Семантический поиск по коду на базе ИИ в виде MCP-инструмента — на основе реверс-инжиниринга протокола SWE-grep от Windsurf.
Любой MCP-совместимый клиент (Claude Code, Claude Desktop, Cursor и т.д.) может использовать его для поиска по кодовым базам с помощью запросов на естественном языке. Все инструменты поставляются через npm — системные зависимости не требуются (ripgrep через @vscode/ripgrep, tree через tree-node-cli). Работает на macOS, Windows и Linux.
You: "where is the authentication logic?"
│
▼
┌─────────────────────────┐
│ Fast Context MCP │
│ (local MCP server) │
│ │
│ 1. Maps project → /codebase
│ 2. Sends query to Windsurf Devstral API
│ 3. AI generates rg/readfile/tree commands
│ 4. Executes commands locally (built-in rg)
│ 5. Returns results to AI
│ 6. Repeats for N rounds
│ 7. Returns file paths + line ranges
│ + suggested search keywords
└─────────────────────────┘
│
▼
Found 3 relevant files.
[1/3] /project/src/auth/handler.py (L10-60)
[2/3] /project/src/middleware/jwt.py (L1-40)
[3/3] /project/src/models/user.py (L20-80)
Suggested search keywords:
authenticate, jwt.*verify, session.*tokenRelated MCP server: code-rag
Node.js >= 18
Учётная запись Windsurf — бесплатного тарифа достаточно (нужен для API-ключа)
Устанавливать ripgrep не нужно — он поставляется вместе с @vscode/ripgrep.
# Latest stable release
npm install @sammysnake/fast-context-mcp
# Or beta/next release
npm install @sammysnake/fast-context-mcp@nextgit clone https://github.com/SammySnake-d/fast-context-mcp.git
cd fast-context-mcp
npm installСервер автоматически извлекает API-ключ из Devin CLI/Desktop или из устаревшей установки Windsurf. Также вы можете использовать MCP-инструмент extract_windsurf_key после настройки или задать WINDSURF_API_KEY вручную.
Учётные данные Desktop обнаруживаются в следующем порядке: Devin, устаревший Deviv, затем Windsurf.
Платформа | Путь |
macOS |
|
Windows |
|
Linux |
|
В WSL/Linux сервер сначала проверяет учётные данные Devin CLI в ~/.local/share/devin/credentials.toml. Если ключ, извлечённый в Windows, возвращает 403 внутри WSL, выполните devin login внутри WSL и повторите попытку.
Добавьте в ~/.claude.json в раздел mcpServers:
{
"fast-context": {
"command": "npx",
"args": ["-y", "--prefer-online", "@sammysnake/fast-context-mcp"],
"env": {
"WINDSURF_API_KEY": "sk-ws-01-xxxxx"
}
}
}Для бета/следующего релиза:
{
"fast-context": {
"command": "npx",
"args": ["-y", "--prefer-online", "@sammysnake/fast-context-mcp@next"],
"env": {
"WINDSURF_API_KEY": "sk-ws-01-xxxxx"
}
}
}Добавьте в claude_desktop_config.json в раздел mcpServers:
{
"fast-context": {
"command": "npx",
"args": ["-y", "--prefer-online", "@sammysnake/fast-context-mcp"],
"env": {
"WINDSURF_API_KEY": "sk-ws-01-xxxxx"
}
}
}Для бета/следующего релиза:
{
"fast-context": {
"command": "npx",
"args": ["-y", "--prefer-online", "@sammysnake/fast-context-mcp@next"],
"env": {
"WINDSURF_API_KEY": "sk-ws-01-xxxxx"
}
}
}Если
WINDSURF_API_KEYне задан, сервер автоматически определит его из вашей локальной установки Windsurf.
