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BlockRunAI

BlockRun MCP

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by BlockRunAI

blockrun_chat

Chat with AI models using routing modes including GLM, coding, reasoning, or free NVIDIA models. Pay per call with no API keys required.

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-v3.2" (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
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-5.4' 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?

With no annotations provided, the description carries full burden. It discloses important behaviors: budget enforcement via agent_id, routing modes, and caution against duplicate system prompts. It could mention rate limits or response format but covers the main behavioral traits.

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 well-structured with bullet points and front-loaded purpose. It is slightly lengthy but every section adds value. Could be trimmed slightly while maintaining clarity.

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 10 parameters, no output schema, and complexity (routing, budgets, multi-turn), the description covers all key aspects: model selection, modes, routing profiles, agent budget tracking, and multi-turn message handling. It is comprehensive for a tool of this complexity.

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

Parameters5/5

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

Schema coverage is 100%, and the description adds significant value beyond the schema by explaining modes, routing options, pricing, and model selection in detail. This enriches the agent's understanding of how to use parameters effectively.

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 the tool's purpose: 'Get a second opinion from another AI model, or use a specialized model for a specific task.' It distinguishes itself from sibling tools by focusing on AI chat/multi-model routing, which is unique among the blockrun 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?

The description provides explicit guidance on when to use different modes (glm, coding, reasoning, free) and how to select models directly. It also advises running blockrun_models for a full list. However, it lacks explicit 'when not to use' statements.

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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