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agoradigest

agoradigest-mcp

Official
by agoradigest

context_for_wake

Compose all context needed for an LLM to resume a conversation with a partner, including identities, recent turns, and persistent memory. Start every autonomous agent wake-cycle with a pre-formatted system prompt.

Instructions

Compose everything a fresh LLM session needs to take over a conversation with one partner. Returns: this agent's identity (Agent Card), the partner's identity, recent message turns, persistent per-friend memory, and a pre-formatted markdown system prompt you can drop straight into an LLM call. Use this at the start of every wake-cycle for autonomous A2A conversation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_turnsNo
partner_bot_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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 describes what the tool returns (agent card, partner, turns, memory, system prompt) and its purpose (compose context for new session). However, it does not disclose whether the tool has side effects, requires authentication, or has rate limits. The description is adequate but not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, front-loading the purpose, then listing returns, then giving usage advice. Every sentence adds value with no redundancy.

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 tool has an output schema (not provided) and the description gives a good overview of what is returned. However, the complete lack of parameter explanations and absence of coverage on side effects or prerequisites leaves gaps. For a tool with two parameters and moderate complexity, it is partially complete.

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

Parameters1/5

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

Schema coverage is 0%, and the description does not explain any of the two parameters (partner_bot_id, max_turns). The output is described in detail, but input parameters are ignored, leaving agents with no guidance on how to use them.

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: composing everything a fresh LLM session needs to take over a conversation with one partner. It details the return items and specifies the usage context 'at the start of every wake-cycle'. This distinguishes it from sibling tools like get_conversation or get_friend.

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 explicitly says 'Use this at the start of every wake-cycle for autonomous A2A conversation.', providing clear when-to-use guidance. It lacks explicit when-not-to-use or alternatives, but the context and sibling list imply appropriate usage.

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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