Переменная | По умолчанию | Описание |
| (автоопределение) | API-ключ Windsurf |
|
| Раундов поиска на запрос (больше = глубже, но медленнее) |
|
| Макс. параллельных команд за раунд |
|
| Connect-Timeout-Ms для потоковых запросов |
|
| Скрывает |
|
| Макс. строк в выводе команды (усечение) |
|
| Макс. символов в строке вывода (усечение) |
| (не задано) | Отключает кэш результатов в памяти с помощью |
|
| TTL кэша результатов; |
|
| Максимальное количество записей в кэше в памяти |
| (не задано) | Устанавливайте |
|
| Название модели Windsurf |
|
| Версия приложения Windsurf (метаданные протокола) |
|
| Версия языкового сервера Windsurf (метаданные протокола) |
Модель можно изменить, задав WS_MODEL (см. переменные окружения выше).
По умолчанию: MODEL_SWE_1_6_FAST — максимальная скорость, самые богатые ключевые слова grep, наилучшая гранулярность местоположений.
fast_context_searchСемантический поиск по коду на базе ИИ с настраиваемыми параметрами.
Параметр | Тип | Обязательный | По умолчанию | Описание |
| string | Да | — | Поисковый запрос на естественном языке |
| string | Нет | cwd | Абсолютный путь к корню проекта |
| integer | Нет |
| Глубина дерева каталогов для карты репозитория (1-6). Больше = больше контекста, но больше полезная нагрузка. Автоматически откатывается на меньшую глубину, если дерево превышает 250 КБ. Используйте 1-2 для огромных монорепозиториев (>5000 файлов), 3 для большинства проектов, 4-6 для небольших проектов. |
| integer | Нет |
| Раундов поиска (1-5). Больше = глубже поиск, но медленнее. Используйте 1-2 для простых запросов, 3 для большинства запросов, 4-5 для сложного анализа. |
| integer | Нет |
| Максимальное количество файлов для возврата (1-30). Меньше = более сфокусированно, больше = более широкое исследование. |
| string[] | Нет |
| Шаблоны каталогов/файлов, исключённые из карты репозитория и контекста поиска. |
Возвращает:
Релевантные файлы с диапазонами строк
Предлагаемые ключевые слова для поиска (паттерны rg, использованные при ИИ-поиске)
Диагностические метаданные (строка [config], показывающая фактическую использованную tree_depth, размер дерева и произошёл ли откат)
Пример вывода:
Found 3 relevant files.
[1/3] /project/src/auth/handler.py (L10-60, L120-180)
[2/3] /project/src/middleware/jwt.py (L1-40)
[3/3] /project/src/models/user.py (L20-80)
grep keywords: authenticate, jwt.*verify, session.*token
[config] tree_depth=3, tree_size=12.5KB, max_turns=3Вывод ошибок содержит подсказки, зависящие от статуса:
Error: Request failed: HTTP 403
[hint] 403 Forbidden: Authentication failed. The API key may be expired or revoked.
Try re-extracting with extract_windsurf_key, or set a fresh WINDSURF_API_KEY env var.
If you are running inside WSL, run `devin login` inside WSL so `~/.local/share/devin/credentials.toml` exists.Error: Request failed: HTTP 413
[diagnostic] tree_depth_used=3, tree_size=280.0KB (auto fell back from requested depth)
[hint] If the error is payload-related, try a lower tree_depth value.extract_windsurf_keyИзвлекает API-ключ Windsurf из локальной установки. Без параметров.
Установите FC_HIDE_EXTRACT_WINDSURF_KEY_TOOL=1 при запуске MCP-сервера, чтобы скрыть этот инструмент из tools/list. Это не отключает внутреннее автоматическое определение API-ключа для fast_context_search.
