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Tribal Knowledge Service

Name: Tribal Knowledge Service
Author: agentience

by agentience

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

部族 - 知識サービス

Tribalは、エラーナレッジの追跡と取得のためのMCP（Model Context Protocol）サーバー実装です。Claude CodeやClineなどのツールとの統合のために、REST APIとネイティブMCPインターフェースの両方を提供します。

特徴

完全なコンテキストでエラーレコードを保存および取得します
ChromaDB を使用したベクトル類似性検索
REST API (FastAPI) とネイティブ MCP インターフェース
APIキーを使用したJWT認証
ローカルストレージ（ChromaDB）とAWS統合
Docker-compose デプロイメント
CLIクライアント統合

Related MCP server: MemoCall

概要

Tribalは、Claudeがプログラミングエラーを記憶し、学習するのに役立ちます。Claude Codeセッションを開始すると、追加のインポートなしでTribalがMCPを通じて自動的に利用可能になります。

クロードは次のことを行います:

ストアプログラミングエラーと解決策
問題が発生した場合に類似のエラーを検索する
コーディングパターンに特化した知識ベースを構築する

UVを使用したTribalのパッケージ化とインストール

前提条件

Python 3.12以上
uv パッケージマネージャー (推奨)

ビルドとインストールの手順

オプション1：UVによる直接設置

最も簡単な方法は、現在のディレクトリから直接インストールすることです。

# From the project root directory
cd /path/to/tribal

# Install using uv
uv pip install .

オプション2: 開発インストール

変更をすぐに反映させたい開発作業の場合:

# From the project root directory
cd /path/to/tribal

# Install in development mode
uv pip install -e .

オプション3: 最初にパッケージをビルドする

配布可能なパッケージをビルドする場合:

# Make sure you're in the project root directory
cd /path/to/tribal

# Install the build package if needed
uv pip install build

# Build the package
python -m build

# This creates distribution files in the dist/ directory
# Now install the wheel file
uv pip install dist/tribal-0.1.0-py3-none-any.whl

オプション4: `uv tool install`コマンドを使用する

ツールのインストール方法を使用することもできます。

# Install as a global tool
cd /path/to/tribal
uv tool install .

# Or install in development mode
uv tool install -e .

検証

インストール後、ツールが正しくインストールされていることを確認します。

# Check the installation
which tribal

# Check the version
tribal version

クロードとの統合

インストール後、Claude と統合できます。

# Add Tribal to Claude Code
claude mcp add tribal --launch "tribal"

# Verify the configuration
claude mcp list

# For Docker container
claude mcp add tribal http://localhost:5000

使用法

利用可能なMCPツール

Tribal は次の MCP ツールを提供します。

add_error - 新しいエラーレコードを作成する (POST /errors)
get_error - UUIDでエラーを取得する (GET /errors/{id})
update_error - 既存のエラーを変更する (PUT /errors/{id})
delete_error - エラーレコードを削除する (DELETE /errors/{id})
search_errors - 条件でエラーを検索する (GET /errors)
find_similar - 意味的類似性検索 (GET /errors/similar)
get_token - JWTトークンを取得する（POST /token）

クロードとの使用例

クロードがエラーに遭遇した場合:

I'll track this error and look for similar problems in our knowledge base.

クロードが解決策を見つけたとき:

I've found a solution! I'll store this in our knowledge base for next time.

クロードへのコマンド

クロードに次のことを依頼できます。

「部族の知識ベースで同様のエラーを探してください」
「このソリューションをエラーデータベースに保存します」
「このエラーが以前に発生したかどうかを確認してください」

サーバーの実行

部族の命令を使う

# Run the server
tribal

# Get help
tribal help

# Show version
tribal version

# Run with options
tribal server --port 5000 --auto-port

Pythonモジュールの使用

# Run the Tribal server
python -m mcp_server_tribal.mcp_app

# Run the FastAPI backend server
python -m mcp_server_tribal.app

レガシーエントリポイントの使用

# Legacy MCP server
mcp-server

# Legacy FastAPI server
mcp-api

コマンドラインオプション

# Development mode with auto-reload
mcp-api --reload
mcp-server --reload

# Custom port
mcp-api --port 8080
mcp-server --port 5000

# Auto port selection
mcp-api --auto-port
mcp-server --auto-port

FastAPIサーバーはhttp://localhost:8000で利用可能で、APIドキュメントは/docsにあります。Claudeやその他のMCP対応LLM用のMCPサーバーはhttp://localhost:5000で利用可能になります。

