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ContextTree MCP

by chelslava

ContextTree MCP 🌳

Глубокий семантический поиск по коду для ИИ-ассистентов — на основе AST-парсинга и локальных эмбеддингов. Полностью офлайн.

Python License: MIT MCP README (RU)

ContextTree MCP — это локальный сервер Model Context Protocol, который даёт вашему ИИ-ассистенту для написания кода структурное понимание кодовой базы. Он объединяет два мира:

  • tree-sitter разбирает исходные файлы в AST и извлекает логические блоки — функции, методы, сигнатуры классов с docstring.

  • sentence-transformers (all-MiniLM-L6-v2) превращает каждый блок — дополненный путём к файлу, именем класса, именем метода и docstring — в вектор внутри локального векторного хранилища ChromaDB.

В итоге ваш ассистент находит код по смыслу, а не по ключевым словам, и каждый результат поиска содержит точный путь к файлу и номера строк.

🔒 Приватность прежде всего. Всё выполняется на вашей машине: разбор, модель эмбедов, векторный индекс. Никаких облачных вызовов, никакой телеметрии, код никогда не покидает диск, на котором лежит.


Почему не простой текстовый поиск?

grep и полнотекстовый поиск сопоставляют строки. Они спотыкаются ровно там, где разработчикам нужнее всего помощь:

Задача

Текстовый поиск

ContextTree MCP

«Где у нас проверка JWT-токенов?»

❌ нужно угадывать точные ключевые слова

✅ находит AuthService.verify_token(), даже если слово «validate» не встречается в коде

Найти метод, имя которого изменили при рефакторинге

❌ переименование ломает поиск

✅ docstring + окружающий контекст по-прежнему несут смысл

Отличить определение от использования

❌ невозможно без регулярных выражений

✅ разделение объявлений и мест вызова на уровне AST

Точные координаты результата

⚠️ только строка совпадения

✅ метаданные file, class, method, start_line, end_line

Игнорировать шум комментариев / строк / импортов

❌

✅ индексируются только реальные логические единицы

Related MCP server: Paparats MCP

Возможности

  • 🌲 AST-сегментация — индексирует функции, методы и сигнатуры классов через tree-sitter, а не произвольные текстовые окна.

  • 🧩 Контекстно-обогащённые логические блоки — каждый индексируемый документ включает свой путь к файлу, владельца-класс, имя метода, docstring и тело, так что запросы вроде «логика повтора платежей» попадают точно в нужный метод.

  • ⚡ Инкрементальная индексация — SHA-256-хэши содержимого на файл; повторно обрабатываются только изменённые/новые/удалённые файлы.

  • 🔍 Семантический поиск — запрос на естественном языке → ранжированные фрагменты кода с точными диапазонами строк.

  • 📞 Поиск использований на основе AST — находит реальные места вызовов любого символа (функции или класса), отсеивая ложные срабатывания вроде строковых литералов или комментариев.

  • 🗄️ Постоянный локальный индекс — ChromaDB хранится в .chroma/, переживает перезапуски и никогда не попадает в Git.

  • 🔌 Транспорт MCP stdio — подключается к Claude Desktop, OpenCode, Cursor, Cline или любому MCP-совместимому клиенту.

Технологический стек

Слой

Технология

Язык

Python 3.12+

Протокол

Официальный SDK mcp (транспорт stdio)

Разбор AST

tree-sitter + биндинги для Python, TypeScript, JavaScript

Векторная БД

chromadb (локальный персистентный режим)

Эмбеддинги

sentence-transformers · all-MiniLM-L6-v2

Установка

Требуется Python 3.12+. Рекомендуется uv как быстрыйкий современный пакетный менеджер:

git clone https://github.com/<your-org>/mcp-context-tree.git
cd mcp-context-tree

# Option A — uv (recommended): resolves dependencies from pyproject.toml, locks uv.lock
uv sync

# Option B — classic pip + venv
python -m venv venv && source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt

💡 При первом запуске модель эмбеддингов (~90 МБ). PyTorch устанавливается как зависимость sentence-transformers; достаточно CPU-сборки — GPU не требуется.

