DeepSource MCP Server
DeepSource MCP サーバー
DeepSource と統合して、AI アシスタントにコード品質メトリック、問題、分析結果へのアクセスを提供するモデル コンテキスト プロトコル (MCP) サーバー。
概要
DeepSource MCPサーバーは、AIアシスタントがモデルコンテキストプロトコルを介してDeepSourceのコード品質分析機能と連携することを可能にします。この統合により、AIアシスタントは以下のことが可能になります。
コードメトリクスと分析結果を取得する
アクセスとフィルターの問題
品質状態を確認する
プロジェクトの品質を時間の経過とともに分析する
Related MCP server: CodeAlive MCP
特徴
DeepSource API統合:GraphQL API経由でDeepSourceに接続します
MCP プロトコル サポート: AI アシスタント統合用のモデル コンテキスト プロトコルを実装します
品質メトリクスとしきい値: しきい値を使用してコード品質メトリクスを取得および管理します
セキュリティコンプライアンスレポート: OWASP Top 10、SANS Top 25、MISRA-C コンプライアンスレポートにアクセスします
依存関係の脆弱性: 依存関係に関するセキュリティ脆弱性情報にアクセスする
TypeScript/Node.js : 型安全性と最新のJavaScript機能のためにTypeScriptで構築されています
クロスプラットフォーム: Linux、macOS、Windowsで動作
堅牢なエラー処理: ネットワーク、認証、解析の問題に対する包括的なエラー処理
Claude Desktopでの使用
claude_desktop_config.jsonを編集します。クロードデスクトップを開く
Settings」→Developer」→Edit Configへ移動します。以下のいずれかの構成を
mcpServersセクションに追加します。
変更を適用するには、Claude Desktopを再起動してください。
クエリの例
接続されると、AI アシスタントは次のようなクエリで DeepSource データを使用できるようになります。
What issues are in the JavaScript files of my project?これには、フィルター付きのproject_issuesツールが使用されます。
{
"projectKey": "your-project-key",
"path": "src/",
"analyzerIn": ["javascript"],
"first": 10
}分析実行をフィルタリングするには:
Show me the most recent Python analysis runsこれには、フィルター付きのproject_runsツールが使用されます。
{
"projectKey": "your-project-key",
"analyzerIn": ["python"],
"first": 5
}コード品質メトリクスの場合:
What's my code coverage percentage? Is it meeting our thresholds?これにはquality_metricsツールが使用されます。
{
"projectKey": "your-project-key",
"shortcodeIn": ["LCV", "BCV", "CCV"]
}セキュリティ コンプライアンス レポートの場合:
Are we compliant with OWASP Top 10 security standards?これには、 compliance_reportツールが使用されます。
{
"projectKey": "your-project-key",
"reportType": "OWASP_TOP_10"
}しきい値を設定するには:
Update our line coverage threshold to 80%これにはupdate_metric_thresholdツールが使用されます。
{
"projectKey": "your-project-key",
"repositoryId": "repo-id",
"metricShortcode": "LCV",
"metricKey": "AGGREGATE",
"thresholdValue": 80
}環境変数
サーバーは次の環境変数をサポートしています。
DEEPSOURCE_API_KEY(必須): 認証用のDeepSource APIキーLOG_FILE(オプション): ログを書き込むファイルへのパス。設定されていない場合はログは書き込まれません。LOG_LEVEL(オプション): 書き込むログの最小レベル (DEBUG、INFO、WARN、ERROR)。デフォルトはDEBUG
ログ記録を含む構成の例:
{
"mcpServers": {
"deepsource": {
"command": "npx",
"args": [
"-y",
"deepsource-mcp-server@1.1.0"
],
"env": {
"DEEPSOURCE_API_KEY": "your-deepsource-api-key",
"LOG_FILE": "/tmp/deepsource-mcp.log",
"LOG_LEVEL": "DEBUG"
}
}
}
}ドッカー
{
"mcpServers": {
"deepsource": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"DEEPSOURCE_API_KEY",
"-e",
"LOG_FILE=/tmp/deepsource-mcp.log",
"-v",
"/tmp:/tmp",
