AIエージェントがMCPツールに費やすトークンを最大98%削減——何も失うことなく。
サーバー | ツール数 | 圧縮前 | 圧縮後 | 削減率 |
filesystem (Anthropic) | 14 | 1,892 tok | 236 tok | −88% |
sequential-thinking | 1 | 926 tok | 20 tok | −98% |
Google Calendar | 5 | 5,010 tok | 660 tok | −87% |
Enterprise (Cal + Gmail + Drive) | 12 | 8,649 tok | 882 tok | −90% |
sample_app.js (JavaScript) | — | 799 tok | 284 tok | −64.5% |
sample_app.ts (TypeScript) | — | 378 tok | 266 tok | −29.6% |
ast_extractor.py (Python) | — | 3,633 tok | 890 tok | −75.5% |
送信トークンが減れば、APIコストの低下と応答の高速化。そして何も失われません。すべての確認で、圧縮後もツールは100%使用可能な状態が保たれています。
これらの数値はご自身でも再現可能です。すべての入力はリポジトリ内の静的フィクスチャであり、トークンはtiktoken cl100k_baseでカウントしています。
python benchmarks/run_benchmark.py # the table above
python benchmarks/run_benchmark.py --json # machine-readable
python benchmarks/run_benchmark.py --fixture path/to/your_schemas.jsonRelated MCP server: Bifrost-MCP Gateway
ワンライナー（macOS / Linux）——パッケージをインストールし、Claude Desktopを設定します。
curl -sSL https://raw.githubusercontent.com/LoudiliMed/Refract/main/install-refract.sh | shインターネットからシェルにスクリプトをパイプで流し込む前に、一読をお勧めします：install-refract.shをGitHubで確認。sudoは使用しません。
pipで直接インストールする場合：
pip install refract-mcpオプションの追加パッケージ：
pip install refract-mcp[semantic] # semantic tool routing with embeddings
pip install refract-mcp[multilang] # JavaScript, TypeScript, JSX, TSX supportエージェントと任意のMCPサーバーの間に配置されます。ツールスキーマをオンザフライで圧縮し、エージェントが毎回すべてのカタログを読み込む必要をなくします。
ローカルサブプロセス（stdio）：
refract-proxy --target "npx @modelcontextprotocol/server-filesystem /tmp" --verboseリモートHTTP/SSEサーバー：
# --url implies SSE transport (explicit, recommended for remote endpoints)
refract-proxy --url https://my-mcp-server.com/sse
# or with --transport flag (auto-detection can be overridden)
refract-proxy --target https://my-mcp-server.com/sse --transport sseプロキシフラグ：
フラグ | デフォルト | 説明 |
| 必須 | MCPターゲット：stdioコマンド、HTTP URL、またはJSONファイル |
| — | stdioコマンド用の |
| — | リモートSSE/HTTPエンドポイント—— |
| auto | ターゲットへのトランスポートを強制： |
| 30 | SSEターゲットの接続タイムアウト（3回リトライ） |
| stdio | プロキシがエージェントにサービスを提供する方法 |
| 8080 |
|
| off | リクエストごとのトークン数を表示 |
| WARNING | DEBUG / INFO / WARNING / ERROR |
Claude Desktopに追加：
{
"mcpServers": {
"my-server-via-refract": {
"command": "/path/to/refract-proxy",
"args": [
"--target",
"npx @modelcontextprotocol/server-filesystem /path/to/folder",
"--verbose"
]
}
}
}リモートMCPサーバー（SSE）の場合：
{
"mcpServers": {
"remote-via-refract": {
"command": "/path/to/refract-proxy",
"args": ["--url", "https://my-mcp-server.com/sse"]
}
}
}エントリを一つずつ編集する代わりに（またはサーバーごとにrefract-installを実行する代わりに）、refract-wrap-allはclaude_desktop_config.json内のすべてのstdioサーバーを一度のコマンドでrefract-proxy経由に書き換えます：
