AgentLedger
Was dieses MCP kann
Tracks, budgets, caps, alerts on, and reports AI agent spending across payment and token usage rails.
Tools
Eingabeschema
{'type': 'object', 'required': ['agent_id'], 'properties': {'agent_id': {'type': 'string', 'description': 'unique agent identifier'}, 'agent_secret': {'type': 'string', 'default': '', 'description': "the agent's own secret (either this or workspace_key)"}, 'workspace_key': {'type': 'string', 'default': '', 'description': "the owning workspace's key (either this or agent_secret)"}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'properties': {'topic': {'type': 'string', 'default': '', 'description': '"quickstart" | "mcp" | "rest" | "budget" | "errors" | "idempotency" | "all"\n (default "" == "all"). Unknown topics fall back to the full docs.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['agent_id'], 'properties': {'model': {'type': 'string', 'default': '', 'description': 'model id to price from tokens (instead of amount_cents)'}, 'agent_id': {'type': 'string', 'description': 'the agent that would spend'}, 'tokens_in': {'type': 'integer', 'default': 0, 'description': 'expected input tokens (with model)'}, 'tokens_out': {'type': 'integer', 'default': 0, 'description': 'expected output tokens, e.g. your max_tokens (with model)'}, 'agent_secret': {'type': 'string', 'default': '', 'description': "the agent's own secret (either this or workspace_key)"}, 'amount_cents': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'default': None, 'description': 'the spend in cents, if you already know it'}, 'workspace_key': {'type': 'string', 'default': '', 'description': "the owning workspace's key (either this or agent_secret)"}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['pattern'], 'properties': {'pattern': {'type': 'string', 'description': '"python_tracking" | "budget_enforcement" | "weekly_report" |\n "retry_safe_writes"'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['model'], 'properties': {'model': {'type': 'string', 'description': 'the model id you are about to call (e.g. gpt-4o, claude-sonnet-4)'}, 'tokens_in': {'type': 'integer', 'default': 0, 'description': 'expected input tokens'}, 'tokens_out': {'type': 'integer', 'default': 0, 'description': 'expected output tokens, e.g. your max_tokens'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['agent_id'], 'properties': {'agent_id': {'type': 'string', 'description': 'the agent whose budget the proxied calls are billed to'}, 'provider': {'type': 'string', 'default': 'openai', 'description': 'which upstream to proxy: openai or anthropic'}, 'agent_secret': {'type': 'string', 'default': '', 'description': "the agent's own secret (either this or workspace_key)"}, 'workspace_key': {'type': 'string', 'default': '', 'description': "the owning workspace's key (either this or agent_secret)"}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['agent_id'], 'properties': {'days': {'type': 'integer', 'default': 30, 'description': 'report window in days (default 30)'}, 'agent_id': {'type': 'string', 'description': 'unique agent identifier'}, 'agent_secret': {'type': 'string', 'default': '', 'description': "the agent's own secret (either this or workspace_key)"}, 'workspace_key': {'type': 'string', 'default': '', 'description': "the owning workspace's key (either this or agent_secret)"}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['agent_id', 'workspace_key'], 'properties': {'agent_id': {'type': 'string'}, 'workspace_key': {'type': 'string'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['agent_id', 'workspace_key'], 'properties': {'agent_id': {'type': 'string'}, 'workspace_key': {'type': 'string'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['agent_id', 'monthly_cents'], 'properties': {'agent_id': {'type': 'string', 'description': 'unique agent identifier'}, 'daily_cents': {'type': 'integer', 'default': 0, 'description': 'daily spending cap in cents (0 = no daily cap)'}, 'agent_secret': {'type': 'string', 'default': '', 'description': 'required for every call after the first for this agent_id'}, 'daily_tokens': {'type': 'integer', 'default': 0, 'description': 'daily token-burn cap (0 = no cap)'}, 'monthly_cents': {'type': 'integer', 'description': 'monthly spending cap in cents'}, 'workspace_key': {'type': 'string', 'default': '', 'description': 'required when claiming a brand-new agent_id; not\n needed once the agent_id has been claimed'}, 'monthly_tokens': {'type': 'integer', 'default': 0, 'description': 'monthly token-burn cap (0 = no cap)'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['agent_id', 'rail', 'amount_cents', 'service'], 'properties': {'rail': {'type': 'string', 'description': 'payment rail used â\x80\x94 one of "mpp", "x402", "api_key", "manual"'}, 'model': {'type': 'string', 'default': '', 'description': 'model name (e.g. "gpt-4o") â\x80\x94 token burn is reported per model'}, 'service': {'type': 'string', 'description': 'what was purchased (e.g. "search_query", "data_export")'}, 'agent_id': {'type': 'string', 'description': 'unique agent identifier (e.g. "research-agent-v2")'}, 'tokens_in': {'type': 'integer', 'default': 0, 'description': 'prompt tokens consumed (0 if unknown)'}, 'tokens_out': {'type': 'integer', 'default': 0, 'description': 'completion tokens consumed (0 if unknown)'}, 'agent_secret': {'type': 'string', 'default': '', 'description': 'required for every call after the first for this agent_id'}, 'amount_cents': {'type': 'integer', 'description': 'spend amount in cents (100 = $1.00), 0-10000000'}, 'workspace_key': {'type': 'string', 'default': '', 'description': 'required when claiming a brand-new agent_id; not\n needed once the agent_id has been claimed'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['uri'], 'properties': {'uri': {'type': 'string', 'description': 'e.g. skill://<product>/<skill-name>/SKILL.md\n Get valid URIs from `skills_list_tool`.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Letzte Tool-Änderungen
Ähnliche MCP-Server
ThinkNEO Control Plane
Provides an enterprise AI control plane for governance, guardrails, spend tracking, compliance, and model or tool routing.
Focxle: AI governance for B2B deals between agents
Provides agent spending controls, delegated budgets, signed authority checks, negotiations, escrow contracts, workforce hiring, a…
Conductor Relay MCP
Provides agent enrollment, discovery, messaging, task exchange, job claiming, governed direct sessions, balances, and marketplace…
Catalyst Governance
Governs AI-agent actions through permission checks, approval workflows, compliance scans, task management, workflow registration,…
CIVITAE — Governed AI Agent Marketplace
Provides a governed marketplace and collaboration platform for registering AI agents, discovering missions and bounties, negotiat…
Licium
Provides structured page data, source monitoring, bounty workflows, paid callable capabilities, and a directory of remote MCP end…
Sales Ops — Commission Statement Review — Quillon Operations (f8485f73)
Provides A2AWire agent registration, benchmarking, agent discovery and hiring, paid work, data purchases, and USDC escrow workflo…
Healthcare Admin — Authorization Review — Northgate Advisors (2bd7f518)
Provides A2AWire agent registration, benchmarking, agent discovery and hiring, paid work, data purchases, and USDC escrow workflo…