OpenClaw用のトークンコストテレメトリ。ClaudeやMCP対応エージェントからクエリ可能です。 エージェント別・プロバイダー別のコスト配分、コスト急増の異常検知、モデルルーティングの推奨、30日間の予測機能を提供します。silentwatch-mcpおよびopenclaw-health-mcpのコンパニオンツールです。
AIの本番環境では、複数のプロバイダー、数十のエージェント、数百のスキル呼び出しにわたってトークンコストが発生します。月末に請求書が届く頃には、「どこでコストがかかったのか？」という問いへの答えは10万行のログの中に埋もれてしまいます。このMCPサーバーは、そのデータをライブで可視化し、Claude内から平易な英語でクエリできるようにします。
> claude: where did our LLM spend go this week?
[MCP tools: cost_overview + top_cost_drivers]
Total spend last 7 days: $42.18
By provider:
Anthropic $30.40 (72%) — claude-sonnet-4 dominates
OpenAI $9.20 (22%) — chat-bot agent
Gemini $1.86 (4%) — cron-summarizer (cheap-route working)
Ollama $0.00 (local, free)
Top 3 cost drivers:
data-extraction-agent $28.50 (68%)
chat-bot $9.20 (22%)
cron-summarizer $1.86 (4%)
1 anomaly flagged — see find_cost_anomalies for details.> claude: any cheaper-routing opportunities?
[MCP tool: model_routing_recommendations]
Recommendation: data-extraction-agent currently runs claude-sonnet-4
with avg 400-token completions — extraction-style work that
gemini-2.5-flash usually handles at ~95% quality for ~5% the cost.
Estimated savings: $27.10/30d if migrated. Test on a 10% slice first.Related MCP server: nikhilnt
openclaw-cost-tracker-mcp を選ぶ理由既存のツール（プロバイダーの請求ダッシュボード、汎用FinOpsツール、カスタムスクリプト）では対応できない3つの機能を提供します。
プロバイダー単位の合計だけでなく、エージェント単位でのコスト配分。 プロバイダーのダッシュボードには「Anthropicで$X使用」と表示されますが、6つのエージェントのうちどれがその78%を占めているかは分かりません。Cost trackerはOpenClawのリクエストごとのコストログJSONLを読み込み、agent_idを保持したまま集計します。
エージェントごとのコスト急増の異常検知。 チャットに12万トークンのテキストを貼り付けるだけで、通常の1週間分のトラフィック以上のコストがかかることがあります。デフォルトの「エージェントごとの中央値の3倍」というしきい値により、月末の請求書に載る前にそれらをフラグ立てします。
実際の使用量に基づいたルーティングの推奨。 「より安いモデルを使う」といった一般的なアドバイスは役に立ちません。このサーバーは、ボリュームと完了トークン数のパターンから、より安価なプロバイダーでも同じ結果が得られる可能性が高い「特定のエージェント」を特定し、具体的な30日間の節約額を提示します。
OpenClawを本番環境で運用し、実際のコストを管理している本番AIオペレーターのために構築されました。
ツール | 戻り値 |
| 指定期間の合計支出、プロバイダー別・エージェント別・モデル別のトップ、異常数 |
| エージェント別の内訳（リクエストあたりの平均コスト、全体に占める割合） |
| プロバイダー別の内訳（トークン数） |
| エージェントの中央値の3倍以上のコストがかかったリクエスト |
| コストの高いエージェントおよびモデルのトップN |
| 具体的な安価なモデルの提案と30日間の節約予測 |
| 30日間の合計およびプロバイダー別の予測（信頼度付き） |
リソース:
cost://overview — 7日間のスナップショット
cost://forecast — 30日間の予測
cost://anomalies — 最近フラグ立てされた異常
プロンプト:
diagnose-cost-spike — 最近のコスト急増の原因と対策を調査
weekly-cost-digest — 200語程度の週次コストダイジェスト
pip install openclaw-cost-tracker-mcp~/Library/Application Support/Claude/claude_desktop_config.json (macOS) または %APPDATA%\Claude\claude_desktop_config.json (Windows) に以下を追加します：
{
"mcpServers": {
"openclaw-cost": {
"command": "python",
"args": ["-m", "openclaw_cost_tracker_mcp"],
"env": {
"OPENCLAW_COST_BACKEND": "mock"
}
}
}
}バックエンド | ステータス | 説明 |
| ✅ v1.0 | プロトコル検証用の意図的な異常やルーティング機会を含むサンプルデータ |
| ✅ v1.0 | OpenClawのネイティブコストログJSONLファイルを解析（デフォルト |
| ⏳ v1.1 | AnthropicおよびOpenAIの請求APIを直接読み込み（ログシム不要） |
各行は1つのJSONレコードです：
{"request_id":"req-abc123","timestamp":"2026-05-04T12:34:56Z","provider":"anthropic","model":"claude-sonnet-4","agent_id":"data-extraction-agent","skill_id":"extract-structured-data","prompt_tokens":8500,"completion_tokens":600,"total_tokens":9100,"cost_usd":0.0345,"duration_ms":4823}OpenClawのデプロイメントがこの形式を出力しない場合は、プロバイダー呼び出しを小さなログシムでラップしてください（examples/にサンプルシムがあります）。
