MCP Chat
MCP Chat
MCP Chat는 Anthropic API를 통해 AI 모델과 대화형 채팅 기능을 제공하는 명령줄 인터페이스 애플리케이션입니다. 이 애플리케이션은 문서 검색, 명령 기반 프롬프트, MCP(Model Control Protocol) 아키텍처를 통한 확장 가능한 도구 통합을 지원합니다.
사전 요구 사항
Python 3.9+
Anthropic API 키
Related MCP server: mcp_starter_project
설정
1단계: 환경 변수 구성
프로젝트 루트에
.env파일을 생성하거나 편집하고 다음 변수가 올바르게 설정되었는지 확인합니다.
ANTHROPIC_API_KEY="" # Enter your Anthropic API secret key2단계: 종속성 설치
옵션 1: uv로 설정 (권장)
uv는 빠른 Python 패키지 설치 프로그램이자 해석기(resolver)입니다.
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.py사용 방법
기본 상호 작용
메시지를 입력하고 Enter를 눌러 모델과 대화합니다.
문서 검색
@ 기호 다음에 문서 ID를 입력하여 쿼리에 문서 내용을 포함시킵니다:
> Tell me about @deposition.md명령
/ 접두사 뒤에 MCP 서버에 정의된 명령을 입력하여 실행합니다:
> /summarize deposition.mdTab 키를 누르면 명령이 자동 완성됩니다.
개발
새 문서 추가
mcp_server.py 파일을 편집하여 docs 사전에 새 문서를 추가합니다.
MCP 기능 구현
MCP 기능을 완전히 구현하려면:
mcp_server.py의 TODO를 완료합니다.mcp_client.py에서 누락된 기능을 구현합니다.
린트 및 타입 검사
구현된 린트 또는 타입 검사는 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 the old text | |
| old_str | Yes | The text to replace. Must match exactly, including whitespace |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It states that a string is replaced, but does not clarify whether all occurrences are replaced or only the first, nor does it mention that the edit is permanent/mutating, any side effects, or error conditions. This ambiguity is a significant gap for a mutation tool.
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, compact sentence with no filler or redundant restatement of the tool name. It is front-loaded with the action and resource, and every word contributes meaning.
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 simple 3-parameter tool, the description plus full schema coverage is mostly sufficient for an agent to make a call. However, the missing occurrence behavior (all vs. first match) and the absence of an output schema leave some uncertainty about the tool's actual effect and return value, making it minimally complete rather than fully contextualized.
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 input schema covers 100% of the parameters with clear descriptions. The tool description essentially paraphrases the schema (old_str replaced by new_str) without adding new semantic meaning, such as constraints or examples. Baseline 3 is appropriate because the schema carries the burden.
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 ('Edit'), names the exact resource ('a document'), and specifies the mechanism ('replacing a string ... with a new string'). This clearly distinguishes it from the sibling read_doc_contents, which is a read operation.
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 a document's content must be modified via string replacement. However, it provides no explicit guidance about when NOT to use it or when to prefer the sibling read_doc_contents instead. The alternative is only inferable from the tool name, not the description.
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 clearly distinct: one reads document contents, the other modifies them. There is no overlap or ambiguity between the operations.
Both tools use a verb_noun pattern and snake_case, but one uses 'doc_contents' and the other uses 'document', which is a minor inconsistency. A more consistent pair would be read_document/edit_document.
Two tools is a thin surface and falls at the borderline for a useful server. The tools are focused, but the count feels minimal for a standalone document-related MCP server.
The tool surface covers reading and editing documents but lacks create, delete, list, and search capabilities. This leaves significant lifecycle gaps that agents may need to work around.
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