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BlockRunAI

BlockRun MCP

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by BlockRunAI

blockrun_chat

Query AI models from multiple providers with routing modes for speed, cost, or specialization. Supports multi-turn chat and model-specific options.

Instructions

Get a second opinion from another AI model, or use a specialized model for a specific task.

Notable modes:

  • mode:"glm" → Zhipu GLM-5 / GLM-5-Turbo ($0.001/call, excellent for coding tasks, pays via USDC on BlockRun)

  • mode:"coding" → GLM-5 first, then code-specialized models

  • mode:"cheap" → GLM-5, NVIDIA free, DeepSeek

  • mode:"reasoning" → Claude Opus, o3, o1, deepseek-reasoner

  • mode:"free" → NVIDIA models (no cost)

  • routing:"smart" → auto-select via ClawRouter

Pick directly: model:"zai/glm-5", model:"openai/o3", model:"nvidia/deepseek-v4-flash" (free).

Run blockrun_models to see all available models with pricing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoRouting mode: glm = Zhipu GLM-5/GLM-5-Turbo ($0.001/call, great for coding), coding = GLM-5 + code models, cheap = GLM-5 + budget, free = NVIDIA only (ignored if model specified)
stopNoUp to 4 stop sequences; generation halts when any is produced
modelNoSpecific model ID (e.g., 'zai/glm-5', 'openai/o3')
systemNoOptional system prompt
messageYesYour message to the AI
routingNoSet to "smart" to auto-select the optimal model via ClawRouter (14-dimension AI routing)
agent_idNoAgent identifier. If a budget was delegated for this agent_id via blockrun_wallet action:'delegate', spending is tracked and enforced. The agent is hard-stopped when its budget is exhausted.
messagesNoConversation history for multi-turn context. When provided, 'message' is appended as the final user turn. Use with explicit 'model' param (defaults to 'openai/gpt-5.5' if not specified). Note: if you include a role:'system' entry in messages[], do not also pass the system param to avoid duplicate system messages.
thinkingNoAnthropic extended thinking. Only honored for anthropic/claude-* models — these go direct to the native /v1/messages endpoint and the response includes verbatim type:'thinking' blocks with their original signature. Ignored for non-Claude models (no native thinking channel).
max_tokensNoMax tokens in response
temperatureNoCreativity 0-2
response_formatNoSet to 'json_object' to force valid JSON output (no markdown fences). Works across all providers.
routing_profileNoCost/quality profile for ClawRouter: "eco" (budget), "auto" (balanced, default), "premium" (best quality). Note: "free" maps to "auto" (the SDK dropped the free profile) and still settles a PAID model — for zero-cost generation use mode:"free" or model:"nvidia/...". Only applies when routing:"smart".auto
Behavior4/5

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

With no annotations, description covers key behaviors: routing modes, budget tracking via agent_id, thinking parameter limitations to Claude models, and smart routing via ClawRouter. Could mention more about response format or error handling.

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?

Well-organized with bullet points and clear sections for modes and parameters. Slightly verbose in some areas (e.g., repeating model IDs), but overall efficient.

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?

Given 13 parameters, nested objects, and no output schema, the description thoroughly explains all key aspects: modes, routing, thinking, agent budgets, and multimodal messages. Provides enough detail for an agent to use effectively.

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%, and description adds contextual value for many parameters (e.g., explaining mode options with examples, thinking restrictions, agent_id budget enforcement). Some parameters like 'stop' or 'temperature' have minimal added description beyond schema.

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?

Description clearly states it's for 'getting a second opinion from another AI model or using a specialized model for a specific task.' Lists distinct modes and directs to blockrun_models for full list, distinguishing from sibling tools.

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

Usage Guidelines4/5

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

Describes when to use each mode (e.g., glm for coding, cheap for budget, free for no cost) and directs to blockrun_models for pricing. However, lacks explicit when-not-to-use scenarios or comparison with sibling tools.

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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