"sapientpants/deepsource-mcp-server"
],
"env": {
"DEEPSOURCE_API_KEY": "your-deepsource-api-key",
"LOG_FILE": "/tmp/deepsource-mcp.log"
}
}
}
}NPX
{
"mcpServers": {
"deepsource": {
"command": "npx",
"args": [
"-y",
"deepsource-mcp-server@1.1.0"
],
"env": {
"DEEPSOURCE_API_KEY": "your-deepsource-api-key"
}
}
}
}利用可能なツール
DeepSource MCP サーバーは次のツールを提供します。
projects: 利用可能なすべてのDeepSourceプロジェクトを一覧表示しますパラメータ:
必須パラメータはありません
project_issues: DeepSourceプロジェクトからフィルタリングして問題を取得するパラメータ:
projectKey(必須) - DeepSourceプロジェクトの一意の識別子ページネーションパラメータ:
offset(オプション) - ページ区切りでスキップする項目の数first(オプション) - 返されるアイテムの数 (デフォルトは 10)after(オプション) - 前方ページ送りのカーソルbefore(オプション) - 後方ページ送りのカーソルlast(オプション) - 'before' カーソルの前に返される項目の数 (デフォルト: 10)
フィルタリングパラメータ:
path(オプション) - 特定のファイルパスで問題をフィルタリングしますanalyzerIn(オプション) - 特定のアナライザーで問題をフィルタリングします (例: ["python", "javascript"])tags(オプション) - タグで問題をフィルタリングする
project_runs: DeepSourceプロジェクトの分析実行をフィルタリング付きで一覧表示するパラメータ:
projectKey(必須) - DeepSourceプロジェクトの一意の識別子ページネーションパラメータ:
offset(オプション) - ページ区切りでスキップする項目の数first(オプション) - 返されるアイテムの数 (デフォルトは 10)after(オプション) - 前方ページ送りのカーソルbefore(オプション) - 後方ページ送りのカーソルlast(オプション) - 'before' カーソルの前に返される項目の数 (デフォルト: 10)
フィルタリングパラメータ:
analyzerIn(オプション) - 特定のアナライザーによる実行をフィルタリングします (例: ["python", "javascript"])
run: runUid または commitOid で実行された特定の分析を取得します。パラメータ:
runIdentifier(必須) - 実行を識別するための runUid (UUID) または commitOid (コミットハッシュ)
recent_run_issues: ページネーションサポートを使用して、特定のブランチで実行された最新の分析から問題を取得します。パラメータ:
projectKey(必須) - DeepSourceプロジェクトの一意の識別子branchName(必須) - 最新の実行を取得するブランチ名ページネーションパラメータ:
first(オプション) - 返される問題の数 (デフォルトは 10)after(オプション) - 前方ページ送りのカーソルlast(オプション) - カーソルの前に返される問題の数 (デフォルト: 10)before(オプション) - 後方ページ送りのカーソル
戻り値:
ブランチでの最新の実行に関する情報
プロジェクトの現在の問題 (注: 問題はリポジトリ レベルであり、実行固有のものではありません)
カーソルやページステータスを含むページネーション情報
実行とブランチに関するメタデータ
dependency_vulnerabilities: DeepSource プロジェクトから依存関係の脆弱性を取得するパラメータ:
projectKey(必須) - DeepSourceプロジェクトの一意の識別子ページネーションパラメータ:
offset(オプション) - ページ区切りでスキップする項目の数first(オプション) - 返されるアイテムの数 (デフォルトは 10)after(オプション) - 前方ページ送りのカーソルbefore(オプション) - 後方ページ送りのカーソルlast(オプション) - 'before' カーソルの前に返される項目の数 (デフォルト: 10)
quality_metrics: フィルタリングを使用してDeepSourceプロジェクトから品質メトリクスを取得するパラメータ:
projectKey(必須) - DeepSourceプロジェクトの一意の識別子shortcodeIn(オプション) - 特定のショートコードでメトリックをフィルタリングします (例: ["LCV", "BCV"])
次のようなメトリックを返します。
ラインカバレッジ(LCV)
ブランチカバレッジ(BCV)
ドキュメントカバレッジ(DCV)
重複コード率(DDP)
各指標には、現在の値、しきい値、合格/不合格ステータスが含まれます。
update_metric_threshold: 特定の品質メトリックのしきい値を更新するパラメータ:
projectKey(必須) - DeepSourceプロジェクトの一意の識別子repositoryId(必須) - GraphQLリポジトリIDmetricShortcode(必須) - 更新するメトリックのショートコードmetricKey(必須) - メトリックの言語またはコンテキストキーthresholdValue(オプション) - 新しいしきい値、またはしきい値を削除する場合は null
