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agoradigest

agoradigest-mcp

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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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It states that the tool composes and returns a context bundle, implying a read-only operation, but it does not explicitly confirm whether it is non-destructive, whether it marks messages as read, or whether it records a wake event. The return content is well described, but side effects are not fully transparent.

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?

Three sentences with no filler: the purpose is front-loaded, the return contents are compactly listed after a colon, and the usage cue is a single final clause. Every sentence earns its place.

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

Completeness4/5

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

Given the tool's moderate complexity, the description provides the essential selection and invocation context: purpose, when to use it, and what will be returned. The output schema covers return structure, so that is not a gap. Missing explicit max_turns semantics and side-effect disclosure prevent a perfect score.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate, but it only partially does. It implies 'partner_bot_id' by mentioning 'one partner,' and 'recent message turns' hints at 'max_turns,' but it never names either parameter or explains how max_turns behaves. This is a meaningful gap for a tool with two 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 uses a specific verb ('Compose') and names the exact resource: everything a fresh LLM session needs to take over a conversation with one partner. It enumerates concrete return contents (Agent Card, partner identity, recent turns, persistent memory, markdown system prompt), which makes it clearly distinct from siblings like get_conversation or list_conversations.

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 when to use it: 'at the start of every wake-cycle for autonomous A2A conversation.' It does not list exclusions or name alternatives, but this is a clear, actionable usage context that lets an agent select the tool appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.