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BlockRunAI

BlockRun MCP

Official
by BlockRunAI

blockrun_chat

Access 41+ AI models for coding, reasoning, or free chat. Use modes for specialized routing or let smart routing auto-select an optimal model.

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" → o3, o1, DeepSeek-R1

  • 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 41+ models with pricing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYesYour message to the AI
modelNoSpecific model ID (e.g., 'zai/glm-5', 'openai/o3')
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)
routingNoSet to "smart" to auto-select the optimal model via ClawRouter (14-dimension AI routing)
routing_profileNoCost/quality profile for ClawRouter: "free" (zero cost NVIDIA), "eco" (budget), "auto" (balanced, default), "premium" (best quality) (only applies when routing: "smart")auto
systemNoOptional system prompt
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.
stopNoUp to 4 stop sequences; generation halts when any is produced
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).
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.
Behavior4/5

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 covers modes, routing, pricing, and model selection, but lacks details on authentication, rate limits, or error handling. This is still relatively thorough.

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?

The description is fairly long but well-structured with bullet points for modes. It is informative without being overly verbose, though some sentences could be streamlined.

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, 100% schema coverage, and no output schema, the description covers essential behavioral aspects and usage. It explains multi-turn messaging, agent_id budget, and thinking for Claude, making it very complete.

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%, so baseline is 3. The description adds value by explaining modes, routing, and model selection, including pricing hints and which models are free, which goes beyond the schema's basic descriptions.

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 states it is for getting a second opinion from another AI model or using a specialized model, with distinct modes listed. This distinguishes it from sibling tools like blockrun_defi, blockrun_image, etc., which are for other specific tasks.

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?

The description provides explicit guidance on when to use different modes and routing options, and suggests running blockrun_models to see all models. It does not explicitly state when not to use the tool, but the sibling tools are distinctly different, making usage context clear.

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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