例: 80% の行カバレッジしきい値を設定する: metricShortcode="LCV", metricKey="AGGREGATE", thresholdValue=80
update_metric_setting: 品質メトリックの設定を更新するパラメータ:
projectKey(必須) - DeepSourceプロジェクトの一意の識別子repositoryId(必須) - GraphQLリポジトリIDmetricShortcode(必須) - 更新するメトリックのショートコードisReported(必須) - メトリックを報告するかどうかisThresholdEnforced(必須) - しきい値を強制するかどうか (チェックに失敗する可能性があります)
compliance_report: DeepSourceプロジェクトからセキュリティコンプライアンスレポートを取得する
パラメータ:
projectKey(必須) - DeepSourceプロジェクトの一意の識別子reportType(必須) - 取得するコンプライアンスレポートの種類 ( OWASP Top 10 、 SANS Top 25 、またはMISRA-C )
以下を含む包括的なセキュリティ コンプライアンス データを返します。
カテゴリと重大度別のセキュリティ問題の統計
コンプライアンスステータス(合格/不合格)
時間の経過に伴う変化を示すトレンドデータ
セキュリティ体制を改善するための分析と推奨事項
発達
リポジトリをクローンします。
git clone https://github.com/sapientpants/deepsource-mcp-server.git
cd deepsource-mcp-server依存関係をインストールします:
pnpm installプロジェクトをビルドします。
pnpm run buildClaudeデスクトップの設定
{
"mcpServers": {
"deepsource": {
"command": "node",
"args": [
"/path/to/deepsource-mcp-server/dist/index.js"
],
"env": {
"DEEPSOURCE_API_KEY": "your-deepsource-api-key"
}
}
}
}前提条件
Node.js 20以上
pnpm 10.7.0以上
Docker(コンテナビルド用)
スクリプト
pnpm run build- TypeScriptコードをビルドするpnpm run start- サーバーを起動するpnpm run dev- 開発モードでサーバーを起動しますpnpm run test- テストを実行するpnpm run lint- ESLint を実行するpnpm run format- Prettier でコードをフォーマットする
トラブルシューティング
デバッグログを有効にする
問題が発生している場合は、デバッグ ログを有効にして詳細情報を確認してください。
LOG_FILE環境変数を、ログを書き込むファイルパスに設定します。LOG_LEVEL``DEBUGに設定する(これがデフォルトです)詳細なエラー情報についてはログファイルを確認してください
構成例:
{
"mcpServers": {
"deepsource": {
"command": "npx",
"args": ["-y", "deepsource-mcp-server@1.1.0"],
"env": {
"DEEPSOURCE_API_KEY": "your-api-key",
"LOG_FILE": "/tmp/deepsource-mcp.log",
"LOG_LEVEL": "DEBUG"
}
}
}
}次に、ログ ファイルを確認します。
tail -f /tmp/deepsource-mcp.logよくある問題
認証エラー:
DEEPSOURCE_API_KEYが正しく、必要な権限があることを確認してくださいログが表示されない:
LOG_FILEパスが書き込み可能であり、親ディレクトリが存在することを確認してくださいツールエラー: 詳細なエラーメッセージとスタックトレースをログファイルで確認してください
ライセンス
マサチューセッツ工科大学
Available Tools
10 toolscompliance_reportA
Get security compliance reports from a DeepSource project
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to identify the project | |
| reportType | Yes | Type of compliance report to fetch |
Output Schema
| Name | Required | Description |
|---|---|---|
| key | Yes | |
| title | Yes | |
| currentValue | Yes | |
| status | Yes | |
| securityIssueStats | Yes | |
| trends | No | |
| analysis | Yes | |
| recommendations | Yes |
TDQS
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 only says 'Get', implying a read operation, but does not disclose any side effects, permissions, rate limits, or output behavior beyond the schema.