fast-context-mcp/
├── package.json
├── src/
│ ├── server.mjs # MCP server entry point
│ ├── core.mjs # Auth, message building, streaming, search loop
│ ├── executor.mjs # Tool executor: rg, readfile, tree, ls, glob
│ ├── extract-key.mjs # Windsurf API Key extraction (SQLite)
│ ├── path-safety.mjs # Project-root confinement for model-selected paths
│ ├── response-repair.mjs # Malformed response repair and evidence salvage
│ ├── shared.mjs # Repository map, answer parser, prompt builder
│ ├── cache.mjs # In-memory search-result cache
│ └── protobuf.mjs # Protobuf encoder/decoder + Connect-RPC frames
├── test/ # Unit and MCP stdio integration tests
├── README.md
└── LICENSEКаталог проекта сопоставляется с виртуальным путём /codebase
Дерево каталогов генерируется на запрошенной глубине (по умолчанию L=3), с автоматическим откатом на меньшую глубину, если дерево превышает 250 КБ
Запрос + дерево каталогов отправляются модели Devstral от Windsurf через Connect-RPC/Protobuf
Devstral генерирует команды инструментов (ripgrep, чтение файлов, tree, ls, glob)
Команды выполняются локально параллельно (до FC_MAX_COMMANDS за раунд)
Результаты отправляются обратно Devstral для следующего раунда
После max_turns раундов Devstral возвращает пути к файлам + диапазоны строк
Все паттерны rg, использованные во время поиска, собираются как предлагаемые ключевые слова
Добавляются диагностические метаданные, чтобы помочь вызывающему ИИ настроить параметры
Протокол: Connect-RPC через HTTP/1.1, кодирование Protobuf, сжатие gzip
Модель: Devstral (MODEL_SWE_1_6_FAST, настраивается)
Локальные инструменты: rg (поставляется через @vscode/ripgrep), readfile (Node.js fs), tree (tree-node-cli), ls (Node.js fs), glob (Node.js fs)
Аутентификация: API-ключ → JWT (автоматически получается для каждой сессии)
Среда выполнения: Node.js >= 18 (ESM)
Пакет | Назначение |
| Каркас MCP-сервера |
| Встроенный бинарный файл ripgrep (кроссплатформенный) |
| Кроссплатформенное дерево каталогов (заменяет системный |
| Чтение локальной SQLite-базы Devin/Windsurf без нативного этапа сборки |
| Проверка схемы; позволяет избежать неполного tar-архива |
MIT
Extract Windsurf API Key from local installation. Auto-detects OS (macOS/Windows/Linux) and reads the API key from Windsurf's local database. Set the result as WINDSURF_API_KEY env var.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses OS auto-detection, reading from the local database, and setting the result as an env var. It does not mention error cases or prerequisites, but it is transparent about its main side effect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary action, and every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema, the description covers the core function and side effect. It lacks mention of failure cases or requirements, but is otherwise complete for its simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and 100% schema coverage (empty), so the baseline is 4. No additional parameter explanation is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Extract Windsurf API Key from local installation', a specific verb+resource with source. It distinguishes itself from the unrelated sibling fast_context_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (to get the API key and set it as an env var), and the sibling tool is unrelated, so no exclusions are needed. It does not explicitly list alternatives, but usage is apparent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AI-driven semantic code search using Windsurf's Devstral model. Searches a codebase with natural language and returns relevant file paths with line ranges, plus suggested grep keywords for follow-up searches. Parameter tuning guide:
tree_depth: Controls how much directory structure the remote AI sees before searching. If you get a payload/size error, REDUCE this value. If search results are too shallow (missing files in deep subdirectories), INCREASE this value.
