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BlockRunAI

BlockRun MCP

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by BlockRunAI

blockrun_chat

Send messages to various AI models including GPT-5.6-sol, Claude Opus 4.8, and Kimi K3. Select modes for reasoning, coding, or cost-efficient queries.

Instructions

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

Notable modes:

  • mode:"powerful" → Claude Opus 4.8, GPT-5.6-sol, Claude Fable 5 (frontier, 1M context)

  • mode:"reasoning" → Claude Opus 4.8, GPT-5.6-sol, Kimi K3, Grok 4.3, deepseek-v4-pro

  • mode:"coding" → Claude Opus 4.8, GPT-5.3-codex, Kimi K3, Grok Build, GLM-5.2

  • mode:"cheap" → deepseek-v4-pro, MiniMax M3, GLM-5, NVIDIA free

  • mode:"glm" → Zhipu GLM-5 / 5.2 / 5.1 / 5-Turbo (cheap, strong at coding)

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

Pick directly: model:"moonshot/kimi-k3", model:"openai/gpt-5.6-sol", model:"anthropic/claude-opus-4.8", model:"xai/grok-4.5", model:"nvidia/deepseek-v4-flash" (free).

Run blockrun_models to see all available models with pricing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoRouting mode: powerful/reasoning = frontier models (Opus 4.8, GPT-5.6-sol, Kimi K3), coding = code-specialized, glm = Zhipu GLM (cheap, great for coding), cheap = budget models, free = NVIDIA only (ignored if model specified)
stopNoUp to 4 stop sequences; generation halts when any is produced
modelNoSpecific model ID (e.g., 'moonshot/kimi-k3', 'openai/gpt-5.6-sol', 'zai/glm-5')
systemNoOptional system prompt
messageYesYour message to the AI
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-5.6-terra' 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.
Behavior3/5

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

The description notes the agent_id parameter for budget tracking via blockrun_wallet, but does not mention other traits like cost, rate limits, or error handling. The annotations (readOnlyHint=false, destructiveHint=false) provide basic safety info. No contradiction.

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 a clear intro and bullet points for modes. It is informative without being overly verbose, though a bit lengthier than necessary.

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

Completeness3/5

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

The description and schema together cover parameters well, but there is no output schema or explanation of the return format. For a chat tool, this is a notable gap. The description is otherwise adequate for the complexity.

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 the baseline is 3. The description adds value beyond the schema by explaining mode options and model examples (e.g., Claude Opus 4.8, GPT-5.6-sol), which helps the agent choose appropriate parameters.

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 also distinguishes from sibling tools (e.g., blockrun_image, blockrun_search) which cover unrelated domains.

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 clear context on when to use the tool (getting a second opinion or specialized model) and details various modes for routing. However, it does not explicitly state when NOT to use it or mention alternatives beyond listing 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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