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 sentence with no unnecessary words. It is front-loaded with the verb and resource, making it easy to parse.
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?
Given the presence of an output schema and 100% parameter coverage, the description is minimally adequate. However, it lacks behavioral transparency and usage context, which are not compensated by other fields.
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 coverage is 100% – both 'projectKey' and 'reportType' are described in the input schema. The description adds no additional semantic information, so baseline 3 is appropriate.
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 the verb 'Get' and the resource 'security compliance reports from a DeepSource project'. It distinguishes this tool from siblings like 'dependency_vulnerabilities' and 'project_issues', which focus on different data.
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?
No explicit guidance on when to use this tool versus alternatives. The description implies it's for compliance reports but does not specify when not to use it or mention other tools for similar purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dependency_vulnerabilitiesB
Get dependency vulnerabilities from a DeepSource project
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to fetch vulnerabilities for | |
| first | No | Number of items to retrieve (forward pagination) | |
| after | No | Cursor to start retrieving items after (forward pagination) | |
| last | No | Number of items to retrieve (backward pagination) | |
| before | No | Cursor to start retrieving items before (backward pagination) | |
| page_size | No | Number of items per page (alias for first, for convenience) | |
| max_pages | No | Maximum number of pages to fetch (enables automatic multi-page fetching) |
Output Schema
| Name | Required | Description |
|---|---|---|
| vulnerabilities | Yes | |
| pageInfo | Yes | |
| totalCount | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states a read operation but omits details like pagination behavior, potential errors, or authentication needs, despite the schema hinting at pagination.
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 with no wasted words. However, it is very brief and could be restructured to include more context efficiently.
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?
Given the complexity (7 parameters, output schema, no annotations), the description is too minimal. It fails to explain the tool's purpose in a broader workflow or set expectations about pagination and project key usage.
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 coverage is 100%, so the baseline is 3. The description adds no extra meaning beyond what the schema already provides for each parameter.
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 the action ('Get'), the resource ('dependency vulnerabilities'), and the scope ('from a DeepSource project'). This distinctly differentiates it from sibling tools like compliance_report or project_issues.
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?
No guidance is provided on when to use this tool versus alternatives. The description lacks any context about prerequisites, exclusions, or comparative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
project_issuesB
Get issues from a DeepSource project with filtering capabilities
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to fetch issues for | |
| path | No | Filter issues by file path | |
| analyzerIn | No | Filter issues by analyzer shortcodes | |
| tags | No | Filter issues by tags | |
| first | No | Number of items to retrieve (forward pagination) | |
| after | No | Cursor to start retrieving items after (forward pagination) | |
| last | No | Number of items to retrieve (backward pagination) | |
| before | No | Cursor to start retrieving items before (backward pagination) | |
| page_size | No | Number of items per page (alias for first, for convenience) | |
| max_pages | No | Maximum number of pages to fetch (enables automatic multi-page fetching) |
Output Schema
| Name | Required | Description |
|---|---|---|
| issues | Yes | |
| pageInfo | Yes | |
| pagination | No | User-friendly pagination metadata |
| totalCount | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose pagination behavior, rate limits, or what the response contains. Without annotations, the description fails to inform about key behavioral traits.
