The External Brain for AI Coding Agents — see what your LLM can't see
Blindspot MCP — The External Brain for AI Coding Agents
See what your LLM can't see. Early release — Laravel production-tested, 15 more framework plugins in alpha.
v0.1.0 — Report bugs | Contribute
Install
pip install blindspot-mcp
Claude Code — add to ~/.claude/settings.json:
{
"mcpServers": {
"blindspot": {
"command": "blindspot-mcp",
"args": ["--project-path", "/path/to/your/project"]
}
}
}
Cursor / VS Code — add to .cursor/mcp.json:
{
"mcpServers": {
"blindspot": {
"command": "blindspot-mcp",
"args": ["--project-path", "."]
}
}
}
Why Blindspot Exists
I built Blindspot because I was frustrated. I use AI coding agents (Claude Code, Cursor) daily on my Laravel project, and they kept making the same mistakes — changing a model field without knowing which controllers use it, editing a route without checking the middleware chain, breaking cache invalidation because they couldn't see the full picture.
The root cause is simple: AI agents have limited context windows. They can read 5-10 files, but your project has hundreds. They edit blindly.
So I built an "external brain" — a tool that indexes the entire codebase and gives the AI structured intelligence without reading files into its context window. I used it on my own Laravel project for months, and the difference was night and day. The AI stopped breaking things. It started writing code that actually understood the project.
Now I'm open-sourcing it with support for 16 frameworks across 12 programming languages, because every developer using AI coding tools deserves this.
This is a community project. The Laravel plugin is battle-tested on a real production codebase. The other framework plugins are architecturally complete but need real-world testing. Your contributions — bug reports, edge case fixes, new framework support — will make this the definitive code intelligence tool for AI agents.
The Problem
Every AI coding agent today works like this:
Developer: "Change the is_active field on the User model"
AI Agent: *reads User.php*
AI Agent: *makes the change*
AI Agent: "Done!"
Reality: 14 controllers, 8 templates, 3 cache keys, and 2 form
validations just broke. The AI had no idea they existed.
Why does this happen?
Your Project: ~500 files, ~50,000 lines of code
AI Context Window: ~10 files at a time
AI's Visibility: 2% of your codebase
The AI is editing with 98% of the codebase invisible. It's like performing surgery blindfolded.
The Real Cost
Without codebase intelligence, AI agents waste tokens and your time:
Typical AI workflow WITHOUT Blindspot:
1. Read file A to understand structure (~2,000 tokens)
2. Read file B to check imports (~1,500 tokens)
3. Read file C to understand relationships (~3,000 tokens)
4. Read file D to check routes (~1,000 tokens)
5. Read file E to check validation (~2,000 tokens)
6. Make the edit (~500 tokens)
7. Realize something broke, read file F (~2,000 tokens)
8. Fix the broken thing (~500 tokens)
─────────────────────────────────────────────
Total: ~12,500 tokens, 8 tool calls, multiple errors
Same task WITH Blindspot:
1. get_context_for_edit("User.php", "is_active") (~800 tokens response)
→ Returns: all relationships, affected controllers,
cache keys, validation rules, template usages,
risk level, and the symbol's source code
2. Make the edit with full awareness (~500 tokens)
─────────────────────────────────────────────
Total: ~1,300 tokens, 2 tool calls, zero errors
That's ~90% fewer tokens and zero broken code.