Регистрация в MCP-клиенте

Примерконфигурации (Claude Desktop / любой клиент с поддержкой stdio-серверов):

{
  "mcpServers": {
    "context-tree": {
      "command": "<path-to-venv>/bin/python",
      "args": ["-m", "context_tree"],
      "env": {}
    }
  }
}

(В Windows используйте <path-to-venv>\Scripts\python.exe.)

Инструменты, доступные ассистенту

Инструмент

Сигнатура

Описание

index_workspace

(directory_path: str)

Обходит проект, обнаруживает изменённые файлы по хэшу, инкрементально обновляет коллекцию ChromaDB.

semantic_search

(query: str, limit: int = 5)

Поиск по индексированному коду на естественном языке. Возвращает обогащённые сниппеты с file, class, start_line, end_line.

find_ast_usages

(symbol_name: str)

AST-поиск реальных мест вызова / инстанцирования функции или класса.

Типичный рабочий процесс:

1. index_workspace("D:/projects/my-app")
2. semantic_search("where do we handle payment retries", limit=8)
3. find_ast_usages("PaymentGateway.retry")

Поддерживаемые языки

Язык

Статус

Python

✅ поддерживается из коробки

TypeScript / TSX

✅ поддерживается из коробки

JavaScript / JSX

✅ поддерживается из коробки

Go, Java, Rust…

🗺️ дорожная карта — дизайн реестра парсеров рассчитан на расширение

Документация

  • 🏛️ ARCHITECTURE.md — структура проекта, схема данных ChromaDB, логика AST-извлечения, дизайн инкрементальной индексации.

  • 🇷🇺 README.ru.md — документация на русском языке.

Дорожная карта

  • Режим наблюдения — автоматическая переиндексация при изменении файлов (fs-watcher)

  • Больше языков через tree-sitter-биндинги

  • Гибридный поиск (BM25 + переранжирование по векторам)

  • Ранжирование с учётом графа вызово » («кто это вызывает?»)

  • Релиз на PyPI (context-tree-mcp)

Участие в разработке

Приветствуются issue и пулл-реквесты. Пожалуйста, поддерживайте согласованность с ARCHITECTURE.md.

Лицензия

MIT — см. LICENSE.

Available Tools

4 tools
find_ast_usagesA

AST-based lookup of real call sites / instantiations of a function or class. Filters out string literals, comments, and non-call occurrences.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNo
symbol_nameYes
directory_pathNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations supplied, the description carries the full behavioral burden. It does disclose a valuable filtering behavior: "Filters out string literals, comments, and non-call occurrences." However, it does not mention whether indexing is required, how missing symbols are handled, or any result behavior beyond being AST-based.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single focused sentence that communicates the mechanism, target resource, and exclusion behavior without redundancy. Every clause adds useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple enough that the core lookup behavior is adequately described, and an output schema exists. The main gaps are the lack of usage guidance around semantic_search/index_workspace and the absence of practical details about scoping and limit behavior, which leaves the description merely adequate rather than complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description should compensate. It adds meaning for symbol_name by stating it refers to "a function or class," but it gives no guidance on directory_path or limit, such as how the directory is searched or what the limit controls. This leaves non-obvious semantics undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb plus precise resource: "AST-based lookup of real call sites / instantiations of a function or class." It also clarifies what is excluded, which distinguishes it from string or semantic search. This is far more informative than the bare tool name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The AST-based wording implicitly indicates this is for exact code-structure matching rather than fuzzy search, and the sibling tools suggest a semantic-search alternative. However, the description does not explicitly state when to use this tool versus semantic_search or index_workspace, nor does it mention any exclusions or preconditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