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?
Single sentence, front-loaded, but too brief; could be expanded to include key details without becoming verbose.
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?
With 10 parameters including pagination, the description is insufficient; doesn't explain pagination cursor usage or filtering capabilities beyond the schema.
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?
Input schema has 100% description coverage, so baseline is 3. Description adds no extra meaning beyond what the schema already provides.
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?
Description clearly states verb 'Get' and resource 'issues from a DeepSource project', distinguishing it from sibling tools like compliance_report and dependency_vulnerabilities.
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?
No guidance on when to use this tool vs. alternatives, such as other issue-related tools (e.g., recent_run_issues). Lacks explicit context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
projectsA
List all available DeepSource projects. Returns a list of project objects with "key" and "name" properties.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| projects | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility. It transparently describes a read-only listing operation with no side effects. While simple, it fully discloses the behavior without omission.
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, well-structured sentence that front-loads the action ('List all available DeepSource projects') and follows with concise details. No extraneous words.
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?
Given zero parameters, an output schema, and a straightforward task (listing projects), the description is complete. It covers the purpose and return format, and no additional context is needed.
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?
Since the input schema has no parameters (100% coverage), the description adds value by specifying the return structure (key and name properties). This exceeds the baseline of 4 for zero-parameter tools.
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 it lists all available DeepSource projects and specifies the return properties (key and name). This distinguishes it from sibling tools like compliance_report which focus on specific aspects.
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?
No guidance is provided on when to use this tool versus alternatives such as compliance_report or project_issues. The description simply states the functionality without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
quality_metricsB
Get quality metrics from a DeepSource project with optional filtering
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to fetch quality metrics for | |
| shortcodeIn | No | Optional filter for specific metric shortcodes |
Output Schema
| Name | Required | Description |
|---|---|---|
| metrics | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It only indicates a read operation ('Get') but does not disclose side effects, authentication requirements, rate limits, or any behavioral traits beyond that.
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 of 11 words. It is highly concise with no superfluous information, earning a top score.
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?
The tool is simple with 2 parameters and an output schema, so the description partially covers what is needed. However, it lacks context on prerequisites, how quality metrics relate to other sibling tools, or any performance implications. With output schema present, return values are covered, but overall completeness is average.
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 parameters with descriptions, so the description adds minimal value ('optional filtering' is already implied by shortcodeIn). Baseline 3 is appropriate since the schema already explains the parameters 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 the verb 'Get', the resource 'quality metrics', and the source 'DeepSource project'. It also mentions optional filtering, which adds clarity. However, it does not elaborate on what 'quality metrics' entail (e.g., code quality metrics), missing an opportunity to differentiate from siblings like dependency_vulnerabilities or project_issues.
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?
No guidance is provided on when to use this tool versus its siblings. The description mentions optional filtering but does not explain when filtering is appropriate or when alternative tools (e.g., for issues or vulnerabilities) should be used instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recent_run_issuesB
Get issues from the most recent analysis run on a specific branch
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to fetch issues for | |
| branchName | Yes | Branch name to fetch the most recent run from | |
| first | No | Number of items to retrieve (forward pagination) | |
| after | No | Cursor to start retrieving items after (forward pagination) | |
| last | No | Number of items to retrieve (backward pagination) | |
| before | No | Cursor to start retrieving items before (backward pagination) | |
| page_size | No | Number of items per page (alias for first, for convenience) | |
| max_pages | No | Maximum number of pages to fetch (enables automatic multi-page fetching) |
Output Schema
| Name | Required | Description |
|---|---|---|
| run | Yes | |
| issues | Yes | |
| pageInfo | Yes | |
| totalCount | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. However, it only states a read-like operation without mentioning it is read-only, does not discuss authentication, rate limits, or pagination behavior. The description adds minimal value beyond the tool name.
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 sentence of 9 words, extremely concise and front-loaded. Every word is necessary, and there is no redundant information.
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?