環境変数

FastAPIサーバー

PERSIST_DIRECTORY : ChromaDB ストレージパス (デフォルト: "./chroma_db")
API_KEY : 認証キー（デフォルト: "dev-api-key")
SECRET_KEY : JWT署名キー（デフォルト: "insecure-dev-key-change-in-production")
REQUIRE_AUTH : 認証要件（デフォルト: "false"）
PORT : サーバーポート（デフォルト: 8000）

MCPサーバー

MCP_API_URL : FastAPI サーバーの URL (デフォルト: " http://localhost:8000 ")
MCP_PORT : MCPサーバポート（デフォルト: 5000）
MCP_HOST : バインドするホスト（デフォルト: "0.0.0.0")
API_KEY : FastAPI アクセスキー (デフォルト: "dev-api-key")
AWS_ACCESS_KEY_ID 、 AWS_SECRET_ACCESS_KEY 、 AWS_S3_BUCKET : AWS統合用

APIエンドポイント

POST /errors : 新しいエラーレコードを作成する
GET /errors/{error_id} : IDでエラーを取得する
PUT /errors/{error_id} : エラーレコードを更新する
DELETE /errors/{error_id} : エラーを削除
GET /errors : 条件でエラーを検索する
GET /errors/similar : 類似のエラーを検索する
POST /token : 認証トークンを取得する

クライアントの使用

# Add a new error record
mcp-client --action add --error-type ImportError --language python --error-message "No module named 'requests'" --solution-description "Install requests" --solution-explanation "You need to install the requests package"

# Get an error by ID
mcp-client --action get --id <error-id>

# Search for errors
mcp-client --action search --error-type ImportError --language python

# Find similar errors
mcp-client --action similar --query "ModuleNotFoundError: No module named 'pandas'"

仕組み

TribalはChromaDBを使用してエラー記録と解決策を保存します
クロードがエラーに遭遇すると、エラーの詳細をTribalに送信します。
トライバルはエラーをベクトル化し、類似のエラーを検索します
クロードは適切な解決策を提案します
新しいソリューションは将来の参照用に保存されます

発達

テストの実行

pytest
pytest tests/path_to_test.py::test_name  # For specific tests

リンティングと型チェック

ruff check .
mypy .
black .

GitHubワークフロー

このプロジェクトでは、継続的インテグレーションとデプロイメントにGitHub Actionsを使用しています。このワークフローは、メインへのプッシュとプルリクエスト時に、テスト、リンティング、型チェックを自動的に実行します。

ワークフローの手順

テスト: リンティング、型チェック、ユニットテストを実行します
- Python 3.12 を使用
- uvで依存関係をインストールします
- ruff、black、mypy、pytest を実行
ビルドと公開: パッケージをビルドしてPyPIに公開します
- メインブランチへのプッシュ時にのみトリガーされます
- Pythonのビルドシステムを使用する
- twineを使用してPyPIに公開する

ローカルテスト

提供されているスクリプトを使用して、GitHub ワークフローをローカルでテストできます。

# Make the script executable
chmod +x scripts/test-workflow.sh

# Run the workflow locally
./scripts/test-workflow.sh

このスクリプトは、ローカルマシン上で GitHub ワークフローステップをシミュレートします。

Pythonのバージョンをチェックします（3.12を推奨）
uvを使用して依存関係をインストールします
毛羽立ちとひだのある走り
黒でフォーマットをチェック
mypyで型チェックを実行する
pytestでテストを実行する
パッケージをビルドする

注: スクリプトは、ローカルテストの公開手順をスキップします。

プロジェクト構造

tribal/
├── src/
│   ├── mcp_server_tribal/      # Core package
│   │   ├── api/                # FastAPI endpoints
│   │   ├── cli/                # Command-line interface
│   │   ├── models/             # Pydantic models
│   │   ├── services/           # Service layer
│   │   │   ├── aws/            # AWS integrations
│   │   │   └── chroma_storage.py # ChromaDB implementation
│   │   └── utils/              # Utility functions
│   └── examples/               # Example usage code
├── tests/                      # pytest test suite
├── docker-compose.yml          # Docker production setup
├── pyproject.toml              # Project configuration
├── VERSIONING.md               # Versioning strategy documentation
├── CHANGELOG.md                # Version history
├── .bumpversion.cfg            # Version bumping configuration
└── README.md                   # Project documentation

バージョン管理

Tribalはセマンティックバージョニングに準拠しています。詳細についてはVERSIONING.mdをご覧ください。

バージョン番号 (MAJOR.MINOR.PATCH)
データベース互換性のためのスキーマのバージョン管理
ブランチの命名規則
リリースとホットフィックスの手順

バージョンを確認するには以下を実行します:

