Skip to main content
Glama
IDHaunter

MCP Chat

by IDHaunter

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可能に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)アーキテクチャを介した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

  1. プロジェクトルートの.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。

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=1

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。

  1. 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
  1. 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
  1. 依存関係をインストールします。

uv pip install -e .
  1. 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

  1. 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
  1. 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"
  1. プロジェクトを実行します。

python main.py

使用方法

基本操作

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.md

Tab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ディクショナリ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:

  1. mcp_server.pyのTODOを完了します。

  2. mcp_client.pyに不足している機能を実装します。

リンターと型チェック

リンターや型チェックは実装されていません。

Available Tools

2 tools
edit_documentA

Edit a document by replacing a string in the documents content with a new string

NameRequiredDescriptionDefault
doc_idYesId of the document that will be edited
new_strYesThe new text to insert in place of old text
old_strYesThe text to replace. Must match exactly, including whitespaces.

TDQS

A3.5/5.0
Behavior2/5

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.

Conciseness5/5

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.

Completeness3/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines3/5

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.

NameRequiredDescriptionDefault
doc_idYesId of the document to read

TDQS

A4/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

  1. 2 tool updatesv0.1.0
    • First observededit_document
    • First observedread_doc_contents

TDQS

A3.6/5.0

Scored across 2 tools

Disambiguation5/5

The two tools are completely distinct: one reads document contents, the other edits them. There is no overlap or ambiguity in their purposes.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness2/5

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.

Maintenance

ActivityStale
ResponsivenessNo issues

Related MCP Connectors

Related MCP Servers