ContextTree MCP
ContextTree MCP 🌳
AI 어시스턴트를 위한 심층 의미론적 코드 검색 — AST 파싱과 로컬 임베딩 기반. 100% 오프라인.
ContextTree MCP는 코드베이스에 대한 구조적 이해를 AI 코딩 어시스턴트에 제공하는 로컬 Model Context Protocol 서버입니다. 두 가지 영역을 결합합니다:
tree-sitter는 소스 파일을 AST로 파싱하여 논리적 블록 — 함수, 메서드, docstring이 포함된 클래스 시그니처를 추출합니다.
sentence-transformers (
all-MiniLM-L6-v2)는 파일 경로, 클래스 이름, 메서드 이름, docstring으로 강화된 각 블록을 로컬 ChromaDB 벡터 저장소에 임베딩합니다.
그 결과: 어시스턴트는 키워드가 아닌 의미로 코드를 찾고, 모든 검색 적중 결과는 정확한 파일 경로와 줄 번호와 함께 반환됩니다.
🔒 개인정보 보호 우선. 파싱, 임베딩 모델, 벡터 인덱스까지 모든 것이 사용자 머신에서 실행됩니다. 클라우드 호출도, 텔레메트리도 없으며, 코드가 저장된 디스크를 떠나는 일은 결코 없습니다.
일반 텍스트 검색은 왜 부족한가?
grep과 전문 검색은 문자열을 매칭합니다. 그리고 개발자의 도움이 가장 필요한 바로 그 지점에서 실패합니다:
작업 | 텍스트 검색 | ContextTree MCP |
"JWT 토큰을 어디서 검증하지?" | ❌ 정확한 키워드 추측이 필요 | ✅ 코드에 "validate"가 전혀 없어도 |
이름이 리팩터링된 메서드 찾기 | ❌ 이름 변경으로 중단 | ✅ docstring + 주변 맥락이 여전히 의미를 담고 있음 |
사용처 vs 정의 정의 구별 | ❌ 정규식 억지 없이는 불가능 | ✅ AST 수준에서 선언과 호출 사이트를 분리 |
정확한 위치 반환 | ⚠️ 매칭된 줄만 | ✅ |
주석 / 문자열 / import 노이즈 무시 | ❌ | ✅ 실제 논리 단위만 인덱싱 |
Related MCP server: Paparats MCP
기능
🌲 AST 인식 청킹 — 임의의 텍스트 창이 아닌 주제, 메서드, 클래스 시그니처를 tree-sitter를 통해 인덱싱합니다.
🧩 맥락 강화 논리적 블록 — 인덱싱된 모든 문서는 파일 경로, 소속 클래스, 메서드 이름, docstring, 본문을 포함하므로 "결제 재시도 로직" 이 메서드를 찾습니다. 같은 쿼리가 정확한 메서드를 검칩니다.
⚡ 증분 인덱싱 — 파일별 SHA-256 콘텐츠 해시; 변경된 새 파일 / 삭제된 파일만 재처리됩니다.
🔍 의미론적 검색 (Semantic search) — 자연어 쿼리 → 정확한 줄 범위를 가진 코드 조각을 순위와 함께 반환합니다.
📞 AST 사용처 조회 — 모든 기호(함수 또는 클래스)의 실제 호출 사이트를 검색하되, 문자열 리터럴 또는 주석 블록의 호출처럼 잘못된 양성(오탐) 결과는 걸러냅니다.
🗄️ 영속 로컬 인덱스 — ChromaDB가
.chroma/에 저장되어 재시작 후에도 유지되며, Git에 커밋되지 않습니다.🔌 stdio MCP 전송 — Claude Desktop, OpenCode, Cursor, Cline 또는 MCP 호환 클라이언트에 붙을 수 있습니다.
기술 스택
계층 | 기술 |
언어 | Python 3.12+ |
프로토콜 | 공식 |
AST 파싱 |
|
벡터 데이터베이스 |
|
임베딩 |
|
설치
Python 3.12+ 필요. 빠르고 현대적인 패키지 매니저인 uv:
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💡 첫 실행 시 임베딩 모델(~90MB)을 다운로드합니다. PyTorch는
sentence-transformers의 의존성으로 설치됩니다. CPU 빌드이면 충분하며 — GPU는 필요하지 않습니다.
MCP 클라이언트에 등록
예시 기본값 (Claude Desktop / stdio 서버를 지원하는 모든 클라이언트):
{
"mcpServers": {
"context-tree": {
"command": "<path-to-venv>/bin/python",
"args": ["-m", "context_tree"],
"env": {}
}
}
}(Windows에서는 <path-to-venv>\Scripts\python.exe를 사용하세요.)