Given the complexity of 8 parameters including pagination, and the presence of an output schema, the description is minimal. It does not explain that only the latest run is considered, nor does it describe the order or filtering. Adequate for basic understanding but incomplete for nuanced usage.
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%, so the input schema already explains all parameters. The description does not add new meaning to any parameter beyond what the schema provides. Baseline score is 3 as description provides no additional semantic value.
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 it retrieves issues from the most recent analysis run on a specific branch. The verb 'Get' and resource 'issues' are specific, and the scope 'most recent analysis run on a specific branch' distinguishes it from siblings like 'project_issues' which likely list all issues.
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 provides no guidance on when to use this tool versus alternatives such as 'project_issues' or 'runs'. It does not mention when not to use it or provide context about prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
runB
Get a specific analysis run by its runUid or commitOid
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to identify the project | |
| runIdentifier | Yes | The run identifier (runUid or commitOid) | |
| isCommitOid | No | Flag to indicate whether the runIdentifier is a commitOid (default: false) |
Output Schema
| Name | Required | Description |
|---|---|---|
| run | Yes | |
| analysis | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a read-only operation ('Get'), which is consistent with the expected behavior. With no annotations provided, the description adequately conveys that it is a retrieval tool, but it does not disclose error handling or behavior when the run is not found.
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 conveys the essential purpose. No unnecessary words, making it easy for the agent to parse quickly.
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?
With an output schema present, the description does not need to explain return values. The description is sufficient for a simple getter tool, though it could be improved by mentioning that it returns a single run object. Overall, it is reasonably complete for the tool's complexity.
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 coverage is 100%, so the schema already documents the parameters. The description adds minimal value by restating that runIdentifier can be runUid or commitOid, but this is already encoded in the schema and the isCommitOid parameter. No additional semantic detail beyond the schema.
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 the tool retrieves a specific analysis run using either runUid or commitOid. The verb 'Get' and the resource 'analysis run' are explicit. However, it does not explicitly differentiate from the sibling tool 'runs', which likely lists all runs, so clarity is good but not perfect.
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?
No guidance on when to use this tool versus alternatives like 'runs' or other tools. There is no mention of prerequisites or context, leaving the agent to infer usage from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
runsA
List analysis runs for a DeepSource project with filtering
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to fetch runs for | |
| analyzerIn | No | Filter runs by analyzer shortcodes | |
| first | No | Number of items to retrieve (forward pagination) | |
| after | No | Cursor to start retrieving items after (forward pagination) | |
| last | No | Number of items to retrieve (backward pagination) | |
| before | No | Cursor to start retrieving items before (backward pagination) | |
| page_size | No | Number of items per page (alias for first, for convenience) | |
| max_pages | No | Maximum number of pages to fetch (enables automatic multi-page fetching) |
Output Schema
| Name | Required | Description |
|---|---|---|
| runs | Yes | |
| pageInfo | Yes | |
| totalCount | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It does not mention pagination behavior, rate limits, or what happens on invalid projects. The schema includes cursor parameters (first, after, last, before, page_size, max_pages), but the description omits any behavioral context like automatic pagination or cursor-based pagination.
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?
A single, clear sentence with no fluff. Every word is necessary and earns its place.
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?
Given 8 parameters and no annotations, the description is minimal but combined with the schema is adequate. However, it lacks context on pagination behavior and does not differentiate from sibling tool 'run'. Output schema exists to describe return values.
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 coverage is 100% with all parameters described. The description adds only 'with filtering', which is vague and does not provide additional meaning beyond the schema. Baseline score of 3 is appropriate as schema does the heavy lifting.
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?
Description clearly states the tool lists analysis runs for a DeepSource project with filtering. It uses a specific verb (list) and resource (analysis runs), and distinguishes from sibling tools like 'run' (likely single run retrieval) and 'project_issues'.
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?