The Solution
Blindspot is a Model Context Protocol (MCP) server that acts as your AI agent's external brain. It:
- Indexes your entire codebase using tree-sitter (12 languages) into a local SQLite database
- Understands your framework — not just syntax, but relationships, routes, schemas, cache keys, middleware chains
- Provides structured intelligence via MCP tools that any AI agent can call
- Never sends your code anywhere — everything runs locally on your machine
┌─────────────────────────────────────────────────────────┐
│ YOUR AI AGENT │
│ (Claude Code / Cursor / Copilot) │
│ │
│ "I need to change User.is_active" │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ get_context_for_edit("User.php", "is_active") │ │
│ └──────────────────────┬──────────────────────────┘ │
│ │ │
└─────────────────────────┼───────────────────────────────┘
│ MCP Protocol
▼
┌─────────────────────────────────────────────────────────┐
│ BLINDSPOT MCP SERVER │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────────────────┐ │
│ │ Deep │ │ Framework│ │ Symbol │ │
│ │ Index │ │ Plugin │ │ Resolver │ │
│ │ (SQLite) │ │ (Laravel)│ │ (cross-file analysis)│ │
│ └──────────┘ └──────────┘ └──────────────────────┘ │
│ │
│ Response (structured, ~800 tokens): │
│ { │
│ "symbol_code": "public function getIsActiveAttr...", │
│ "class_hierarchy": { "extends": "Model", ... }, │
│ "ripple_effect": { │
│ "risk_level": "high", │
│ "affected_files": 14, │
│ "controllers": ["UserController", "AdminCtrl"], │
│ "cache_keys": ["user_active_count"], │
│ "templates": ["profile.blade.php", ...] │
│ }, │
│ "impact_summary": { "total_affected": 22 } │
│ } │
└─────────────────────────────────────────────────────────┘
The AI now knows everything about the impact of its change — without reading a single file.
Token & Context Savings
Blindspot dramatically reduces token usage by replacing file reads with structured queries:
┌─────────────────────────────────────────────────────────────┐
│ TOKEN USAGE COMPARISON │
│ │
│ Task: "Rename is_active to is_enabled on User model" │
│ │
│ WITHOUT Blindspot: │
│ ████████████████████████████████████████████ ~15,000 tokens│
│ (Read 7+ files to understand dependencies) │
│ │
│ WITH Blindspot: │
│ ██████ ~2,000 tokens │
│ (1 call: get_context_for_edit + rename_symbol) │
│ │
│ Savings: ~87% │
├─────────────────────────────────────────────────────────────┤
│ │
│ Task: "Understand the full project structure" │
│ │
│ WITHOUT Blindspot: │
│ ████████████████████████████████████████████████████████████│
│ ~50,000+ tokens (read dozens of files) │
│ │
│ WITH Blindspot: │
│ ████ ~1,500 tokens │
│ (1 call: get_project_snapshot) │
│ │
│ Savings: ~97% │
├─────────────────────────────────────────────────────────────┤
│ │
│ Task: "Check what breaks if I change this function" │
│ │
│ WITHOUT Blindspot: │
│ ████████████████████████████████ ~10,000 tokens │
│ (Read callers, grep across codebase) │
│ │
│ WITH Blindspot: │
│ ███ ~800 tokens │
│ (1 call: get_ripple_effect) │
│ │
│ Savings: ~92% │
└─────────────────────────────────────────────────────────────┘
What This Means in Practice
| Metric | Without Blindspot | With Blindspot |
|--------|------------------|----------------|
| Tokens per edit task | ~12,000-15,000 | ~1,500-2,500 |
| Files read into context | 5-10 | 0 |
| Tool calls per task | 6-10 | 1-3 |
| Broken code from blind edits | Frequent | Rare |
| Time to understand a new codebase | 30+ minutes of reading | 1 call (get_project_snapshot) |
Why This Matters Even More for Smaller Models
Blindspot isn't just for Claude Opus or GPT-4. It's a game-changer for smaller, cheaper, and faster models that have limited context windows.
