MCP Chat
MCP Chat
MCP Chat은 Anthropic API를 통해 AI 모델과 대화형 채팅 기능을 제공하는 명령줄 인터페이스 애플리케이션입니다. 이 애플리케이션은 MCP(Model Control Protocol) 아키텍처를 통해 문서 검색, 명령 기반 프롬프트, 확장 가능한 도구 통합을 지원합니다.
사전 요구 사항
Python 3.9+
Anthropic API 키
Related MCP server: MCP Chat
설정
1단계: 환경 변수 구성
프로젝트 루트에
.env파일을 생성하거나 편집하고 다음 변수가 올바르게 설정되었는지 확인합니다.
CLAUDE_MODEL="claude-sonnet-4-5"
ANTHROPIC_API_KEY="" # Enter your Anthropic API secret key
# Set to 1 if you're using uv to run the project
# Set to 0 if you're *not* using uv
USE_UV=12단계: 종속성 설치
옵션 1: uv로 설정 (권장)
uv는 빠른 Python 패키지 설치 프로그램이자 해석기입니다.
아직 설치되지 않은 경우 uv를 설치합니다:
pip install uv가상 환경을 생성하고 활성화합니다:
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate종속성을 설치합니다:
uv pip install -e .프로젝트를 실행합니다
uv run main.py옵션 2: uv 없이 설정
가상 환경을 생성하고 활성화합니다:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate종속성을 설치합니다:
pip install anthropic python-dotenv prompt-toolkit "mcp[cli]==1.8.0"프로젝트를 실행합니다
python main.py사용법
기본 대화
메시지를 입력하고 Enter를 눌러 모델과 채팅하세요.
문서 검색
@ 기호 다음에 문서 ID를 사용하여 쿼리에 문서 내용을 포함하세요:
> Tell me about @deposition.md명령
MCP 서버에 정의된 명령을 실행하려면 / 접두사를 사용하세요:
> /summarize deposition.md명령은 Tab을 누르면 자동 완성됩니다.
개발
새 문서 추가
mcp_server.py 파일을 편집하여 docs 사전에 새 문서를 추가하세요.
MCP 기능 구현
MCP 기능을 완전히 구현하려면:
mcp_server.py의 TODO를 완료하세요mcp_client.py에서 누락된 기능을 구현하세요
린트 및 타입 검사
구현된 린트 또는 타입 검사가 없습니다.
Actually, for "Model Control Protocol" - I should keep this as the expansion of MCP. Since "MCP" is the abbreviation and it's the actual protocol name, the expansion might be kept in English. Let me think... The instruction says to keep product names verbatim. "MCP" is definitely an acronym for the architecture name. The full name "Model Control Protocol" - should I translate it? It's a technical term. I'll keep it in English as protocol names are typically not translated, but I could add Korean context. Actually, looking at Korean technical writing conventions, they often keep English technical terms. Let me keep "MCP(Model Control Protocol)" as is since it's a technical/protocol name.
Hmm, but actually, thinking about it more - in Korean translation of technical docs, abbreviations and technical terms are usually kept in English. So I'll keep "MCP (Model Control Protocol)" as is.
Also, should "Anthropic API" be kept? Yes, "Anthropic" is a company/product nameive.
Let me also reconsider: "command-based prompts" - I translated to "명령 기반 프롬프트". The word "prompt" is commonly used in Korean tech context as "프롬프트" so that works.
Let me also double-check that I'm preserving the GXP placeholders and all markdown formatting.
One more consideration: "Run the project" - should be "프로젝트 실행" or "프로젝트를 실행합니다". I used the latter form consistently.
I think my translation is good. Let me output it.# MCP Chat
MCP Chat은 Anthropic API를 통해 AI 모델과 대화형 채팅 기능을 제공하는 명령줄 인터페이스 애플리케이션입니다. 이 애플리케이션은 MCP(Model Control Protocol) 아키텍처를 통한 문서 검색, 명령 기반 프롬프트, 확장 가능한 도구 통합을 지원합니다.
## 사전 요구 사항
* Python 3.9+
* Anthropic API 키
## 설정
### 1단계: 환경 변수 구성
1. 프로젝트 루트에 `.env` 파일을 생성하거나 편집하고 다음 변수가 올바르게 설정되었는지 확인합니다.
GXP1
### 2단계: 종속성 설치
#### 옵션 1: uv로 설정 (권장)
[uv](https://github.com/astral-sh/uv)는 빠른 Python 패키지 설치 프로그램이자 해석기입니다.
1. 아직 설치되지 않은 경우 uv를 설치합니다:
GXP2
2. 가상 환경을 생성하고 활성화합니다:
GXP3
3. 종속성을 설치합니다:
GXP4
4. 프로젝트를 실행합니다
GXP5
#### 옵션 2: uv 없이 설정
1. 가상 환경을 생성하고 활성화합니다:
GXP6
2. 종속성을 설치합니다:
GXP7
3. 프로젝트를 실행합니다
GXP8
## 사용법
### 기본 상호 작용
메시지를 입력하고 Enter를 눌러 모델과 채팅하세요.
### 문서 검색
`@` 기호 다음에 문서 ID를 입력하여 쿼리에 문서 내용을 포함하세요:
GXP9
### 명령
MCP 서버에 정의된 명령을 실행하려면 `/` 접두사를 사용하세요:
GXP10
Tab을 누르면 명령이 자동 완성됩니다.
## 개발
### 새 문서 추가
`mcp_server.py` 파일을 편집하여 `docs` 사전에 새 문서를 추가하세요.