# Display version information
tribal version

依存関係の管理

# Add a dependency
uv pip add <package-name>

# Add a development dependency
uv pip add <package-name>

# Update dependencies
uv pip sync requirements.txt requirements-dev.txt

展開

Docker デプロイメント

# Build and start containers
docker-compose up -d --build

# View logs
docker-compose logs -f

# Stop containers
docker-compose down

# With custom environment variables
API_PORT=8080 MCP_PORT=5000 REQUIRE_AUTH=true API_KEY=your-secret-key docker-start

デスクトップ統合のクロード

オプション1: Claude for Desktopでサーバーを起動する

~/Library/Application Support/Claude/claude_desktop_config.json開きます。
MCP サーバー構成を追加します (Tribal ツールがすでにインストールされていることを前提としています)。
```
{
  "mcpServers": [
    {
      "name": "tribal",
      "launchCommand": "tribal"
    }
  ]
}
```
デスクトップ版のClaudeを再起動する

オプション2: 実行中のDockerコンテナに接続する

コンテナを起動します。
```
cd /path/to/tribal
docker-start
```

Claude をデスクトップ用に設定する:

{
  "mcpServers": [
    {
      "name": "tribal",
      "url": "http://localhost:5000"
    }
  ]
}

Claude Code CLI 統合

# For Docker container
claude mcp add tribal http://localhost:5000

# For directly launched server
claude mcp add tribal --launch "tribal"

# Test the connection
claude mcp list
claude mcp test tribal

トラブルシューティング

部族のインストールを確認する: which tribal
設定を確認してください: claude mcp list
テストサーバーのステータス: tribal status
Claudeの出力でエラーメッセージを探す
データベースディレクトリが存在し、適切な権限があることを確認してください

クラウド展開

このプロジェクトには、AWS サービスのプレースホルダー実装が含まれています。

S3Storage : Amazon S3にエラーレコードを保存する
DynamoDBStorage : データベースとして DynamoDB を使用する場合

ライセンス

MITライセンス

Available Tools

6 tools

delete_errorB

Delete an error record.

Args:
    error_id: UUID of the error record

Returns:
    True if deleted, False if not found

ParametersJSON Schema

Name	Required	Description	Default
`error_id`	Yes

TDQS

B3.1/5.0

Behavior2/5

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 states the action ('Delete') and return values, but lacks critical details: it doesn't specify if deletion is permanent/reversible, mention authentication or permission requirements, indicate side effects (e.g., cascading deletions), or note rate limits. For a destructive tool, this leaves significant gaps in understanding its behavior.

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 well-structured and front-loaded with the core purpose. It uses three concise sentences (purpose, args, returns) with zero wasted words. Each sentence earns its place by adding distinct value: action, parameter meaning, and outcome.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (destructive operation with no annotations and no output schema), the description is incomplete. It lacks context on safety (e.g., confirmation prompts, irreversible effects), error handling beyond 'not found', and integration with sibling tools. The return value explanation is minimal, but without an output schema, more detail on responses would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate. It adds essential semantics: it clarifies that 'error_id' is a 'UUID of the error record', which is not in the schema (which only lists 'Error Id' as the title). This provides meaningful context beyond the schema, though it doesn't elaborate on UUID format or sourcing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Delete') and resource ('an error record'), making the purpose unambiguous. It distinguishes itself from siblings like 'get_error_by_id' (read) and 'track_error' (create). However, it doesn't explicitly differentiate from potential destructive siblings beyond the verb, so it's not a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'search_errors' or 'find_similar_errors'. It doesn't mention prerequisites (e.g., needing the error_id from another operation) or warn against misuse. The only implied usage is when you have an error_id to delete, but this is basic and insufficient for a destructive operation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

find_similar_errorsC

Find errors similar to the given query.

Args:
    query: Text to search for in the knowledge base
    max_results: Maximum number of results to return

Returns:
    List of similar error records

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`max_results`	No

TDQS

C2.6/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions returning a list of similar error records but doesn't disclose behavioral traits such as how similarity is determined, whether it's read-only, performance characteristics, or error handling. This is a significant gap for a search tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded with the core purpose. The Args and Returns sections are structured clearly, though the 'Returns' part could be more specific. Every sentence adds value, but it could be slightly more concise by integrating the sections more fluidly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a similarity search tool with no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what 'similar' means, the return format beyond 'List of similar error records', or how results are ordered, leaving gaps for the agent to infer behavior.