┌──────────────────────────────────────────────────────────────────┐
│ CONTEXT WINDOW COMPARISON BY MODEL │
│ │
│ Claude Opus 4 ████████████████████████████████████ 200K │
│ GPT-4o ████████████████████████████ 128K │
│ Claude Sonnet 4 ████████████████████████████████████ 200K │
│ Gemini 2.5 Flash ██████████████████████████████████ 1M │
│ Codex (CLI) ████████████████████████████ 200K │
│ GPT-4o mini ████████████████████████████ 128K │
│ Gemini 2.0 Flash ██████████████████████████████████ 1M │
│ DeepSeek V3 ████████████████ 64K │
│ Llama 4 Scout ██████████████████████████████████████ 10M │
│ Qwen 3 ██████████ 32K │
│ Local LLMs ████████ 8-32K │
│ │
│ Your 500-file project needs: ~250K tokens to read everything │
│ Blindspot gives full awareness for: ~2K tokens per task │
└──────────────────────────────────────────────────────────────────┘
The Problem Scales With Model Size
| Model Category | Context Window | Without Blindspot | With Blindspot | |---------------|---------------|-------------------|----------------| | Large models (Claude Opus, GPT-4o) | 128-200K | Can read ~20 files, still misses 90% of project | Full project awareness in ~2K tokens | | Medium models (Gemini Flash, Codex) | 32-128K | Can read ~5-10 files, misses 95%+ | Full project awareness in ~2K tokens | | Small/fast models (GPT-4o mini, DeepSeek) | 32-64K | Can read ~3-5 files, essentially blind | Full project awareness in ~2K tokens | | Local models (Llama, Qwen, Mistral) | 8-32K | Can barely read 1-2 files | Full project awareness in ~2K tokens |
The smaller the model, the bigger the impact.
A local Llama model with 8K context can't even read your main controller file (often 500+ lines = 2K+ tokens). But with Blindspot, it gets:
- The full project structure (
get_project_snapshot= ~1.5K tokens) - Complete context for any edit (
get_context_for_edit= ~800 tokens) - Symbol-level impact analysis (
get_ripple_effect= ~500 tokens)
Total: ~2.8K tokens — fits easily in 8K context, with room to spare for the actual edit.
This Unlocks New Workflows
Without Blindspot: With Blindspot:
"Use expensive models for "Use cheap/fast models for
everything because only they everything because Blindspot
can hold enough context" provides the context they need"
Claude Opus: $15/M tokens GPT-4o mini: $0.15/M tokens
→ 100x more expensive → Same quality output
→ Slower → 10x faster
→ Still misses things → Full project awareness
Blindspot essentially decouples code intelligence from model intelligence. The model doesn't need to be smart enough to hold your entire codebase in memory — Blindspot does that for it.
Real-World Impact by Model
| Scenario | Model | Without Blindspot | With Blindspot |
|----------|-------|-------------------|----------------|
| Refactor a model field | GPT-4o mini (128K) | Reads 5 files, misses 9 dependencies | Sees all 14 affected files instantly |
| Understand new codebase | DeepSeek V3 (64K) | Reads ~3 files, barely scratches surface | Full project snapshot in 1 call |
| Safe rename across files | Local Llama (8K) | Impossible — can't even fit the analysis | rename_symbol handles it end-to-end |
| Check before editing | Qwen 3 (32K) | Reads 2 files, guesses the rest | get_context_for_edit returns everything |
| Debug production issue | Gemini Flash (1M) | Can read many files but wastes tokens | Targeted get_ripple_effect saves 90% tokens |
Bottom line: Blindspot makes small models behave like large models, and large models behave like they've memorized your entire codebase.
What Makes Blindspot Different?