# Preview what would change — writes nothing
refract-wrap-all --dry-run
# Wrap every stdio server not already going through refract
refract-wrap-all
# Restore the original commands
refract-wrap-all --unwrap例：このエントリ
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": ["@modelcontextprotocol/server-filesystem", "/tmp"],
"env": {"MY_VAR": "1"}
}
}
}は次のようになります：
{
"mcpServers": {
"filesystem": {
"command": "/path/to/refract-proxy",
"args": ["--stdio-cmd", "npx @modelcontextprotocol/server-filesystem /tmp"],
"env": {"MY_VAR": "1"}
}
}
}保証事項：
設定ファイルのバックアップは書き込み前に必ず作成されます（claude_desktop_config.json.bak、次に.bak2、.bak3……既存のバックアップは上書きされません）。
env、cwdおよびその他のサーバーフィールドは保持されます。
すでにrefract-proxyまたはrefract-server経由のサーバーはスキップされます（already wrapped）。
リモートSSE/HTTPサーバー（urlエントリ）はスキップされます——ラップされるのはstdioサーバーのみです。
--unwrapは完全なラウンドトリップです：ラップしてからアンラップすると、元のコマンド、引数、およびすべてのフィールドが復元されます。
フラグ | 値 | 説明 |
| Streamable HTTP | 現在の標準（MCP仕様 2025-03-26）。リモートMCPサーバーで使用します。 |
| SSE | レガシートランスポート、互換性のために維持。Streamable HTTPをサポートしないサーバーで使用。 |
| stdio subprocess | ローカルコマンド（ |
(省略) | 自動検出 |
|
sseとhttpの両方とも、--targetにHTTP(S) URLが必要です。
# Connect to a remote MCP server via Streamable HTTP (recommended)
refract-proxy --target "https://my-mcp-server.com/mcp" --transport http
# Connect via SSE (legacy)
refract-proxy --target "https://my-mcp-server.com/sse" --transport sse
# Local subprocess (auto-detected, --transport stdio optional)
refract-proxy --target "npx @modelcontextprotocol/server-filesystem /tmp"コードベースをMCPサーバーとして公開します。エージェントはリポジトリのインデックス作成、圧縮ファイルコンテキストの取得、特定の関数の展開、影響分析、破壊的変更の検出、セキュリティリスクのマッピングが可能です。
refract-server --root /path/to/your/repoClaude Desktopに追加：
{
"mcpServers": {
"refract-code": {
"command": "/path/to/refract-server",
"args": ["--root", "/path/to/your/repo"]
}
}
}50冊の本がある図書館を想像してください。
Refractなし：エージェントは、答えにたった1冊しか必要ない場合でも、すべての質問に対して50冊すべての本の詳細な要約を受け取ります。
Refractあり：エージェントは最初にタイトルのリスト（インデックス）を取得します。どの本が必要か分かると、その本の内容だけを受け取ります。
技術的には：
インデックス（常に送信）：ツール名と各ツールの短い説明のみ。
詳細（必要なときのみ送信）：実際に使用されるツールの完全な説明、それを正しく使用するために必要なすべての情報、それ以上は何もありません。
検証：圧縮後、Refractは自動的に重要な情報が削除されていないかを確認します。疑わしい場合は、リスクを冒さずに完全版を送信します。
このプロセスにはAIモデルは関与しません。完全に自動化、高速、かつ決定論的です。
ツール | 機能 |
圧縮 | ツールスキーマをオンザフライで最大98%削減 |
シグナルチェック | 圧縮後、呼び出し可能な契約を検証 |
セマンティックルーティング | 埋め込みを使用して適切なツールを特定（オプトイン） |
プロンプトキャッシング | 繰り返しリクエストに対してAnthropic cache_controlを注入 |
ツール | 入力 | 出力 |
index_repo | リポジトリパス | すべてのPython、JS、TSファイルの集約インデックス |
get_compressed | ファイルパス | 圧縮された構造＋トークン統計 |
