, etc.)

    * Format documents in JSON or Markdown


    * **Vector Store Management**

    * Ingest and manage documents in vector store

    * Query and retrieve document content

    * Manage document embeddings


    * **Context Management**

    * Manage context for different document types

    * Set and retrieve PRD context

    * Support for future context types (technical specs, user stories, etc.)


    * **Knowledge Graph**

    * Generate knowledge graphs from codebase queries

    * Visualize relationships between code entities

    * Analyze code structure and dependencies


    * **API Endpoints**

    * `/api/documents/*` - Document generation and formatting

    * `/api/generate/*` - Document formatting utilities

    * `/api/vector-store/*` - Vector store management

    * `/api/agents/*` - AI agent operations

    * `/api/context/*` - Context management for documents

    * `/api/graph/*` - Knowledge graph generation and analysis


    Note: JWT authentication support coming soon. All endpoints currently provide detailed OpenAPI documentation. * **Observability**

    * OpenTelemetry instrumentation for distributed tracing

    * Prometheus metrics endpoint at `/metrics`

    * Comprehensive request/response tracking

    * Custom business metrics


    # CoreStory System Architecture


    This document provides a high-level overview of how the CoreStory platform ingests, analyzes, and serves structured software intelligence through its API. The system is designed to process codebases and related technical artifacts in order to generate product specifications, technical documents, and knowledge graph representations. ---


    ## Core Components


    ### 1. **Ingestion Layer**


    - Accepts Git URLs, file uploads, or local archives

    - Extracts source files, documentation, and metadata

    - Initiates chunking and embedding via the vector store pipeline


    ### 2. **Vector Store and Context Engine**


    - Stores chunked and embedded documents

    - Supports semantic search and document retrieval

    - Feeds LLMs with scoped context for generation and reasoning


    ### 3.'
  version: 1.33.0
servers:
- url: /
  description: Current server
security:
- BearerAuth: []
tags:
- name: Context
paths:
  /api/projects/{project_id}/prd/context:
    get:
      tags:
      - Context
      summary: Get the context for the project's latest PRD
      description: 'Get the context for the project''s latest PRD.


        Args:

        project_id: ID of the Project


        Returns:

        PRDContextResponse: The PRD context with creation timestamp'
      operationId: get_prd_context
      parameters:
      - name: project_id
        in: path
        required: true
        schema:
          type: integer
          title: Project Id
      responses:
        '200':
          description: The PRD context with creation timestamp.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/PRDContextResponse'
        default:
          description: Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
    post:
      tags:
      - Context
      summary: Set or update the context for the project's latest PRD
      description: 'Set or update the context for a specific PRD.


        Args:

        project_id: ID of the Project


        Returns:

        PRDContextResponse: The updated PRD context with creation timestamp'
      operationId: set_prd_context
      parameters:
      - name: project_id
        in: path
        required: true
        schema:
          type: integer
          title: Project Id
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/PRDContextRequest'
      responses:
        '200':
          description: The updated PRD context with creation timestamp.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/PRDContextResponse'
        default:
          description: Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
components:
  schemas:
    ErrorBody:
      properties:
        message:
          type: string
          title: Message
        type:
          type: string
          title: Type
        details:
          additionalProperties: true
          type: object
          title: Details
          description: Optional extra context for the error.
        code:
          anyOf:
          - type: string
          - type: 'null'
          title: Code
          description: Stable machine-readable code for clients that branch on the specific failure.
        required_organization:
          anyOf:
          - $ref: '#/components/schemas/RequiredOrganization'
          - type: 'null'
          description: Set when the caller can retry after switching organization context.
      type: object
      required:
      - message
      - type
      title: ErrorBody
    PRDContextRequest:
      properties:
        context:
          type: string
          title: Context
          description: The context to set for the PRD
          example: This is the context for the PRD that will be used for document generation.
      type: object
      required:
      - context
      title: PRDContextRequest
      description: Request model for setting PRD context.
    PRDContextResponse:
      properties:
        context:
          type: string
          title: Context
          description: The PRD context
        created_at:
          type: string
          format: date-time
          title: Created At
          description: When the context was created
      type: object
      required:
      - context
      - created_at
      title: PRDContextResponse
      description: Response model for PRD context.
    RequiredOrganization:
      properties:
        id:
          type: string
          title: Id
          description: Clerk organization id the client should switch to.
        name:
          type: string
          title: Name
          description: Display name of that organization.
      type: object
      required:
      - id
      - name
      title: RequiredOrganization
      description: The organization a caller must be acting under before a denied request can succeed.
    ErrorSchema:
      properties:
        error:
          $ref: '#/components/schemas/ErrorBody'
      type: object
      required:
      - error
      title: ErrorSchema
  securitySchemes:
    BearerAuth:
      type: http
      scheme: bearer
      bearerFormat: JWT
      description: Enter the Bearer token from Clerk authentication
x-tagGroups:
- name: Ingest Your Codebase
  tags:
  - vector_store
  - projects
  - pre_ingestion
  - reingestion
- name: Generate Code Intelligence
  tags:
  - documents
  - document_generation
  - document_formatters
  - prd
  - prd_version
  - tech_spec
  - quality_metrics
  - sample_projects
- name: Query Your Codebase
  tags:
  - conversations
  - workflows
  - discovery
  - context
  - artifacts
- name: Manage and Inspect Intelligence
  tags:
  - api_debugging
  - events
  - version
  - files
  - cache
  - token_tracking
- name: Authenticate and Manage Access
  tags:
  - clerk_authentication
  - git_webhooks
  - github_integration
  - organizations
  - api_key_management
  - mcp_token_management
  - user
- name: (Advanced) Prompt Tuning
  tags:
  - prompts
- name: MCP Protocol
  tags:
  - mcp_protocol
- name: Administration
  tags:
  - admin
x-provenance:
  method: harvested
  publisher: CoreStory
  source: https://c2s.corestory.ai/openapi.json
  note: split by refine-openapis from the provider's own published document, verified by fetch (openapi/_original/*.fetch.json);
    stamped by stamp-openapi-provenance.py (roadmap#793)