Tool Details: agent_context

Basic Information

MCP Tool Usage Examples

This tool can be called via the MCP (Model Context Protocol) endpoint. Here are examples of how to use it:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "agent_context",
    "arguments": {
      "input": "your input here"
    }
  }
}

curl -X POST https://venue-3.covia.ai/mcp \\
  -H "Content-Type: application/json" \\
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "agent_context",
      "arguments": {
        "input": "your input here"
      }
    }
  }'

import requests
import json

url = "https://venue-3.covia.ai/mcp"
payload = {
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
        "name": "agent_context",
        "arguments": {
            "input": "your input here"
        }
    }
}

response = requests.post(url, json=payload)
result = response.json()
print(result)

const fetch = require('node-fetch');

const url = 'https://venue-3.covia.ai/mcp';
const payload = {
    jsonrpc: '2.0',
    id: 1,
    method: 'tools/call',
    params: {
        name: 'agent_context',
        arguments: {
            input: 'your input here'
        }
    }
};

fetch(url, {
    method: 'POST',
    headers: {
        'Content-Type': 'application/json'
    },
    body: JSON.stringify(payload)
})
.then(response => response.json())
.then(data => console.log(data));

Asset Metadata

{
	"name": "Agent Context",
	"description": "Assemble the exact context an agent would send to its model for one hypothetical call, without calling the model: the same Spec through the same assembler as a live transition. Describe the call — an inbox message (or several), pending job results, a task, a session — and receive the level-3 input (model, messages, tools, cacheMarks) plus budget and band diagnostics, palette provenance, load-resolution details and logical prefix hashes. With nothing to act on, the context shows the empty-state signal a wake-up would see.",
	"dateCreated": "2026-04-12T00:00:00Z",
	"operation": {
		"adapter": "agent:context",
		"internal": false,
		"toolName": "agent_context",
		"input": {
			"type": "object",
			"properties": {
				"agentId": {
					"type": "string",
					"description": "Agent reference: a bare name for the caller's agent, g/<id>, /g/<id>, or <ownerDID>/g/<id>"
				},
				"message": {
					"description": "A hypothetical inbox message for this call: a string, or an envelope {message, caller?}. Renders as the current input, with venue attribution when the caller is not the agent's own principal."
				},
				"messages": {
					"type": "array",
					"description": "Several inbox messages, same forms as message."
				},
				"pending": {
					"type": "array",
					"description": "Job results arriving this cycle, as {jobId, status, output}. (goaltree: results arrive as session turns, so none render separately.)"
				},
				"task": {
					"description": "A task input. llmagent renders it as the outstanding task the agent must complete or fail, with the task tools offered; goaltree renders it as the goal."
				},
				"sessionId": {
					"type": "string",
					"description": "Optional session id (hex, with or without a 0x prefix): the session's conversation — prior turns, tool-failure diagnostics — renders exactly as a live transition on that session would see it."
				}
			},
			"required": [
				"agentId"
			]
		},
		"output": {
			"type": "object",
			"description": "The level-3 input — model and caller tuning such as maxTokens/temperature/topP/cache when configured, responseFormat?, messages, tools, cacheMarks? — plus budget {bytes, used, remaining}, marks {head, live, conversation, toolLoop}, labels, palette {tools, unavailable}, loads [{ref, kind, status, bytes, budget, truncated, deduplicated}], and sessionTokens when a session has measured usage."
		}
	}
}

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