There are other code intelligence MCP servers. Here's why Blindspot is different:
| Feature | Generic MCP Servers | Blindspot | |---------|-------------------|-----------| | Symbol search | grep-like text search | Structured cross-file references with usage types (import, call, extends, instantiation) | | Impact analysis | "who calls this function?" | "If I change this, which controllers, templates, cache keys, and validations break?" | | Framework awareness | None | Deep understanding of 16 frameworks — routes, ORM, templates, middleware, DI | | Edit safety | None | Syntax check + auto-rollback + anti-pattern detection + ripple effect warnings | | Project overview | File listing | Compact structured snapshot: classes, hotspots, import graph, metrics | | Token efficiency | Read full files | Returns only the data needed, structured and compact |
Supported Frameworks (16)
Blindspot auto-detects your framework and loads only the relevant plugin tools:
| Framework | Language | Tools | What It Understands | Status | |-----------|----------|-------|-------------------|--------| | Laravel | PHP | 13 | Eloquent relationships, Blade templates, routes, migrations, cache maps, validation chains, middleware | Production-tested | | Next.js | TypeScript | 14 | React components, API routes, Prisma schemas, state management (Zustand/Redux), data fetching, middleware | Tested on real projects | | NestJS | TypeScript | 14 | Module graphs, guards, pipes, interceptors, TypeORM/Prisma, DI | Tested on real projects | | Django | Python | 14 | Model relationships, URL maps, template dependencies, DRF serializers, migrations, cache, middleware | Alpha | | Spring Boot | Java/Kotlin | 14 | JPA entities, endpoint maps, Spring Security filters, DI container, cache annotations, Thymeleaf | Alpha | | Express.js | Node.js | 12 | Mongoose/Sequelize/TypeORM models, route maps, middleware chains, validation (Joi/Zod) | Alpha | | Go (Gin/Echo/Chi) | Go | 12 | GORM structs, route maps, interface implementations, middleware, dependency graphs | Alpha | | Rails | Ruby | 13 | ActiveRecord relationships, route maps, ERB/HAML templates, cache, migrations, jobs | Alpha | | FastAPI | Python | 12 | SQLAlchemy + Pydantic models, Alembic migrations, Depends() injection graph, async patterns | Alpha | | Vue/Nuxt 3 | TypeScript | 14 | Vue components, composables, Pinia stores, auto-imports, server routes, middleware | Alpha | | SvelteKit | TypeScript | 13 | File-based routing, Svelte stores, load functions, form actions, hooks | Alpha | | Flutter | Dart | 12 | Widget trees, Riverpod/BLoC state, GoRouter routes, model schemas, assets | Alpha | | ASP.NET Core | C# | 13 | EF Core entities, Razor views, DI container, middleware pipeline, validation | Alpha | | React Native | TypeScript | 12 | React Navigation maps, native modules, platform-specific code, StyleSheet analysis | Alpha | | Rust (Actix/Axum) | Rust | 12 | Structs, trait implementations, error handling chains, middleware layers | Alpha | | Phoenix | Elixir | 13 | Ecto schemas, LiveViews, bounded contexts, plug pipelines, HEEx templates | Alpha |
Status guide:
- Production-tested — Battle-tested on real production codebases for months
- Tested on real projects — Verified on multiple real-world projects with real data
- Alpha — Architecture and parsing logic complete, needs community testing on diverse projects. Your bug reports and PRs will make these production-ready!
No framework detected? The 31 core tools still work perfectly on any project in any language. They use the deep index (tree-sitter) for symbol extraction, cross-file references, class hierarchies, and impact analysis.
