MCP Chat
MCP Chat
MCP Chat は、Anthropic API を通じて AI モデルとの対話型チャット機能を実現す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)アーキテクチャによる拡張可能なツール統合をサポートしています。
前提条件
Python 3.9+
Anthropic API キー
Related MCP server: MCP Chat
セットアップ
ステップ 1: 環境変数の設定
プロジェクトルートにある
.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ます:
ANTHROPIC_API_KEY="" # Enter your Anthropic API secret keyステップ 2: 依存関係のインストール
オプション 1: uv を使用したセットアップ(推奨)
uv は高速な Python パッケージインストーラー兼リゾルバーです。
uv がまだインストールされていない場合は、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オプション 2: uv を使用しないセットアップ
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使い方
基本的な対話
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。
ドキュメントの取得
@ 記号の後にドキュメント ID を続けて使用すると、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コマンド
/ 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。
開発
新しいドキュメントの追加
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 機能の実装
MCP 機能を完全に実装するには:
mcp_server.pyの TODO を完了しますmcp_client.pyに不足している機能を実装します
リントチェックと型チェック
リントチェックや型チェックは実装されていません。
Available Tools
2 toolsedit-documentB
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates a mutation operation ('Edit') but does not state whether changes are permanent, whether write permissions are required, or what happens if the old string is not found. It also leaves ambiguous whether all occurrences are replaced or just the first. This 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 concise sentence that front-loads the main action. It is efficient and to the point, though it has a minor grammatical issue ('documents content'). It earns a 4 because it is appropriately sized and immediately understandable.
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 replace operation with fully described parameters, the description is fairly minimal. But it lacks critical behavioral details: it does not specify whether replacement is global or first-occurrence, what the return value is, or error handling when the old string is absent. Since there is no output schema and no annotations, these gaps make the tool incomplete for an agent to predict behavior accurately.
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 has 100% coverage with each parameter described (doc_id, old_str, new_str). The description does not add any semantic value beyond what the schema already provides, so it stays at the baseline of 3. The schema handles the parameter documentation adequately.
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 a specific verb ('Edit') and resource ('document') with a specific action ('replacing a string... with a new string'). It distinguishes from the sibling read_doc_contents, which is a read operation, so an agent can easily tell them apart.
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 (you edit when you need to replace text), but it provides no explicit guidance on when to use this tool versus the sibling read_doc_contents, nor any exclusion criteria or prerequisites. The context is clear enough for a simple tool, but explicit 'when not' guidance is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_doc_contentsB
Read the contents of a document and return it 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?
No annotations are provided, so the description must bear the full burden of behavioral disclosure. It only states that the contents are returned as a string and does not mention permissions, read-only behavior, error cases, or what happens with missing or malformed document IDs. For a read operation, this is a notable gap.
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 concise sentence that communicates the core action immediately. The minor grammatical error ('return it a string' instead of 'return it as a string') slightly detracts from polish but does not harm clarity significantly.
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 single-parameter read tool, the description is mostly adequate: it names the action and the return type. However, with no annotations and no output schema, it could better describe return value details, potential errors, or the scope of content returned (e.g., full document vs. excerpt).
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 100%, with the single parameter 'doc_id' already described as 'Id of the document to read'. The tool description adds no additional parameter-level meaning, so the baseline score of 3 applies.
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 ('Read') with a clear resource ('contents of a document') and even specifies the return type ('a string'). This clearly distinguishes it from the sibling tool '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 usage is implied by the tool's purpose: use it when you need to view a document's contents. However, there is no explicit guidance on when not to use it or which sibling alternative to choose, though the sibling name 'edit-document' hints at the distinction.
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 have clearly distinct purposes: one reads a document's contents, the other edits it by replacing strings. There is no overlap or ambiguity, making selection straightforward.
The tool names use inconsistent naming conventions: 'read_doc_contents' follows snake_case, while 'edit-document' uses kebab-case. This inconsistency could confuse agents expecting a uniform pattern.
With only 2 tools, the server feels minimal. While it might be intentionally scoped to read/edit operations, the low count borders on thin for a document manipulation server, though not entirely unreasonable.
The server only provides read and edit capabilities. Obvious lifecycle operations like create, delete, or list documents are missing, leaving significant gaps that would require external tools to complete basic document workflows.
Maintenance
Related MCP Connectors
Use your Mac, Windows or Linux computer from ChatGPT, Claude or Codex: files, commands, documents.
Source-checked CLI guides and model-aware planning for Claude Code, Codex, and Grok Build.
- QuallaaOAuthcom.quallaa
Talk to your public-facing AI from any MCP client — Claude, ChatGPT, Cursor, Cline, Windsurf.
Work on your own computers from Claude or ChatGPT: run commands, edit and search files.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceA command-line interface application that enables interactive chat with AI models through the Anthropic API, supporting document retrieval, command-based prompts, and extensible tool integrations.-
- FlicenseNot gradedqualityDmaintenanceA command-line interface application that enables interactive chat with AI models through the Anthropic API, supporting document retrieval, command-based prompts, and extensible tool integrations.-
- FlicenseBqualityDmaintenanceA command-line interface application that enables interactive chat with AI models through the Anthropic API, supporting document retrieval, command-based prompts, and extensible tool integrations.2-
- FlicenseNot gradedqualityDmaintenanceA command-line interface application that enables interactive chat with AI models through the Anthropic API, supporting document retrieval, command-based prompts, and extensible tool integrations.-