# Feature Specification: Production-Grade MCP Server for Semantic Code Search
**Feature Branch**: `001-build-a-production`
**Created**: 2025-10-06
**Status**: Draft
**Input**: User description: "Build a production-grade MCP server that indexes code repositories into PostgreSQL with pgvector for semantic search, designed specifically for AI coding assistants. The MCP should be able to ingest an existing codebase, create embeddings for the files and store them in the database. It should also be able to add/update as the code is developed. It should include a task reminder system that can be used to help during the development process where the developer can log tasks with notes and related planning files that need to be done, are in-progress, and complete with related branch and commit details."
## Execution Flow (main)
```
1. Parse user description from Input
→ If empty: ERROR "No feature description provided"
2. Extract key concepts from description
→ Identify: actors, actions, data, constraints
3. For each unclear aspect:
→ Mark with [NEEDS CLARIFICATION: specific question]
4. Fill User Scenarios & Testing section
→ If no clear user flow: ERROR "Cannot determine user scenarios"
5. Generate Functional Requirements
→ Each requirement must be testable
→ Mark ambiguous requirements
6. Identify Key Entities (if data involved)
7. Run Review Checklist
→ If any [NEEDS CLARIFICATION]: WARN "Spec has uncertainties"
→ If implementation details found: ERROR "Remove tech details"
8. Return: SUCCESS (spec ready for planning)
```
---
## ⚡ Quick Guidelines
- ✅ Focus on WHAT users need and WHY
- ❌ Avoid HOW to implement (no tech stack, APIs, code structure)
- 👥 Written for business stakeholders, not developers
### Section Requirements
- **Mandatory sections**: Must be completed for every feature
- **Optional sections**: Include only when relevant to the feature
- When a section doesn't apply, remove it entirely (don't leave as "N/A")
### For AI Generation
When creating this spec from a user prompt:
1. **Mark all ambiguities**: Use [NEEDS CLARIFICATION: specific question] for any assumption you'd need to make
2. **Don't guess**: If the prompt doesn't specify something (e.g., "login system" without auth method), mark it
3. **Think like a tester**: Every vague requirement should fail the "testable and unambiguous" checklist item
4. **Common underspecified areas**:
- User types and permissions
- Data retention/deletion policies
- Performance targets and scale
- Error handling behaviors
- Integration requirements
- Security/compliance needs
---
## Clarifications
### Session 2025-10-06
- Q: What are the target performance requirements for repository indexing? → A: Medium repos (up to 10,000 files / 1M lines) indexed in under 60 seconds
- Q: What is the acceptable latency for semantic search queries? → A: Under 500ms (responsive, suitable for on-demand queries)
- Q: How should the system handle deleted files? → A: Mark as deleted, retain for 90 days (medium-term recovery)
- Q: What file ignore patterns should the system respect when scanning repositories? → A: .gitignore + custom .mcpignore file (flexible control)
- Q: Which programming languages and file types should the system index? → A: All text files (language-agnostic, detect by content)
---
## User Scenarios & Testing *(mandatory)*
### Primary User Story
As an AI coding assistant (Claude Code, Claude Desktop, etc.), I need to semantically search through a developer's codebase and track development tasks so that I can provide contextually accurate assistance, understand project progress, and help developers stay organized during the development process.
### Acceptance Scenarios
1. **Given** a code repository on the local filesystem, **When** the MCP server is configured to index the repository, **Then** all source files are scanned, semantic embeddings are generated and stored in the database with searchable metadata
2. **Given** an indexed codebase, **When** a developer modifies existing files or adds new files, **Then** the MCP server detects the changes and updates the embeddings without requiring full re-indexing
3. **Given** an AI assistant needs to find relevant code, **When** a semantic search query is submitted, **Then** the system returns the most relevant code snippets ranked by similarity with sufficient context for understanding
4. **Given** a developer working on a feature, **When** they create a task with description, notes, and planning file references, **Then** the task is stored with "need to be done" status and associated metadata
5. **Given** a developer starts working on a task, **When** they update the task status to "in-progress" with branch information, **Then** the task tracking system records the status change and branch association
6. **Given** a developer completes a task, **When** they mark it "complete" with commit details, **Then** the system stores the completion status, commit information, and maintains the full task history
7. **Given** multiple tasks exist in the system, **When** an AI assistant queries for project status or task context, **Then** accurate task information is returned including status, notes, related planning files, branch, and commit details
### Edge Cases
- What happens when the codebase exceeds target size (>10,000 files or >1M lines)?
- How does the system handle binary files or non-text files?
- What happens when file encoding is ambiguous or corrupted?
- How are deleted files handled after the 90-day retention period expires?
- What happens when concurrent processes modify the same files?
- How does the system handle tasks without branch or commit information?
- What happens if planning file references in tasks point to non-existent files?
- How are task conflicts resolved when multiple users work on the same repository?
## Requirements *(mandatory)*
### Functional Requirements
**Repository Indexing**
- **FR-001**: System MUST scan a specified code repository directory and identify all source files
- **FR-002**: System MUST generate semantic embeddings for source file content
- **FR-003**: System MUST store embeddings in a database with vector similarity search capabilities
- **FR-004**: System MUST store file metadata including path, size, modification timestamp, and file type
- **FR-005**: System MUST respect both .gitignore patterns and custom .mcpignore file patterns when scanning repositories
- **FR-006**: System MUST index all text files in a language-agnostic manner, detecting content type automatically
**Incremental Updates**
- **FR-007**: System MUST detect when files in the repository are added, modified, or deleted
- **FR-008**: System MUST update embeddings only for changed files, not the entire repository
- **FR-009**: System MUST mark deleted files and retain their embeddings for 90 days before permanent removal
- **FR-010**: System MUST maintain consistency between filesystem state and database state
**Semantic Search**
- **FR-011**: System MUST accept natural language queries from AI assistants
- **FR-012**: System MUST return code snippets ranked by semantic similarity to the query
- **FR-013**: System MUST provide 10 lines of context before and after each search result
- **FR-014**: System MUST support filtering results by [NEEDS CLARIFICATION: file type, directory, date range, or other criteria?]