Core Tools (31 — Always Available)
These tools work on every project, regardless of language or framework:
Editing & Safety
| Tool | What It Does |
|------|-------------|
| apply_edit | Edit files without reading them into context. 5 modes: search-replace, batch, symbol, line-range, occurrence. Syntax check + auto-rollback on error. |
| smart_apply_edit | Same as apply_edit + automatic ripple effect analysis. Warns you if the change affects other files. |
| apply_edit_multi | Edit multiple files atomically in one call. If any file fails validation, all changes roll back. |
| get_edit_region | Get a specific region of a file with line numbers. Much cheaper than reading the whole file. |
| diff_preview | Preview multi-file edits without applying them. Dry-run mode for large refactoring. |
Intelligence & Analysis
| Tool | What It Does |
|------|-------------|
| get_context_for_edit | The "external brain." Call once before editing. Returns: symbol code, class hierarchy, ripple effect, impact summary — everything you need in one call. |
| get_ripple_effect | Symbol-level impact analysis. "If I change User.is_active, what exactly breaks?" Returns affected files by category with risk level. |
| get_impact_analysis | File-level impact analysis. "If I modify this file, what's affected?" Scans all symbols in the file and finds cross-file references. |
| find_references | Find all files referencing a symbol. Returns structured results with usage types: import, static call, method call, instantiation, extends. |
| get_class_hierarchy | Full inheritance chain: extends, implements, mixins/traits, extended_by, implemented_by. Works with PHP, Python, TypeScript, Java, Go, Rust, Ruby. |
| get_project_snapshot | Compact overview of the entire project (~5KB). Classes, hotspots, import graph, metrics. Use as the first call in every session. |
| get_file_summary | Analyze a file's structure without reading it. Returns: classes, functions, methods, imports, line count. |
| get_symbol_body | Extract a symbol's full definition. Two modes: full (with code) or compact (metadata only, ~90% fewer tokens). |
Code Quality
| Tool | What It Does |
|------|-------------|
| detect_anti_patterns | Scan for anti-patterns using built-in rules + custom rules from .blindspot.yaml. Supports PHP, JS/TS, Python, Go, Rust. |
| auto_anti_pattern_check | Compact post-edit check. Call after apply_edit to verify no rules were violated. |
| rename_symbol | Safe cross-file rename. Word-boundary aware (won't rename partial matches). Dry-run preview + syntax check. |
| analyze_queries | Detect N+1 queries, missing indexes, unbounded queries, queries in loops. |
| check_eager_loading | Audit for N+1 risks in controllers and views. |
| detect_cache_conflicts | Find duplicate cache keys, dead cache, stale risks, pattern conflicts. |
Search & Index
| Tool | What It Does |
|------|-------------|
| search_code_advanced | Full-text code search with pagination. Auto-selects best tool (ripgrep > ag > grep). |
| find_files | Find files matching glob patterns using the in-memory index. |
| build_deep_index | Build the full symbol index (tree-sitter + SQLite). Run once per session. |
| refresh_index | Rebuild file index after git operations or when things seem stale. |
Quick Start
Install
pip install blindspot-mcp
Claude Code
Add to ~/.claude/settings.json:
{
"mcpServers": {
"blindspot": {
"command": "blindspot-mcp",
"args": ["--project-path", "/path/to/your/project"]
}
}
}
Cursor / VS Code
Add to .cursor/mcp.json or .vscode/mcp.json:
{
"mcpServers": {
"blindspot": {
"command": "blindspot-mcp",
"args": ["--project-path", "."]
}
}
}
Framework Override
Framework is auto-detected. Override with:
blindspot-mcp --project-path . --framework nextjs
First Session
Once configured, start your AI agent and it will automatically have access to Blindspot tools. For best results:
- Agent calls
build_deep_index(one-time, indexes all symbols) - Agent calls
get_project_snapshot(understand the project structure) - Before any edit, agent calls
get_context_for_edit(get full awareness) - Agent uses
smart_apply_editinstead of raw file writes (safe editing)
Configuration
Create .blindspot.yaml in your project root:
# Language and framework (auto-detected if omitted)
language: typescript
framework: nextjs
# Custom anti-pattern rules
anti_patterns:
- pattern: "console\\.log\\("
severity: error
message: "Remove console.log before commit"
file_types: [js, ts, tsx]
- pattern: "debugger"
severity: error
message: "Remove debugger statement"
file_types: [js, ts, tsx]
# Custom syntax checkers
syntax_check:
typescript: "npx tsc --noEmit --pretty false {file}"
python: "python -m py_compile {file}"
# Directory mapping for your project structure
scan_dirs:
models: "src/models"
controllers: "src/app/api"
views: "src/app"
services: "src/services"
tests: "__tests__"
# Directories to exclude from scanning
exclude_dirs:
- .next
- node_modules
- coverage
See examples/ for framework-specific configuration templates.