Description implies usage for listing filtered runs, but does not explicitly state when not to use or mention alternatives. Context is clear, but no exclusions or comparisons to siblings are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_metric_settingC
Update the settings for a quality metric
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to identify the project | |
| repositoryId | Yes | Repository GraphQL ID | |
| metricShortcode | Yes | Code for the metric to update | |
| isReported | Yes | Whether the metric should be reported | |
| isThresholdEnforced | Yes | Whether the threshold should be enforced |
Output Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| projectKey | Yes | |
| metricShortcode | Yes | |
| settings | Yes | |
| message | Yes | |
| next_steps | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It only says 'Update', implying mutation, but fails to state side effects, authorization requirements, or error conditions (e.g., what happens if the metric doesn't exist). This is insufficient for an agent to safely invoke the 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 with no unnecessary words. It is front-loaded with the key verb and resource, making it efficient for scanning.
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?
Despite the existence of an output schema, the description is too minimal. It does not explain the broader context of updating metric settings, such as the effect on reporting or enforcement, or how it relates to other metric tools. The five required parameters are left unexplained beyond the schema, which is insufficient for a complete understanding.
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 coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema; it merely restates the generic 'settings' without explaining how the boolean parameters affect the metric. The agent must rely entirely on the parameter descriptions in the schema.
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 the action ('Update') and the resource ('settings for a quality metric'), which is sufficient to understand the basic purpose. However, it does not distinguish from the sibling tool 'update_metric_threshold', which might update a specific threshold value, so specificity is slightly lacking.
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?
No guidance is provided on when to use this tool versus alternatives like 'update_metric_threshold'. The description does not mention context, prerequisites, or scenarios where this tool is appropriate, leaving the agent to infer usage from the schema alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_metric_thresholdC
Update the threshold for a specific quality metric
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to identify the project | |
| repositoryId | Yes | Repository GraphQL ID | |
| metricShortcode | Yes | Code for the metric to update | |
| metricKey | Yes | Context key for the metric | |
| thresholdValue | No | New threshold value, or null to remove |
Output Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| projectKey | Yes | |
| metricShortcode | Yes | |
| metricKey | Yes | |
| thresholdValue | No | |
| message | Yes | |
| next_steps | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the full burden of disclosing behavioral traits. It only states 'update' which implies mutation but does not mention authorization needs, idempotency, side effects on other metrics, or whether setting threshold to null removes it. The description adds minimal value beyond the tool name.
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 with only 8 words, containing no fluff or repetition. It efficiently communicates the core purpose.
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?
Given the complexity of the tool (5 parameters, 4 required, mutation with no annotations) and the presence of an output schema, the description is too brief. It lacks details on the effect of null thresholdValue, the meaning of metricShortcode values, and the expected outcome. The existing output schema partially mitigates the need for return value explanation, but the description should provide more operational context.
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 already provides full descriptions for all 5 parameters (100% coverage). The description does not add any additional meaning or context beyond what is in the schema, so baseline score of 3 is appropriate.
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?
Description clearly states the action (update) and the object (threshold for a specific quality metric). It is specific enough to distinguish from sibling tools like update_metric_setting or quality_metrics, though it does not explicitly call out the distinction.
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?
No guidance is provided on when to use this tool versus alternatives, nor are there any prerequisites or when-not-to-use conditions stated. The description simply states what it does without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Scored across 10 tools
Most tools have clearly distinct purposes (projects vs runs vs issues vs metrics vs security). However, project_issues and recent_run_issues both deal with issues and could cause initial confusion, though descriptions clarify the difference.
Tool names follow a predictable pattern: query tools are named after the resource (noun or noun phrase, e.g., projects, runs, quality_metrics) and mutation tools use verb_resource (e.g., update_metric_setting). This is consistent and readable.
10 tools is well-scoped for a code analysis server, covering core areas (projects, runs, issues, metrics, compliance, dependencies) without being overwhelming or too sparse.
The tool set covers most essential operations (list, get, update for key resources) but lacks branch listing (needed for recent_run_issues) and triggering analysis runs, which are notable gaps for a comprehensive interface.
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