バージョン | スコープ | ステータス |
v1.0 | mock + openclaw-jsonlバックエンド、7ツール / 3リソース / 2プロンプト、異常検知・ルーティング・予測、GitHub Actions CIマトリックス、PyPI Trusted Publishing | ✅ |
v1.1 |
| ⏳ |
v1.2 | バックエンドの統合、予算アラート + しきい値超過検知 | ⏳ |
v1.x | チャネルごとのコスト配分、予算アラート用のWebhookエミッター | ⏳ |
AIデプロイメントでOpenClawのコストログ形式を使用していない場合（異なるエージェントハーネス、カスタムログ、AWS Bedrockの計測、ベンダーの請求APIなど）でも、同様の配分・異常検知・ルーティングの可視化を実現したい場合は、カスタムMCP構築をご依頼ください。
ティア | スコープ | 投資額 | 期間 |
シンプル | 既存のコストデータソース用の単一バックエンドアダプター | $8,000–$10,000 | 1–2週間 |
標準 | カスタムバックエンド + カスタム異常ルール + アラート統合 | $15,000–$20,000 | 2–4週間 |
複雑 | マルチバックエンド統合 + 予算強制 + カスタムルーティングロジック | $25,000–$35,000 | 4–8週間 |
依頼方法:
件名を Custom MCP Build inquiry として temur@pixelette.tech までメールしてください。
スタックの説明（1段落）と検討中のティアを記載してください。
2営業日以内に30分間のヒアリング用カレンダーリンクを返信します。
このサーバーは、本番AIインフラストラクチャMCPスイートの一部です。silentwatch-mcp（cronのサイレント障害検知）およびopenclaw-health-mcp（デプロイメントの健全性）と併せて、3つすべてをインストールすることで完全な運用可視性が得られます。
本番環境でAIを運用しており、外部の専門家に準備状況を評価させ、既存の障害パターン（コスト超過は最も一般的なパターンの1つ）を特定し、是正計画を作成したい場合は以下をご検討ください：
ティア | スコープ | 投資額 | 期間 |
Audit Lite | 1システム、上位5つの所見、報告書作成 | $1,500 | 1週間 |
Audit Standard | 完全監査、全14パターン、5つのCs所見、90日後のフォローアップ | $3,000 | 2–3週間 |
Audit + Workshop | 標準監査 + 2日間のチームワークショップ + 初回の月次監査を含む | $7,500 | 3–4週間 |
同じメールアドレス：temur@pixelette.tech（件名：AI audit inquiry）までご連絡ください。
プルリクエストを歓迎します。バックエンドはプラグイン可能です。契約については src/openclaw_cost_tracker_mcp/backends/ を参照してください。
新しいバックエンドを追加するには：
backends/<your_backend>.py で CostBackend をサブクラス化する
get_entries() を実装する
backends/__init__.py に登録する
tests/test_backend_<your_backend>.py にテストを追加する
バグ報告や機能リクエストはGitHubのIssueを作成してください。
MIT — LICENSE を参照してください。
silentwatch-mcp — cronのサイレント障害検知
openclaw-health-mcp — デプロイメントの健全性
AI Production Discipline Framework — Notionテンプレート、$29 — これらのMCPツールが実装する手法（コスト超過はカタログ内のパターンP3.x）
SPEC.md — 完全なサーバー設計
構築者: Temur Khan — 本番AIシステムの独立系専門家 連絡先: temur@pixelette.tech
Top-level cost summary for a window — total spend, request count, by-provider breakdown, top agents, top models, anomaly count. Use this first for a single-pane view.
| Name | Required | Description | Default |
|---|---|---|---|
| window_hours | No | Lookback window in hours (default 168 = 7 days, max 720 = 30 days) | |
| top_n | No | How many top agents + models to include (default 5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. Description implies read-only behavior (cost summary), but does not explicitly state non-destructiveness, rate limits, or other 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?
Two sentences with zero waste. Front-loaded with output items. 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?
No output schema, but description lists all expected return components (total spend, request count, breakdowns, top agents/models, anomalies). Adequate for a top-level summary tool.
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 parameters described). Description adds no additional meaning beyond what the schema provides, 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?