expand | ファイルパス＋関数名 | 逐語的なソース＋依存関係コンテキスト |
blast_radius | ファイルパス＋関数名 | ターゲットが変更された場合に壊れるすべての関数 |
semantic_diff | ファイルパス＋旧ソース＋新ソース | 破壊的変更 vs 本体のみの変更 |
semantic_diff_branches | リポジトリパス＋ファイル＋関数＋ベース/ヘッドgit参照 | 2つのブランチ/コミット間の1関数のsemantic_diff |
security_surface | リポジトリパス | 危険な呼び出しのマップ（subprocess、eval、pickle、requests） |
refract-status --root /path/to/repo
refract-status --root /path/to/repo --jsonフラグ | 説明 |
| 分析するパス（デフォルト：カレントディレクトリ） |
| 機械可読な出力 |
表示内容：言語別ファイル数、生トークンと圧縮トークンの比較、インデックス化された関数/クラス、カテゴリ別の危険な呼び出し、tree-sitter未対応の言語。
ターゲット関数を変更した場合に壊れる関数をClaudeに問い合わせます。
結果の例：
{
"target": "authenticate",
"direct_callers": ["login_user"],
"all_impacted": ["login_user", "verify_session", "admin_access"],
"impacted_count": 3,
"risk_level": "MEDIUM"
}リスクレベル：LOW（影響0〜2）、MEDIUM（3〜5）、HIGH（6以上）。
本体ではなく関数インターフェースを比較することで、破壊的なAPI変更を検出します。CIゲートとして使用できます。
結果の例：
{
"breaking": ["authenticate"],
"body_only": ["logout"],
"added": ["new_function"],
"removed": [],
"unchanged": ["hash_password"],
"is_breaking": true
}is_breakingがtrueの場合、PRは公開APIを変更しているため、レビューが必要です。
リポジトリ全体で、危険なプリミティブを呼び出すすべての関数をマッピングします。
HIGHリスク：subprocess、os.system、eval、exec、pickle、ctypes
MEDIUMリスク：open（書き込みモード）、socket、requests、httpx、urllib
結果の例：
{
"high_risk": [
{
"file": "src/llm_client.py",
"function": "run_command",
"calls": ["subprocess.run"]
}
],
"summary": {
"high_risk_count": 1,
"medium_risk_count": 3,
"total_functions_scanned": 87,
"clean_files": 8
}
}Python（astモジュール経由）、JavaScript、TypeScript、JSX、TSX（tree-sitter経由、pip install refract-mcp[multilang]でオプトイン）。
言語はファイル拡張子から自動検出されます。tree-sitterがインストールされていない場合は、適切にフォールバックします。
RefractはAnthropicのプロンプトキャッシングと統合されています。as_anthropic_tools()は自動的に圧縮カタログをキャッシュ可能としてマークし、繰り返しリクエストのコストをさらに削減します。
30日間、1日100リクエスト、5,000トークンのスキーマの場合の例：
シナリオ | コスト |
Refractなし、キャッシュなし | $45.00 |
Refract + キャッシュあり | $1.49 |
Claude Desktopで「Failed to spawn process: No such file or directory」
Claude DesktopがPATH内でrefract-proxyを見つけられません。絶対パスを特定して直接使用してください：
which refract-proxyその後、claude_desktop_config.jsonでフルパスを使用します：
{
"mcpServers": {
"my-tool-via-refract": {
"command": "/full/path/to/refract-proxy",
"args": [
"--target",
"npx @modelcontextprotocol/server-filesystem /path/to/folder"
]
}
}
}Claude Desktop、Cursor、MCP標準に準拠した任意のクライアント、既存の任意のMCPサーバー。
MIT — 商用利用を含め、自由に使用できます。
Reverse-call-graph impact analysis for a Python function: BFS over inverted call edges to find every function transitively affected by changing target_function. Returns direct_callers, all_impacted, impacted_count, total_functions and a risk_level. Python files only.