go_to_definitionA

Finds the exact definition/declaration location of a symbol (function, method, class, struct, trait, interface) across workspace files.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNo
symbol_nameYes
directory_pathNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.5/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds meaningful context by stating the search is workspace-wide and targets exact definition/declaration locations. Still, it leaves important behavioral details undisclosed, such as whether an index must exist first, how missing or ambiguous symbols are handled, and whether the operation is purely read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler or redundant restatement of the tool name. It conveys the core action, target, and scope efficiently while listing relevant symbol categories without over-elaborating.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Although an output schema exists and the basic action is clear, the description lacks usage guidance relative to its three sibling tools and fails to explain two of three parameters. With no annotations, this is too thin to fully support an agent in choosing and invoking the tool correctly across realistic scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for bare parameter schemas. It partially clarifies symbol_name by listing supported symbol types, but it says nothing about the format of symbol_name (e.g., fully qualified vs simple name, case sensitivity). The limit and directory_path parameters are completely unaddressed in the description, leaving their semantics to inference from the schema titles and defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Finds') with a precise resource: the exact definition/declaration location of symbols, listing supported symbol categories and clarifying the search scope ('across workspace files'). This clearly distinguishes it from sibling tools like find_ast_usages or semantic_search, which target usages or semantically similar matches rather than exact definitions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrasing 'exact definition/declaration location' implies the tool is appropriate when precise definitions are needed, rather than usages or semantic matches. However, it does not explicitly state when to prefer this tool over semantic_search or find_ast_usages, nor does it mention any exclusions or prerequisites such as requiring an indexed workspace.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

index_workspaceA

Walks the project, detects changed files by SHA-256 hash, and incrementally updates the persistent local ChromaDB vector store.

ParametersJSON Schema
NameRequiredDescriptionDefault
directory_pathNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.9/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It covers that the operation walks the project, uses SHA-256 hashing for change detection, and mutates a persistent local ChromaDB store. This is meaningful behavioral context beyond 'index workspace' though it does not mention potential costs, permissions, or failure modes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with no filler. It front-loads the core action and packs in the hashing, incrementality, and storage target without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one optional parameter and an output schema, the description covers the essential behavior: what is walked, how changes are detected, and what is updated. It is reasonably complete, though it could add an explicit note about running after source changes or before semantic_search.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is only one parameter and schema description coverage is 0%, so the description needs to clarify the parameter's meaning. It never mentions directory_path or the default of '.', forcing the agent to rely on the schema's 'Directory Path' title and default value. The description does not compensate for the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action sequence ('Walks the project', 'detects changed files', 'updates the vector store') and a concrete resource ('persistent local ChromaDB vector store'). It is clearly distinct from the sibling tools semantic_search and find_ast_usages, which do search and AST lookup rather than indexing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'incrementally updates' and 'detects changed files' implies the tool is meant to keep the index fresh after edits, but the description never explicitly says when to run it relative to semantic_search or find_ast_usages. It also does not mention exclusions or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv0.4.0
    • Addedgo_to_definition
    • Changedsemantic_search2 fields changed
      • addedInput schema / properties / mode
        Added value: +{
        +  "default": "hybrid",
        +  "enum": [
        +    "hybrid",
        +    "semantic",
        +    "keyword"
        +  ],
        +  "title": "Mode",
        +  "type": "string"
        +}
      • addedInput schema / properties / rerank
        Added value: +{
        +  "default": false,
        +  "title": "Rerank",
        +  "type": "boolean"
        +}
  2. 3 tool updatesv0.1.0
    • First observedfind_ast_usages
    • First observedindex_workspace
    • First observedsemantic_search

TDQS

A3.9/5.0

Scored across 4 tools

Disambiguation5/5

Each tool targets a distinct part of code navigation: semantic search vs. exact definitions vs. AST usages vs. index maintenance. There is no realistic confusion between their purposes.

Naming Consistency4/5

Three of four tools use imperative verb-first names (find_ast_usages, index_workspace, go_to_definition), and all are snake_case. semantic_search breaks the pattern slightly by starting with an adjective instead of a verb, but the naming remains readable and predictable.

Tool Count5/5

With 4 tools, the server is well-scoped and each tool earns its place in the code indexing/search/navigation workflow. There is no redundancy or bloat.

Completeness4/5

The core lifecycle is covered: index the workspace, search semantically, go to definitions, and find AST usages. Minor gaps such as no explicit index status or reset operation prevent a perfect score, but agents can work around them.

Maintenance

ActivitySlowing
ResponsivenessResponsive

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