max_turns: Controls how many search-execute-feedback rounds the remote AI gets. If results are incomplete or the AI didn't find enough files, INCREASE this value. If you want a quick rough answer, use 1. Response includes a [config] line showing actual parameters used — use this to decide adjustments on retry.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language search query (e.g. "where is auth handled", "database connection pool") | |
| project_path | No | Absolute path to project root. Empty = current working directory. | |
| tree_depth | No | Directory tree depth for the initial repo map sent to the remote AI. Default 3. Use 1-2 for huge monorepos (>5000 files) or if you get payload size errors. Use 4-6 for small projects (<200 files) where you want the AI to see deeper structure. Auto falls back to a lower depth if tree output exceeds 250KB. | |
| max_turns | No | Number of search rounds. Each round: remote AI generates search commands → local execution → results sent back. Default 3. Use 1 for quick simple lookups. Use 4-5 for complex queries requiring deep tracing across many files. More rounds = better results but slower and uses more API quota. | |
| max_results | No | Maximum number of files to return. Default 10. Use a smaller value (3-5) for focused queries. Use a larger value (15-30) for broad exploration queries. | |
| exclude_paths | No | Directory/file patterns to exclude from tree and search context. Useful for reducing payload size on large repos. Examples: ['node_modules', 'dist', '.git', 'build', 'coverage', '*.min.*'] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully explains the search process: rounds of search-execute-feedback, auto fallback for tree depth if payload exceeds 250KB, and mentions API quota usage for more rounds. This provides comprehensive behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a main overview and a parameter tuning guide. It is concise overall but the tuning guide could be slightly more compact. Still, it is clear and front-loaded with key purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters, 100% schema coverage, no output schema, and only one sibling, the description covers purpose, behavior, tuning, and response content (including [config] line). It provides sufficient context for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds valuable tuning context for tree_depth and max_turns beyond the schema, explaining payload/size errors and result completeness. For other parameters, the schema already suffices. Thus, a 4 is warranted.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs AI-driven semantic code search, returns file paths with line ranges and grep keywords. It distinguishes itself from the only sibling, extract_windsurf_key, which extracts a key.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a parameter tuning guide that explains when to adjust tree_depth and max_turns based on errors or shallow results. It also advises using the [config] line in responses for retry decisions. However, it does not explicitly state when not to use this tool, but given only one dissimilar sibling, this is acceptable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.2.1fast_context_search4 fields changedInput schema / properties / exclude_pathsAdded value: +{
+ "default": [],
+ "description": "Directory/file patterns to exclude from tree and search context. Useful for reducing payload size on large repos. Examples: ['node_modules', 'dist', '.git', 'build', 'coverage', '*.min.*']",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}Input schema / properties / max_resultsAdded value: +{
+ "default": 10,
+ "description": "Maximum number of files to return. Default 10. Use a smaller value (3-5) for focused queries. Use a larger value (15-30) for broad exploration queries.",
+ "maximum": 30,
+ "minimum": 1,
+ "type": "integer"
+}Input schema / properties / max_turnsAdded value: +{
+ "default": 3,
+ "description": "Number of search rounds. Each round: remote AI generates search commands → local execution → results sent back. Default 3. Use 1 for quick simple lookups. Use 4-5 for complex queries requiring deep tracing across many files. More rounds = better results but slower and uses more API quota.",
+ "maximum": 5,
+ "minimum": 1,
+ "type": "integer"
+}Input schema / properties / tree_depthAdded value: +{
+ "default": 3,
+ "description": "Directory tree depth for the initial repo map sent to the remote AI. Default 3. Use 1-2 for huge monorepos (>5000 files) or if you get payload size errors. Use 4-6 for small projects (<200 files) where you want the AI to see deeper structure. Auto falls back to a lower depth if tree output exceeds 250KB.",
+ "maximum": 6,
+ "minimum": 1,
+ "type": "integer"
+}v1.0.0extract_windsurf_keyfast_context_searchScored across 2 tools
The two tools have completely distinct purposes: one extracts an API key from a local installation, the other performs semantic code search. No overlap or ambiguity exists.
Both tools use snake_case and follow a descriptive pattern (extract_* and fast_*). Although the verb style differs slightly (command vs. brand adjective), the naming is clear and consistent in format.
With only two tools, the server feels minimal. While the tools are focused, the count is on the low end of what is typically expected, making it borderline appropriate.
The server covers API key extraction and semantic search, but lacks related operations like API key management, file content retrieval, or result navigation. Significant gaps exist for a context-focused server.
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