어시스턴트에 노출된 도구
Tool | Signature | Description |
|
| 프로젝트를 탐색하고 해시로 변경된 파일을 감지하여 ChromaDB 컬렉션을 증분 업데이트 |
|
| 인덱싱된 코드에 대한 자연어 검색. |
|
| 함수 또는 클래스의 실제 호출 위치 / 인스턴스를 AST 기반으로 조회 |
일반적인 워크플로:
1. index_workspace("D:/projects/my-app")
2. semantic_search("where do we handle payment retries", limit=8)
3. find_ast_usages("PaymentGateway.retry")지원 언어
언어 | 상태 |
Python | ✅ 출시 시 지원 |
TypeScript / TSX | ✅ 출시 시 지원 |
JavaScript / JSX | ✅ 출시 시 지원 |
Go, Java, Rust… | 🗺️ 로드맵 — 파서 레지스트리가 확장될 수 있도록 설계됨 |
문서
🏛️ ARCHITECTURE.md — 프로젝Http 구조, ChromaDB 데이터 스키마, AST 추출 로직, 증분 인덱싱 설계.
🇷🇺 README.ru.md — 러시아어 문서.
로드맵
Watch mode — 파일 변경 시 자동 재인덱싱 (fs watcher)
tree-sitter 바인딩을 통한 추가 언어 지원
하이브리드 검색 (BM25 + 벡터 탐색)
호출 그래프 인식 조회 ("호출 시점")
PyPI 릴리스 (
context-tree-mcp)
기여
이슈 및 PR은 언제나 환영합니다. 변경 사항을 ARCHITECTURE.md와 일관성 있게 유지해 주시기바랍니다.
저작권
MIT 라이선스 · 자세한 내용은 LICENSE를 확인하세요.
Available Tools
4 toolsfind_ast_usagesA
AST-based lookup of real call sites / instantiations of a function or class. Filters out string literals, comments, and non-call occurrences.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| symbol_name | Yes | ||
| directory_path | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| symbol_name | Yes | ||
| directory_path | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| directory_path | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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.
semantic_searchA
Search code by meaning and keywords (hybrid BM25+vectors, semantic, or keyword). Returns ranked code fragments with exact file, class, method, start_line, end_line, and code snippets. Supports optional Cross-Encoder re-ranking (rerank=True).
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | hybrid | |
| limit | No | ||
| query | Yes | ||
| rerank | No | ||
| directory_path | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing the hybrid retrieval approach, the exact return fields, and optional Cross-Encoder re-ranking. It lacks explicit statements about read-only behavior, performance, or side effects, but for a search tool the disclosed behavior is substantial and useful.
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 with no filler. Key behavior ('ranked code fragments') and return fields are front-loaded, and the optional re-ranking detail is placed at the end without bloating the text.
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?
The output schema exists and covers the return format, so the description does not need to repeat that. However, important invocation context is missing: what 'directory_path' refers to, whether indexing from index_workspace is required first, and how the repository scope is determined. These gaps make the definition adequate but not fully complete for correct invocation.
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 0%, so the description must compensate, but it only enriches two parameters: 'mode' (hybrid BM25+vectors, semantic, keyword) and 'rerank' (Cross-Encoder re-ranking). The parameters 'query', 'limit', and 'directory_path' are not meaningfully explained beyond their schema titles and defaults, leaving significant gaps.
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 identifies a specific verb ('Search') and resource ('code') while explaining that results are ranked by meaning and keywords. It also distinguishes the tool from siblings like find_ast_usages and go_to_definition by emphasizing semantic/keyword search with ranked code fragments rather than AST or definition lookups.
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 implies when to use the tool: when the user wants to search code semantically or by keyword. However, it does not explicitly state when not to use it or contrast it with alternatives, leaving the agent to infer selection from the tool name and sibling context.
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.
2 tool updates
v0.4.0- Added
go_to_definition - Changed
semantic_search2 fields changed- added
Input schema / properties / modeAdded value: +{ + "default": "hybrid", + "enum": [ + "hybrid", + "semantic", + "keyword" + ], + "title": "Mode", + "type": "string" +} - added
Input schema / properties / rerankAdded value: +{ + "default": false, + "title": "Rerank", + "type": "boolean" +}
3 tool updates
v0.1.0- First observed
find_ast_usages - First observed
index_workspace - First observed
semantic_search
TDQS
Scored across 4 tools
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.
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.
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.
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
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