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Path to a .py file (relative to --root or absolute). | |
| target_function | Yes | Function/method name to analyze the blast radius of. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: BFS algorithm, inverted call edges, returns specific fields (direct_callers, all_impacted, etc.), and scope (Python files only). No hidden side effects or destructive actions are implied.
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 two sentences, front-loads the purpose, and includes all essential information without verbosity. Every sentence 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 no annotations or output schema, the description covers purpose, algorithm, input parameters, return fields, and file type restriction. It is complete for an agent to understand and use this tool correctly.
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 params described), so baseline is 3. The description adds value by explaining the algorithm context (BFS over inverted call edges) and linking the parameters to the analysis process, though the schema already defines them clearly.
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 'Reverse-call-graph impact analysis for a Python function', specifying the verb (analyze impact), resource (Python function), and methodology (BFS over inverted call edges). It distinguishes from sibling tools like index_repo and security_surface which have different purposes.
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 implicitly defines usage: when you need to know which functions are affected by changing a target function in a Python file. It does not explicitly compare with siblings, but the context is clear. The restriction 'Python files only' provides a clear constraint, earning a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given function/class names in a .py file, return them verbatim (full source) plus their compressed dependency context.
| Name | Required | Description | Default |
|---|---|---|---|
| targets | Yes | Function/class names to expand. | |
| file_path | Yes | Path to a .py file (relative to --root or absolute). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so description carries full burden. It states it returns 'full source' and 'compressed dependency context', which is a key behavioral trait. However, it does not disclose read-only nature, auth requirements, or potential performance implications (e.g., large files). Adequate but not comprehensive.
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 with purpose. No wasted words; all information is necessary and directly conveys the tool's action and output.
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 that there is no output schema and no annotations, the description is somewhat vague about the exact format of 'compressed dependency context'. While the tool is simple, the description could be more complete by clarifying what 'compressed' means. Sufficient but leaves room for ambiguity.
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 schema already documents both parameters adequately. The tool description adds no new meaning beyond restating the schema descriptions, meeting the baseline but not adding 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?
Description uses specific verb 'expand' on function/class names in a .py file, clearly indicating it returns full source plus dependency context. Differentiates from siblings like 'get_compressed' (which likely returns compressed content) and 'blast_radius' (impact analysis).
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 like 'get_compressed' or 'blast_radius'. Usage is implied by the description (to get full source of specific definitions with dependencies), but lacks when-not-to-use or prerequisite information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
S5-compress a single source file — Python or JS/TS (signatures + dependency contracts, bodies stripped) — and return the compressed structure plus token stats (tokens_before, tokens_after, reduction_pct).
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Path to a .py file (relative to --root or absolute). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behaviors. It does not state whether the operation is read-only or modifies files, nor does it mention side effects, auth requirements, or rate limits. The word 'compress' might imply mutation, but the return of a 'compressed structure' suggests the original file is unchanged. This ambiguity is a 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 well-structured sentence. It front-loads the action ('S5-compress a single source file'), specifies file types, details the compression behavior, and lists the output. No unnecessary 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?
Without output schema, the description should explain the return value structure, but it does mention 'compressed structure plus token stats (tokens_before, tokens_after, reduction_pct),' which is helpful. However, it lacks behavioral context (read/write), does not resolve the file type inconsistency, and does not address how this relates to sibling tools like 'expand.' The minimal viable information is present, but gaps reduce completeness.
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% (one parameter with description). The description adds that the tool supports Python and JS/TS, but the schema's parameter description says 'Path to a .py file,' which contradicts the broader file types stated in the tool description. This inconsistency misleads the agent and detracts from the value added 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 compresses a single source file (Python or JS/TS) by stripping bodies while keeping signatures and dependency contracts, and returns token stats. The verb 'compress' and resource 'source file' are specific, and it distinguishes from siblings like 'blast_radius' or 'expand' implicitly.
