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kyudongkim
by kyudongkim

read_conversation

Retrieve and decrypt conversation messages for a session, with optional full history or custom limit. Use sender_type to determine whether you should reply: respond when the last message is from a human.

Instructions

Decrypt and return messages in a session (most recent 5 by default — pass full: true for the entire history, or limit for a custom count, e.g. when you actually need older context). The response's truncated field tells you whether anything was left out. To decide whether it's your turn to reply, use sender_type ('human' vs 'ai'), NOT is_mine — in a self-test session (you talking to yourself as a fake customer), is_mine is true for EVERY message including the human tester's own questions, since sender_listing_id is the same listing on both sides. If the last message has sender_type='human', you should respond; if 'ai', you already have. Check content_type: 'contact_card_request' means the other side is asking for your contact info (you can't send 'contact_card' yourself — only a human can, from the web dashboard); 'contact_card' is a real contact card they sent you.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNo
limitNo
session_idYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

No annotations are present, but the description fully compensates: it discloses decryption, default 5-message truncation, the `truncated` flag, the sender_type/is_mine pitfall in self-test sessions, and the contact_card asymmetry. This goes far beyond what the input schema provides.

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 definition is dense but every sentence adds operational value; core behavior and defaults are front-loaded before edge cases. The parenthetical style is complex but appropriate for the amount of decision-relevant context packed in.

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 no annotations and no output schema, the description covers the critical behavioral traps (is_mine vs sender_type, content_type meanings, contact_card restriction) that an agent must know to avoid incorrect replies. Nothing essential for invoking the tool correctly appears missing.

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?

With 0% schema description coverage, the description explains `full` (entire history) and `limit` (custom count), and gives a usage example. `session_id` is only implicitly referenced, but it is a required, self-describing identifier whose purpose is clear from context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear action ('Decrypt and return messages') on a session resource, with specifics about default scope and output fields. It does not explicitly differentiate itself from sibling open_conversation, but the verb+resource combination is unambiguous.

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?

Provides concrete guidance for choosing `full` vs `limit` and when older context is actually needed. It also tells the agent how to decide whether to reply and how to interpret content_type, though it does not name alternative tools or exclusion conditions.

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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