CLI Options
blindspot-mcp [options]
Options:
--project-path PATH Root directory of the project (required)
--framework NAME Override auto-detected framework
--transport TYPE stdio | sse | streamable-http (default: stdio)
--port PORT Port for HTTP transports (default: 8000)
--indexer-path PATH Custom path for storing index data
--tool-prefix PREFIX Prefix for all tool names
How It Works
┌──────────────────────────────────────────────────────────┐
│ INDEXING PHASE │
│ │
│ Source Files ──→ Tree-sitter Parser ──→ SQLite Index │
│ (.py .ts .go (12 languages) (symbols, lines, │
│ .java .php imports, types) │
│ .rs .rb .dart │
│ .cs .ex) │
│ │
│ Framework Files ──→ Regex Parser ──→ Framework Metadata │
│ (routes, models, (per plugin) (relationships, │
│ templates, config) routes, schemas) │
└──────────────────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ QUERY PHASE │
│ │
│ AI Agent calls MCP tool │
│ │ │
│ ▼ │
│ Symbol Resolver ──→ Cross-file analysis ──→ Structured │
│ (deep index + (references, hierarchy, response │
│ file scanning) impact, ripple) (~800 tok) │
│ │
│ Framework Plugin ──→ Domain-specific ──→ Rich context │
│ (only if detected) analysis (routes, │
│ (routes, ORM, schemas, │
│ templates) middleware) │
└──────────────────────────────────────────────────────────┘
Key design decisions:
- Local only — Your code never leaves your machine. Everything runs in-process.
- Framework-aware loading — Only the detected framework's plugin loads. A Next.js project gets ~45 tools, not 237.
- Deep index + file scanning — Tree-sitter for structured symbol data, regex for cross-file references. Best of both worlds.
- Compact responses — Every tool is designed to return the minimum data needed. Large diffs get summarized. Symbol bodies can be fetched in compact mode (~90% fewer tokens).
Writing a Plugin
Adding support for a new framework requires two files:
1. Intelligence Service (blindspot/services/myframework_intelligence_service.py)
from .base_service import BaseService
from typing import Any, Dict, Optional
class MyFrameworkIntelligenceService(BaseService):
"""Intelligence service for MyFramework."""
def get_model_schema(self, model_name: str = None) -> Dict[str, Any]:
base = self.base_path
# Parse your framework's model files with regex
# Return structured data
return {"status": "success", "models": [...]}
def get_route_map(self, filter_prefix: str = None) -> Dict[str, Any]:
# Parse your framework's route definitions
return {"status": "success", "routes": [...]}
# Add 10-12 more methods following the pattern of existing plugins
2. Plugin Registration (blindspot/plugins/myframework/__init__.py)
from ..base_plugin import BlindspotPlugin
from mcp.server.fastmcp import Context, FastMCP
class MyFrameworkPlugin(BlindspotPlugin):
@property
def name(self) -> str:
return "myframework"
@property
def framework(self) -> str:
return "myframework"
def register_tools(self, mcp: FastMCP) -> None:
from ...services.myframework_intelligence_service import MyFrameworkIntelligenceService
from ...utils import handle_mcp_tool_errors
from ...server import with_concurrency_limit
@mcp.tool()
@handle_mcp_tool_errors(return_type="dict")
@with_concurrency_limit
def get_myframework_models(ctx: Context, model_name: str = None):
"""Get model schema for MyFramework projects."""
return MyFrameworkIntelligenceService(ctx).get_model_schema(model_name)
Then add it to blindspot/plugins/__init__.py in load_builtin_plugins().
Look at blindspot/plugins/laravel/__init__.py and blindspot/services/laravel_intelligence_service.py for the most complete reference implementation.
Contributing
This project was born from a real need — I built it for my own Laravel project and it transformed how AI agents work with my code. Now it's open source because I believe every developer deserves this.
The Laravel plugin is battle-tested. The other 15 framework plugins are architecturally complete with real parsing logic, but they need real-world testing across diverse projects. This is where the community comes in.