Description clearly states verb (summary) and specific resource (top-level cost for a window). It lists the exact components returned and distinguishes from siblings via 'use this first for a single-pane view'.
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?
Explicitly says 'Use this first for a single-pane view', implying it's the entry point. No explicit when-not or alternatives, but the sibling list provides context for other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Per-agent cost breakdown — total spend + request count + avg cost-per-request + primary provider/model + share of total spend, sorted by cost descending.
| Name | Required | Description | Default |
|---|---|---|---|
| window_hours | No |
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 does not disclose whether the tool is read-only, destructive, or has auth/rate limits. Since it likely just queries data, a note about idempotency would be beneficial.
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 that efficiently lists all output fields and the sort order. 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?
The description explains the output well but omits the single input parameter entirely. Given no output schema, this is a notable gap for input comprehension.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one optional parameter (window_hours) with 0% schema description coverage. The description does not mention this parameter at all, failing to explain its meaning or effect on results. The description adds no value 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 it provides a per-agent cost breakdown with specific metrics (total spend, request count, avg cost-per-request, primary provider/model, share of total spend, sorted by cost descending). This distinguishes it from sibling tools like cost_overview (likely overall) or costs_by_provider.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The usage is implied from the purpose (use when you need per-agent costs), but there is no explicit guidance on when to use this tool versus alternatives, no prerequisites, and no mention of when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Per-provider cost breakdown — total spend + request count + token counts + share of total, sorted by cost descending.
| Name | Required | Description | Default |
|---|---|---|---|
| window_hours | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description is responsible for behavioral disclosure. It mentions output metrics and sorting but omits key details such as that it is a read-only operation, how the time window (window_hours) affects results, or any limits/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 that front-loads the key information (what the tool returns and how it is sorted), with no superfluous content.
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 tool has one optional parameter, no output schema, and several siblings, the description fails to explain the parameter or provide usage guidance, making it incomplete despite adequately covering purpose and basic output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter (window_hours) with 0% description coverage in the schema. The description does not mention window_hours at all, leaving the agent unaware of how to specify the time range for the breakdown.
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 provides a per-provider cost breakdown with specific metrics (total spend, request count, token counts, share of total) sorted by cost descending, which distinguishes it from siblings like costs_by_agent and cost_overview.
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 the tool is for per-provider breakdowns but does not explicitly state when to use it over alternatives like costs_by_agent or cost_overview, nor does it provide exclusions or context for when it is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Requests whose cost is threshold_multiplierx or more above their agent's median request cost. Default 3.0x. Surfaces giant-context-paste accidents, runaway loops, model regressions, etc.
| Name | Required | Description | Default |
|---|---|---|---|
| window_hours | No | ||
| threshold_multiplier | No | Multiplier above agent median to flag (default 3.0, min 1.5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains the tool flags requests exceeding a threshold, but does not disclose if it modifies data, requires permissions, or the format of returned anomalies. This is adequate but leaves gaps.
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?
Two sentences suffice to convey purpose, examples, and default values. Information is front-loaded and no unnecessary words are present.
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 could elaborate on the output format. However, the context of sibling cost tools and the clear examples provide sufficient completeness for a diagnostic tool.
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 50% with only threshold_multiplier having a description. The tool description adds meaning by explaining the purpose of threshold_multiplier (default 3.0x, min 1.5) and implies window_hours as a time window. This provides useful context 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 uses a specific verb 'find' and resource 'cost anomalies', clearly distinguishes from sibling tools like cost_overview or top_cost_drivers, and provides concrete use cases (giant-context-paste accidents, runaway loops).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for detecting anomalous cost spikes with examples, but does not explicitly state when not to use it or compare to alternatives. However, the context of sibling tools makes its purpose distinct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Projects 30-day total spend from the observed window's run rate. Returns per-provider 30d projection + confidence note. Re-run with longer window for stability.
| Name | Required | Description | Default |
|---|---|---|---|
| window_hours | No | Hours of observed data to project from (default 168 = 7 days) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool returns a per-provider projection and confidence note and suggests re-running for stability, but does not explicitly state whether the operation is read-only or discuss any side effects or permissions needed.
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 three short, front-loaded sentences with no filler. Each sentence adds value: purpose, return structure, and usage advice. It is efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one optional parameter and no output schema, the description adequately covers what it does and returns (per-provider projection, confidence note). It is mostly complete, though it could specify the format of the projection or confidence level more precisely.
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 100% coverage (window_hours with description and default). The description adds no new parameter meaning beyond the schema, only context about stability. Baseline 3 is appropriate as the description does not detract but adds minimal extra 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 the tool projects 30-day total spend from observed run rate, with a specific verb ('projects') and resource ('30-day total spend'). It distinguishes from sibling tools like cost_overview or costs_by_provider by focusing on forecasting rather than current breakdowns.