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 description coverage is 0%, so the schema provides no parameter descriptions. The description adds basic semantics for 'query' ('Text to search for in the knowledge base') and 'max_results' ('Maximum number of results to return'), which compensates partially but lacks details like format constraints or default behavior beyond the schema's default value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Find errors similar to the given query' which provides a clear verb ('Find') and resource ('errors'), but it's vague about what constitutes 'similar' and doesn't distinguish from sibling tools like 'search_errors'. It's not tautological but lacks specificity about the similarity mechanism.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'search_errors' or 'get_error_by_id'. The description implies usage for finding similar errors but doesn't specify contexts, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_api_statusC

Check the API status.

Returns:
    API status information

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

C2.7/5.0

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. It states the tool 'Check[s] the API status' and 'Returns API status information', but doesn't disclose behavioral traits like whether it's read-only, requires authentication, has rate limits, or what specific information is included in the return. For a tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences, but it's not front-loaded effectively. The first sentence states the purpose, but the second ('Returns: API status information') is redundant and doesn't add value beyond what's implied. It could be more structured to emphasize key details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'API status information' includes, how it's formatted, or any behavioral context. For a tool that might be critical for monitoring, this leaves too many gaps for effective agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics, but this is appropriate given the lack of parameters, warranting a baseline score of 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'Check[s] the API status', which is a clear verb+resource combination. However, it doesn't differentiate this from sibling tools like 'track_error' or 'get_error_by_id' that might also provide status-related information, nor does it specify what aspects of API status are checked (health, uptime, version, etc.).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, timing, or how it differs from sibling tools like 'track_error' or 'search_errors' that might overlap in monitoring contexts. This leaves the agent with no usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_error_by_idA

Get an error record by its ID.

Args:
    error_id: UUID of the error record

Returns:
    The error record or None if not found

ParametersJSON Schema

Name	Required	Description	Default
`error_id`	Yes

TDQS

A3.6/5.0

Behavior3/5

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 states the tool retrieves an error record or returns None if not found, which clarifies the read-only nature and potential outcomes. However, it lacks details on permissions, rate limits, or error handling beyond the basic return behavior, leaving gaps in transparency.

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 front-loaded with the core purpose, followed by structured sections for Args and Returns, making it highly efficient and easy to parse. Every sentence adds value without redundancy, adhering to best practices for conciseness.

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?

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is adequate but has clear gaps. It covers the basic operation and parameter semantics but lacks details on behavioral aspects like authentication or error scenarios. Without annotations or output schema, more context would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate. It adds meaningful context by specifying that 'error_id' is a UUID, which clarifies the parameter's format beyond the schema's generic string type. Since there's only one parameter, this is sufficient to elevate the score above the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('an error record by its ID'), making it immediately understandable. It distinguishes this from siblings like 'search_errors' or 'find_similar_errors' by focusing on direct ID-based retrieval rather than search operations. However, it doesn't explicitly contrast with 'delete_error' or 'track_error', keeping it from a perfect score.

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 have a specific error ID, as opposed to searching or finding similar errors, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'delete_error' or 'track_error'. There's no mention of prerequisites or exclusions, leaving some ambiguity in context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_errorsC

Search for errors in the knowledge base.

Args:
    error_type: Type of error to filter by
    language: Programming language to filter by
    framework: Framework to filter by
    error_message: Error message to search for
    code_snippet: Code snippet to search for
    task_description: Task description to search for
    max_results: Maximum number of results to return

Returns:
    List of matching error records

ParametersJSON Schema

Name	Required	Description	Default
`error_type`	No
`language`	No
`framework`	No
`error_message`	No
`code_snippet`	No
`task_description`	No
`max_results`	No

TDQS

C2.9/5.0

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 states the tool searches and returns a list, but lacks critical details: whether it's read-only, how results are ordered, if there's pagination, error handling, or performance characteristics. For a search tool with 7 parameters, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections ('Args:', 'Returns:') and uses bullet points for parameters, making it easy to scan. It's appropriately sized for a tool with 7 parameters, though the parameter explanations are minimal. There's no wasted text, but it could be more front-loaded with key usage information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (7 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and parameters but lacks behavioral details, usage guidelines, and output specifics. For a search tool in a knowledge base context, this leaves the agent with insufficient information to use it effectively without trial and error.

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 description lists all 7 parameters with brief labels, but schema description coverage is 0%, so the schema provides no additional documentation. The description adds basic semantic context (e.g., 'error_type: Type of error to filter by'), which helps interpret parameters beyond their names. However, it doesn't explain formats, constraints, or interactions between parameters, leaving room for ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search for errors in the knowledge base.' This specifies the verb ('search') and resource ('errors in the knowledge base'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'find_similar_errors' or 'get_error_by_id', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'find_similar_errors' or 'get_error_by_id', nor does it specify contexts where this search tool is preferred. The agent must infer usage from the tool name alone, which is insufficient for optimal selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

track_errorA

Track an error and its solution in the knowledge base.