- **FR-015**: System MUST handle queries when no relevant results exist gracefully
**Task Management**
- **FR-016**: Developers MUST be able to create tasks with description and notes
- **FR-017**: Tasks MUST support references to planning files [NEEDS CLARIFICATION: file paths, URLs, or structured references?]
- **FR-018**: Tasks MUST have status that can be set to "need to be done", "in-progress", or "complete"
- **FR-019**: Tasks MUST support association with git branch names
- **FR-020**: Tasks MUST support association with git commit identifiers
- **FR-021**: System MUST track status transitions and maintain task history [NEEDS CLARIFICATION: what level of history detail?]
- **FR-022**: System MUST allow querying tasks by status, branch, or other criteria
- **FR-023**: System MUST support updating task notes and metadata after creation
**AI Assistant Integration**
- **FR-024**: System MUST expose search functionality to AI assistants
- **FR-025**: System MUST expose task querying functionality to AI assistants
- **FR-026**: System MUST handle concurrent requests from AI assistants [NEEDS CLARIFICATION: expected concurrency level?]
- **FR-027**: System MUST provide task context when AI assistants request project status
- **FR-028**: System MUST link search results to related tasks when associations exist
**Data Persistence & Reliability**
- **FR-029**: System MUST persist all data durably in the database
- **FR-030**: System MUST handle database connection failures gracefully
- **FR-031**: System MUST provide data integrity guarantees [NEEDS CLARIFICATION: transaction requirements?]
- **FR-032**: System MUST support database backup and restoration [NEEDS CLARIFICATION: automatic or manual?]
- **FR-033**: System MUST log errors and operations for debugging and auditing
**Performance**
- **FR-034**: System MUST index medium-sized repositories (up to 10,000 files or 1M lines of code) within 60 seconds
- **FR-035**: System MUST respond to search queries within 500ms (p95 latency)
- **FR-036**: System MUST handle incremental updates without degrading search performance
- **FR-037**: System MUST support [NEEDS CLARIFICATION: how many concurrent users/AI assistants?]
**Production Quality**
- **FR-038**: System MUST validate configuration on startup and fail fast with clear error messages
- **FR-039**: System MUST handle malformed or corrupted files without crashing
- **FR-040**: System MUST provide health check endpoints or status indicators [NEEDS CLARIFICATION: for monitoring systems?]
- **FR-041**: System MUST support graceful shutdown without data loss
### Key Entities
- **CodeFile**: Represents a source code file with path, content hash, modification timestamp, file size, programming language
- **CodeChunk**: Semantic chunks of code (functions, classes) with associated embedding vectors
- **Embedding**: The semantic vector representation of code content with model metadata (model name, dimensions, generation timestamp)
- **Task**: A development task with unique identifier, description, notes, status (need to be done/in-progress/complete), creation timestamp, and update history
- **TaskPlanningReference**: Link between tasks and related planning documents (specs, design docs, requirements)
- **TaskBranchLink**: Association between tasks and git branch names for tracking work context
- **TaskCommitLink**: Association between tasks and git commit identifiers for tracking completed work
- **SearchQuery**: Record of search requests with query text, results returned, and timestamp for analytics and optimization
- **ChangeEvent**: Record of file system changes (add/modify/delete) with timestamps for incremental indexing triggers
- **IndexingStatus**: Tracks the indexing state of the repository including last index time, files processed, and any errors
---
## Review & Acceptance Checklist
*GATE: Automated checks run during main() execution*
### Content Quality
- [X] No implementation details (languages, frameworks, APIs)
- [X] Focused on user value and business needs
- [X] Written for non-technical stakeholders
- [X] All mandatory sections completed
### Requirement Completeness
- [ ] No [NEEDS CLARIFICATION] markers remain
- [ ] Requirements are testable and unambiguous
- [ ] Success criteria are measurable
- [X] Scope is clearly bounded
- [X] Dependencies and assumptions identified
---
## Execution Status
*Updated by main() during processing*
- [X] User description parsed
- [X] Key concepts extracted
- [X] Ambiguities marked
- [X] User scenarios defined
- [X] Requirements generated
- [X] Entities identified
- [ ] Review checklist passed (pending clarifications)
---
## Notes & Recommendations
This specification captures the core functionality needed for a production-grade MCP server focused on semantic code search and task tracking. The system will serve AI coding assistants by providing:
1. **Semantic Code Understanding**: Deep codebase indexing with vector embeddings enables AI assistants to find relevant code based on intent, not just keywords
2. **Incremental Intelligence**: Smart update detection ensures the index stays current as code evolves without expensive full re-indexing
3. **Development Context**: Task tracking with planning references, branch, and commit details gives AI assistants awareness of project progress and developer intent
4. **Production Readiness**: Requirements focus on reliability, performance, error handling, and graceful degradation
**Key Clarifications Needed** (15 items marked):
- Performance targets (index time, search latency, repository size limits)
- File handling policies (ignore patterns, supported languages, deletion behavior)
- Task management details (planning reference format, history granularity)
- Operational requirements (concurrency levels, backup strategy, monitoring integration)
**Next Steps**:
1. Run `/clarify` to resolve ambiguities through targeted Q&A
2. Proceed to `/plan` for technical design and implementation planning
3. Use `/tasks` to generate detailed task breakdown
4. Execute with `/implement` using orchestrated subagent workflow
The specification is ready for the clarification phase.