### MCP 기능 구현
MCP 기능을 완전히 구현하려면:
1. `mcp_server.py`의 TODO를 완료하세요
2. `mcp_client.py`에서 누락된 기능을 구현하세요
### 린트 및 타입 검사
구현된 린트 또는 타입 검사는 없습니다.# MCP Chat
MCP Chat은 Anthropic API를 통해 AI 모델과 대화형 채팅 기능을 제공하는 명령줄 인터페이스 애플리케이션입니다. 이 애플리케이션은 MCP(Model Control Protocol) 아키텍처를 통해 문서 검색, 명령 기반 프롬프트, 확장 가능한 도구 통합을 지원합니다.
## 사전 요구 사항
* Python 3.9+
* Anthropic API 키
## 설정
### 1단계: 환경 변수 구성
1. 프로젝트 루트에서 `.env` 파일을 생성하거나 편집하고 다음 변수가 올바르게 설정되었는지 확인합니다:
GXP1
### 2단계: 종속성 설치
#### 옵션 1: uv로 설정 (권장)
[uv](https://github.com/astral-sh/uv)는 빠른 Python 패키지 설치 프로그램이자 해석기입니다.
1. 아직 설치되지 않은 경우 uv를 설치합니다:
GXP2
2. 가상 환경을 생성하고 활성화합니다:
GXP3
3. 종속성을 설치합니다:
GXP4
4. 프로젝트를 실행합니다
GXP5
#### 옵션 2: uv 없이 설정
1. 가상 환경을 생성하고 활성화합니다:
GXP6
2. 종속성을 설치합니다:
GXP7
3. 프로젝트를 실행합니다
GXP8
## 사용법
### 기본 대화
메시지를 입력하고 Enter를 눌러 모델과 채팅하세요.
### 문서 검색
`@` 기호 다음에 문서 ID를 입력하여 쿼리에 문서 내용을 포함하세요:
GXP9
### 명령
MCP 서버에 정의된 명령을 실행하려면 `/` 접두사를 사용하세요:
GXP10
명령은 Tab을 누르면 자동 완성됩니다.
## 개발
### 새 문서 추가
`mcp_server.py` 파일을 편집하여 `docs` 사전에 새 문서를 추가하세요.
### MCP 기능 구현
MCP 기능을 완전히 구현하려면:
1. `mcp_server.py`의 TODO를 완료하세요
2. `mcp_client.py`에서 누락된 기능을 구현하세요
### 린트 및 타입 검사
구현된 린트 또는 타입 검사가 없습니다.Available Tools
2 toolsedit_documentA
Edit a document by replacing a string in the documents content with a new string
| Name | Required | Description | Default |
|---|---|---|---|
| doc_id | Yes | Id of the document that will be edited | |
| new_str | Yes | The new text to insert in place of old text | |
| old_str | Yes | The text to replace. Must match exactly, including whitespaces. |
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 only states the operation and does not mention side effects (e.g., persistence, overwriting), failure conditions (e.g., old_str not found), or whether the edit applies to all occurrences. This is a minimal disclosure for a mutating operation.
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, efficient sentence with no fluff. The core action and resource are front-loaded, and every word contributes to understanding the tool's 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?
The tool is relatively simple (3 required string params, no output schema). The description covers the essential operation, but it omits behavioral details like what happens if old_str is not found or if multiple matches exist. For a mutating tool without annotations, this is a minor but noticeable gap.
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 coverage is 100%, so all parameters have descriptions in the schema. The description itself adds no new semantic detail beyond what the schema already provides; it just reiterates the replace action. This meets the baseline for high 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 clearly states the action (edit), the resource (a document), and the specific operation (replacing a string with a new string). This is a specific verb+resource that distinguishes it from the sibling tool read_doc_contents, which is focused on reading.
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 usage (when you need to modify content) but provides no explicit guidance on when to use this tool versus the sibling read_doc_contents, nor does it mention any prerequisites or exclusions. The context is clear but lacks differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_doc_contentsA
Read the contents of a document and return it as a string.
| Name | Required | Description | Default |
|---|---|---|---|
| doc_id | Yes | Id of the document to read |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does state the output behavior ('return it as a string'), but does not mention error handling, permissions, or what happens when the document does not exist. For a simple read operation, this is adequate but not rich.
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 wasted words. It efficiently conveys both the operation and the return format.
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?
This is a simple tool with one parameter, full schema coverage, and no output schema. The description is largely complete for a read operation, especially since it explicitly states the return type. It could be slightly stronger with error or permission context, but nothing essential is missing for basic 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?
The schema already fully documents the only parameter, doc_id, as 'Id of the document to read', so schema coverage is 100%. The description adds no additional parameter meaning beyond the schema, matching the baseline score.
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 states a specific verb and resource: 'Read the contents of a document' and explicitly notes the return type as a string. This clearly distinguishes it from the sibling 'edit_document', which implies modification rather than reading.
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 verb 'Read' establishes a clear context for when this tool is appropriate, and its contrast with 'edit_document' implies read-only usage. However, it does not explicitly state when not to use it or name the alternative.
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
v0.1.0- First observed
edit_document - First observed
read_doc_contents
TDQS
Scored across 2 tools
The two tools are completely distinct: one reads document contents, the other edits them. There is no overlap or ambiguity in their purposes.
Both names follow a verb_noun pattern, but one uses the abbreviation 'doc' while the other uses 'document', and one refers to 'contents' while the other uses 'document'. Minor inconsistency, but the pattern is still readable and predictable.
With only two tools, the server feels thin for a document-related service. While each tool has a clear purpose, the low count is borderline for being considered a well-scoped set.
The server supports reading and editing but lacks create, delete, or listing capabilities. Editing is limited to string replacement, which restricts the possible workflows agents can perform.
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