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 does not mention when to choose compression over expansion or other sibling tools, leaving the agent without decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Walk a repo (Python + JavaScript/TypeScript) and return an aggregated structural index: every function, class, import and dependency. Max depth 3; skips pycache, .git, venv, node_modules.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Repo path (relative to --root or absolute). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses maximum depth 3 and skipped directories (__pycache__, .git, venv, node_modules), but fails to mention behavior for unsupported file types, invalid paths, or performance implications for large repos. Additional context would improve transparency.
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 two concise sentences with front-loaded purpose and clear constraints. Every word adds value, no redundancy.
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 no output schema, the description partially explains return value as 'aggregated structural index' listing functions, classes, imports, dependencies. However, it lacks details on output format (e.g., JSON structure, list vs. dict) and does not address edge cases like empty repos or mixed languages. It is acceptable but not fully complete.
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 a single required 'path' parameter described adequately. The description adds no extra parameter details beyond the schema, so baseline 3 is appropriate. The description's main value is in explaining the tool's overall function, not parameter specifics.
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 walks a repo and returns a structural index of functions, classes, imports, and dependencies, explicitly specifying supported languages (Python and JavaScript/TypeScript). This distinguishes it from sibling tools like 'blast_radius' or 'security_surface', which likely have different purposes.
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 does not provide explicit guidance on when to use this tool versus alternatives like 'blast_radius' or 'expand'. It only implies usage for structural indexing, but lacks when-to-use, when-not-to-use, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Walk a Python repo and find functions that call dangerous primitives — pure AST analysis, zero LLM calls. Classifies calls as HIGH risk (subprocess/os.system/eval/exec/pickle/import/ctypes …) or MEDIUM risk (sockets/requests/httpx/urllib/paramiko/smtplib and write-mode open()). Also scans for potential secrets: hardcoded keys/tokens (AKIA…, sk-…, ghp_…, token=/password=/api_key= assignments), .env/environment loading, and high-entropy token-looking literals. Returns high_risk, medium_risk, secrets, clean files and a summary. Max depth 3; skips pycache, .git, venv, node_modules.
| Name | Required | Description | Default |
|---|---|---|---|
| repo_path | Yes | Repo path (relative to --root or absolute). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It clearly states pure AST analysis, zero LLM calls, risk classification levels, secret detection, max depth 3, and skipped directories. Could mention whether the tool is read-only (likely yes) but overall quite transparent.
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?
Description is front-loaded with the main action and packed with relevant details in three sentences. Minimal redundancy, though length could be trimmed slightly without losing meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description adequately explains return values (high_risk, medium_risk, secrets, clean files, summary). Covers key behaviors and limitations. Could mention file type filtering but sufficient for an agent to understand scope.
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?
Only one parameter (repo_path) with 100% schema coverage. Description adds no extra detail beyond the schema's description of the path. Baseline score 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 it walks a Python repo using AST analysis to find dangerous function calls and secrets, with specific risk classifications. No sibling tool overlaps, making its unique purpose unmistakable.
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 when to use (security scanning of Python repos) but does not explicitly state when not to use or mention alternatives among siblings. More precise guidance would improve this dimension.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.7.0blast_radiusexpandget_compressedindex_reposecurity_surfaceScored across 5 tools
Each tool has a distinct purpose: impact analysis, source retrieval, file compression, repo indexing, and security scanning. No two tools overlap in functionality.
All tools use snake_case, but 'expand' is a lone verb while others follow a verb_noun pattern (e.g., get_compressed, index_repo). Minor inconsistency but still clear.
5 tools is a well-scoped set for a static analysis toolkit. Neither too few nor too many, each tool serves a clear need.
Covers core static analysis needs (impact, source, compression, indexing, security). Missing a dedicated diff or test analysis tool, but the surface is reasonably complete for the domain.
The OpenRouter for tools. One MCP connection gives any AI agent 254 hosted tools, pay per call.
One MCP endpoint for Claude, GPT & Gemini: 100+ tools + no-code connectors + agent workers.
Security & DLP proxy for MCP: tool-poisoning scans, PII redaction on tool args/results. Beta.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
