MCP Chatは、Anthropic APIを介してAIモデルと対話的なチャットができるコマンドラインインターフェースアプリケーションです。このアプリケーションは、ドキュメントの取得、コマンドベースのプロンプト、およびMCP（Model Control Protocol）アーキテクチャを通じた拡張可能なツール統合をサポートしています。
Python 3.9以上
Anthropic APIキー
Related MCP server: MCP Chat
プロジェクトルートに.envファイルを作成または編集し、以下の変数が正しく設定されていることを確認してください：
ANTHROPIC_API_KEY="" # Enter your Anthropic API secret keyuvは、高速なPythonパッケージインストーラーおよびリゾルバーです。
まだインストールされていない場合は、uvをインストールします：
pip install uv仮想環境を作成して有効化します：
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate依存関係をインストールします：
uv pip install -e .プロジェクトを実行します
uv run main.py仮想環境を作成して有効化します：
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate依存関係をインストールします：
pip install anthropic python-dotenv prompt-toolkit "mcp[cli]==1.8.0"プロジェクトを実行します
python main.pyメッセージを入力してEnterキーを押すだけで、モデルとチャットできます。
@記号の後にドキュメントIDを続けることで、クエリにドキュメントの内容を含めることができます：
> Tell me about @deposition.md/プレフィックスを使用して、MCPサーバーで定義されたコマンドを実行します：
> /summarize deposition.mdTabキーを押すと、コマンドが自動補完されます。
mcp_server.pyファイルを編集して、docs辞書に新しいドキュメントを追加します。
MCP機能を完全に実装するには：
mcp_server.py内のTODOを完了させる
mcp_client.pyで不足している機能を実装する
リンティングや型チェックは実装されていません。
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 |
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 the contents of a document and return it as a string.
| Name | Required | Description | Default |
|---|---|---|---|
| doc_id | Yes | Id of the document to read |
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0edit_documentread_doc_contentsScored across 2 tools
The two tools are clearly distinct: one reads document contents, the other edits by string replacement. No ambiguity in purpose.
Both tools follow a consistent verb_noun pattern: read_doc_contents and edit_document. The naming style is uniform.
With only two tools, the server feels extremely thin for any meaningful functionality, especially given the 'Chat' name suggests broader capabilities. The surface is too limited.
The toolset covers only read and edit operations, missing essential CRUD operations like create, delete, or list. This creates significant gaps for document management workflows.
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