Args:
    error_type: Type of error (e.g., ImportError, TypeError)
    error_message: The error message
    language: Programming language (e.g., python, javascript)
    framework: Framework used (e.g., fastapi, react)
    code_snippet: The code that caused the error
    task_description: What the user was trying to accomplish
    solution_description: Brief description of the solution
    solution_code_fix: Code that fixes the error
    solution_explanation: Detailed explanation of why the solution works
    solution_references: List of reference links

Returns:
    The created error record

ParametersJSON Schema

Name	Required	Description	Default
`error_type`	Yes
`error_message`	Yes
`language`	Yes
`framework`	No
`code_snippet`	No
`task_description`	No
`solution_description`	No
`solution_code_fix`	No
`solution_explanation`	No
`solution_references`	No

TDQS

A3.5/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool creates a record ('track an error'), implying a write operation, but doesn't mention permissions needed, whether the operation is idempotent, rate limits, or what happens on failure. The return statement is minimal ('The created error record') without format details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, args, returns) and uses bullet-like formatting for parameters. Every sentence earns its place by explaining functionality or parameters. It could be slightly more concise in the parameter explanations but remains efficient overall.

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?

For a 10-parameter creation tool with no annotations and no output schema, the description provides good parameter documentation but lacks behavioral context. It explains what data to provide but not how the tool behaves operationally (e.g., error handling, authentication). The return value is mentioned but not described in detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage and 10 parameters, the description provides comprehensive parameter semantics beyond the schema. It clearly explains each parameter's purpose with examples (e.g., 'error_type: Type of error (e.g., ImportError, TypeError)'), adding significant value that compensates for the schema's lack of descriptions.

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 tool's purpose with a specific verb ('track') and resource ('error and its solution in the knowledge base'). It distinguishes from sibling tools like 'delete_error', 'find_similar_errors', and 'search_errors' by focusing on creation rather than deletion, retrieval, or search operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'search_errors' or 'find_similar_errors'. It mentions no prerequisites, exclusions, or specific contexts for usage, leaving the agent to infer when this creation tool is appropriate versus other operations.

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.

6 tool updatesAug 18, 2025
- First observeddelete_error
- First observedfind_similar_errors
- First observedget_api_status
- First observedget_error_by_id
- First observedsearch_errors
- First observedtrack_error

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.4/5.0

Scored 2026-04-18 10:45 across 6 tools

Disambiguation4/5

Most tools have distinct purposes: track_error creates records, get_error_by_id retrieves single records, delete_error removes them, search_errors filters by multiple criteria, and find_similar_errors performs semantic similarity searches. However, search_errors and find_similar_errors could potentially be confused as both search for errors, though their approaches differ (filtering vs. similarity).

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case: delete_error, find_similar_errors, get_api_status, get_error_by_id, search_errors, and track_error. The naming is predictable and readable throughout the set.

Tool Count5/5

With 6 tools, this is well-scoped for a knowledge base service focused on error tracking. Each tool serves a clear purpose (CRUD operations, searching, and status checks), and none feel redundant or missing given the domain.

Completeness4/5

The toolset covers core CRUD operations (create via track_error, read via get_error_by_id, delete via delete_error) and searching (search_errors, find_similar_errors), with get_api_status for monitoring. A minor gap is the lack of an update tool to modify existing error records, which agents might need to work around by deleting and recreating.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Reddit Discussion

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部族 - 知識サービス

特徴

概要

UVを使用したTribalのパッケージ化とインストール

前提条件

ビルドとインストールの手順

オプション1：UVによる直接設置

オプション2: 開発インストール

オプション3: 最初にパッケージをビルドする

オプション4: uv tool installコマンドを使用する

検証

クロードとの統合

使用法

利用可能なMCPツール

クロードとの使用例

クロードへのコマンド

サーバーの実行

部族の命令を使う

Pythonモジュールの使用

レガシーエントリポイントの使用

コマンドラインオプション

環境変数

FastAPIサーバー

MCPサーバー

APIエンドポイント

クライアントの使用

仕組み

発達

テストの実行

リンティングと型チェック

GitHubワークフロー

ワークフローの手順

ローカルテスト

プロジェクト構造

バージョン管理

依存関係の管理

展開

Docker デプロイメント

デスクトップ統合のクロード

オプション1: Claude for Desktopでサーバーを起動する

オプション2: 実行中のDockerコンテナに接続する

Claude Code CLI 統合

トラブルシューティング

クラウド展開

ライセンス

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches

オプション4: `uv tool install`コマンドを使用する