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BlockRunAI

BlockRun MCP

Official
by BlockRunAI

blockrun_wallet

Check USDC balance, set spending limits, switch payment chains (Base/Solana), and delegate budgets to child agents for cost control in multi-agent systems.

Instructions

Call this tool to manage the BlockRun payment wallet and control agent spending budgets.

Call this FIRST if any other blockrun_* tool returns a payment/balance error. Call this to check your current USDC balance before expensive operations. Call this to set spending limits before spawning child agents.

The server holds TWO wallets — one on Base, one on Solana — but pays on ONE active chain at a time. status shows both addresses/balances and which is active. Default chain is Base.

To pay on Solana (no env vars, no file editing, no restart):

  1. action:"chain" chain:"solana" → provisions + activates the Solana wallet

  2. action:"setup" → Solana address + funding QR (send USDC SPL on Solana) Switch back with action:"chain" chain:"base". Base-only — these ignore Solana and need Base: blockrun_music, blockrun_speech, blockrun_video, blockrun_realface, paid blockrun_price, blockrun_chat routing:"smart", and native Anthropic (claude-*). blockrun_image pays on either chain.

Actions:

  • status (default): Both wallet addresses + USDC balances, active chain, session spending

  • setup: Get funding instructions + QR code for the ACTIVE chain (call this when balance is 0)

  • qr: Open QR code (active chain) in system viewer

  • chain + chain:"base"|"solana": Switch the active payment chain (omit chain: to just see the current one)

Budget controls:

  • budget + budget_action:"set" + budget_amount:1.00 → Set global spend cap

  • budget + budget_action:"clear" → Remove global spend cap

Multi-agent orchestration:

  • delegate + agent_id:"research" + agent_limit:2.00 → Allocate $2 to a child agent

  • revoke + agent_id:"research" → Remove a child agent's budget

  • report → See per-agent spending breakdown

Usage pattern for multi-agent systems:

  1. blockrun_wallet action:"delegate" agent_id:"worker-1" agent_limit:1.00

  2. Pass agent_id:"worker-1" to all blockrun_chat/search/etc calls for that agent

  3. blockrun_wallet action:"report" to audit spending

Do NOT call this for actual AI queries — use blockrun_chat for that.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNoTarget chain for action='chain'. Omit to view the current active chain.
actionNoWhat to dostatus
agent_idNoAgent identifier for delegate/revoke/report actions
agent_limitNoBudget limit in USD for this agent (required for delegate action)
budget_actionNoBudget action (for action='budget')
budget_amountNoBudget limit in USD (for budget_action='set')
Behavior5/5

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

No annotations provided, so description carries full burden. It thoroughly explains two-wallet setup, chain switching, actions (status, setup, qr, chain, budget, delegate, revoke, report), budget controls, and delegation, leaving no ambiguity about behavior.

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

Conciseness4/5

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

Long but well-structured with sections and bullet points. Front-loaded with purpose and guidelines. Every sentence adds value, though could be slightly more concise.

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

Completeness5/5

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

Covers all aspects of a complex tool: multi-chain, multiple actions, budget controls, multi-agent delegation. Even without output schema, explains what status shows and typical workflow. Complete context for an AI agent to use correctly.

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

Parameters4/5

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

Schema coverage is 100%, baseline 3. Description adds significant meaning beyond schema by explaining usage patterns, default action, and parameter relationships (e.g., 'chain + chain:...' syntax, budget_action dependencies).

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

Purpose5/5

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

The description clearly identifies the tool as managing BlockRun payment wallet and controlling agent spending budgets. It distinguishes from siblings like blockrun_chat by explicitly stating not to call for AI queries.

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

Usage Guidelines5/5

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

Provides explicit when-to-use instructions: call first on payment errors, check balance before expensive ops, set limits before spawning agents. Also includes a usage pattern for multi-agent systems and says when not to use.

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

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