How You Can Help
- Use it on your project and report issues. The most valuable contribution is real-world testing.
- Fix edge cases in framework plugins. Regex parsers don't cover every syntax variation — your PR fixing a parsing edge case helps everyone.
- Add new framework plugins. The plugin architecture makes it straightforward.
- Improve documentation. Better examples, tutorials, framework-specific guides.
Development Setup
git clone https://github.com/umuterdal/blindspot-mcp.git
cd blindspot-mcp
python -m venv .venv
source .venv/bin/activate
pip install -e "."
Running
# Test with MCP Inspector (browser UI)
npx @modelcontextprotocol/inspector .venv/bin/blindspot-mcp --project-path /path/to/project
# Or add to your AI agent config and use directly
Compatible AI Tools & Models
Blindspot works with any AI tool that supports the Model Context Protocol (MCP):
AI Coding Tools
| Tool | Support | Configuration |
|------|---------|--------------|
| Claude Code (CLI) | Full support | ~/.claude/settings.json |
| Cursor | Full support | .cursor/mcp.json |
| VS Code + Copilot | Full support | .vscode/mcp.json |
| Windsurf | Full support | MCP config |
| Cline | Full support | MCP config |
| Continue.dev | Full support | MCP config |
| Any MCP client | Full support | stdio or SSE transport |
AI Models (All Work With Blindspot)
| Model | Provider | Context | Blindspot Benefit | |-------|----------|---------|------------------| | Claude Opus 4 / Sonnet 4 | Anthropic | 200K | Saves 87-97% tokens, prevents blind edits | | GPT-4o / GPT-4o mini | OpenAI | 128K | Critical for mini — can't hold large projects without Blindspot | | Gemini 2.5 Pro / Flash | Google | 1M / 1M | Even with huge context, saves tokens and provides structured data vs raw file reads | | Codex (OpenAI CLI) | OpenAI | 200K | Structured intelligence beats raw file reads | | DeepSeek V3 / R1 | DeepSeek | 64K | Essential — 64K can't hold most projects; Blindspot makes it viable | | Llama 4 Scout/Maverick | Meta | 10M/1M | Structured context beats brute-force file reading | | Qwen 3 | Alibaba | 32K | Essential — too small for most projects without Blindspot | | Mistral Large / Codestral | Mistral | 32-128K | Significant benefit, especially for Codestral | | Local models (Ollama) | Self-hosted | 4-32K | Game-changer — makes local models usable for real projects |
Key insight: The smaller your model's context window, the more you need Blindspot. But even the largest models benefit because structured data beats raw file reads for accuracy and speed.
Monorepo Support
Blindspot automatically detects monorepo structures and loads plugins for all detected frameworks:
your-project/
├── frontend/ → Next.js detected → Next.js plugin loaded
├── backend/ → NestJS detected → NestJS plugin loaded
├── mobile/ → React Native detected → RN plugin loaded
└── package.json → workspaces: ["frontend", "backend", "mobile"]
Just point Blindspot at the root — it finds everything:
{
"mcpServers": {
"blindspot": {
"command": "blindspot-mcp",
"args": ["--project-path", "/path/to/monorepo-root"]
}
}
}
Supported monorepo tools: npm workspaces, yarn workspaces, pnpm workspaces, Turborepo, Nx, Lerna.
Project Stats
Total Python files: 112
Total lines of code: 62,500+
Framework plugins: 16
Total MCP tools: 237 (31 core + ~13 per plugin)
Supported languages: 12 (PHP, TypeScript, JavaScript, Python, Java,
Kotlin, Go, Rust, Ruby, C#, Dart, Elixir)
Tree-sitter parsers: 12
Syntax check support: PHP, JavaScript, TypeScript, Python, Go, Rust
Monorepo support: Automatic workspace detection
License
MIT License. See LICENSE for details.
Built with frustration, then with love. If this saves you even one hour of debugging AI-generated code, it was worth it.