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 advises re-running with a longer window for stability, implying when to adjust the parameter, but it does not explicitly state when to use this tool versus alternatives like find_cost_anomalies or top_cost_drivers. No when-not or comparator guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Suggestions to route some volume to cheaper providers/models — e.g., claude-sonnet-4 → gemini-2.5-flash for extraction-style work, gpt-4o → gpt-4o-mini for short chats. Each rec includes 30d estimated savings.
| Name | Required | Description | Default |
|---|---|---|---|
| window_hours | No | Window over which to find candidates (default 720 = 30 days) |
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 lacks details on side effects (e.g., does it modify anything?), permissions, limitations, or how suggestions are generated. The description only states output (suggestions with savings) but not behavior 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 two sentences, front-loaded with the main purpose, and uses concrete examples to clarify. Every sentence adds value without 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?
The description is adequate for a recommendation tool but lacks details on output format, number of suggestions, or prerequisites (e.g., required cost data). Without output schema, more behavioral context would be helpful for an agent to use it 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?
The single parameter 'window_hours' is fully described in the schema (coverage 100%). The description adds no additional meaning beyond what the schema provides, so baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides suggestions to route volume to cheaper providers/models, with specific examples and mention of 30d estimated savings. It is easily distinguishable from sibling tools which focus on cost analysis rather than recommendations.
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 explicitly state when to use this tool versus alternatives. While the sibling tool names give implicit context, there is no guidance on when to recommend routing vs. other cost actions, or exclusions for certain scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Highest-spend agents + models in the window — flat list, no per-provider or anomaly noise. Useful for 'where's our money going' digest format.
| Name | Required | Description | Default |
|---|---|---|---|
| window_hours | No | ||
| top_n | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that results are a flat list without per-provider or anomaly noise, which is sufficient for a simple query tool. Lacks mention of data freshness or side effects, but none expected.
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?
One concise sentence plus a usage suggestion; no filler words. Front-loaded with key 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 low complexity (2 optional params, no output schema), description adequately covers purpose, format, and use case. Could add details on output fields or ordering, but not critical for a digest.
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 0%, so description must compensate. It does not explicitly explain window_hours or top_n parameters, only implying a time window and top N via context. Defaults are in schema but not described.
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 explicitly states it identifies 'highest-spend agents + models' and contrasts with siblings by saying 'flat list, no per-provider or anomaly noise,' making purpose and differentiation clear.
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?
Explicitly suggests use case: 'where's our money going' digest format, and implicitly advises against using when per-provider breakdowns or anomalies are needed, referencing sibling tools.
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.
v1.0.0cost_overviewcosts_by_agentcosts_by_providerfind_cost_anomaliesforecast_monthly_costmodel_routing_recommendationstop_cost_driversScored across 7 tools
Each tool targets a distinct cost analysis aspect: overview, agent/provider breakdown, anomalies, forecasting, recommendations, and top drivers. No overlap in functionality.
All tool names use consistent lowercase underscore pattern, clearly indicating their purpose (e.g., cost_overview, find_cost_anomalies). Pattern is predictable and readable.
Seven tools is ideal for a focused cost tracker; each tool covers a specific need without redundancy or bloat.
Covers core cost analysis needs (overview, breakdowns, anomalies, forecasting, optimization). Minor gaps like filtering or export are acceptable given the scope.
Cost per run, tool, model and day for AI agents, from one call or an OpenTelemetry export.
Analytics for MCP servers. Query your tool calls, first-call success, retries and schema cost.
Agent Cost Allocator MCP — multi-tenant LLM cost attribution for chargeback billing. Companion to
Costory is the FinOps agent your team builds on. Claude plus a single provider's MCP works for one person and one cloud. It breaks when you add more providers, more people, and more tools to maintain. - Multiple providers: AWS, GCP, Azure, LLM APIs and data clouds normalized and allocated to teams and features in one model, including allocation from telemetry (Kubernetes, Datadog, dbt). - Multiple users: one hosted setup with OAuth, per-user access and enterprise SSO, instead of local credentials and prompts per person. - Maintained for you: tools, skills and connectors kept up to date by Costory, so reorgs and new services don't break your agent. Your agent finds savings, explains every change in spend and issues recommendations that your coding agent can implement. It works in Claude, Cursor and any MCP client, plus Slack. Customers include Alan, Deezer, Delivery Hero, Voodoo and Pennylane. Pricing: Starter from $300/month, flat: unlimited users and data sources, and a fair use that 80% of customers never outgrow. Nothing is ever cut off, and agents connected through the MCP use no AI credits. 30-day free trial.
