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Hand a whole conversation to another model

share_conversation

Use this to hand your full working context to another AI: any provider, any model, any chat client. Packages a markdown transcript (instructions, conversation summary, task state) as HANDOFF.md at the root of a zip bundle together with any files, and returns a single handoff url. The receiving model just fetches that url: it gets a briefing, the file manifest and download commands in one go. Say to the next agent: "Fetch this url to pick up where I left off."

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesNoOptional files the next agent needs alongside the transcript
titleNoShort name for the bundle, e.g. "auth refactor handoff"
passwordNoOptional custom password (8-64 chars); a strong one is generated when omitted
transcriptYesMarkdown briefing for the next agent: context, instructions, conversation summary, task state. Becomes HANDOFF.md at the bundle root.
expires_daysNoDays until the file is deleted (1-180, default 30)

TDQS

A4.1/5.0
Behavior4/5

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

The description goes beyond annotations by explaining the internal packaging: HANDOFF.md at the root of a zip, inclusion of files, and a single handoff URL. It also describes what the receiving model experiences (briefing, file manifest, download commands). Annotations already indicate external side effects, so the added specificity is useful.

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 one solid paragraph that is fairly detailed but front-loaded with the main purpose. Every sentence contributes to explaining the mechanism and how to use the URL, though it could be trimmed slightly without loss.

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?

With no output schema, the description fully explains the result: a handoff URL that the next agent fetches to receive briefing and download commands. It also covers the key parameters (files, transcript) in context, creating a complete mental model for the tool.

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

Parameters3/5

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

The input schema provides 100% coverage with detailed descriptions for all parameters. The tool description mostly restates that the transcript becomes HANDOFF.md and files are included, which the schema already says. It doesn't add substantial new parameter-level meaning.

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 specific purpose: to hand a full working context to another model, packaging a transcript and files into a zip and returning a handoff URL. It explicitly mentions the key elements (instructions, conversation summary, task state) and distinguishes itself from sibling tools that share files or text.

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 gives clear context for when to use the tool ('to hand your full working context to another AI') and details the handoff flow, but it doesn't explicitly mention alternatives or when NOT to use it. Since siblings are share_file/share_text, the usage context is reasonably clear.

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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TDQS

A4.3/5.0
Disambiguation3/5

receive_file is clearly distinct, but the three share_* tools have fuzzy boundaries: share_file accepts text and is described as a 'whole handoff', which overlaps with both share_text and share_conversation. The use-case descriptions help separate them, but an agent could reasonably select the wrong one when sharing text or a handoff bundle.

Naming Consistency5/5

All tools follow the same verb_noun pattern: receive_file, share_conversation, share_file, share_text. The verb prefixes (receive/share) reflect the two core directions and make the set predictable.

Tool Count5/5

Four tools is a well-scoped size for a transfer server: one receive operation, one conversation handoff, one file upload, and one text upload. Each tool earns a place and there is no bloat.

Completeness4/5

The core send/receive workflows are covered, including text, files, and a full AI-to-AI handoff. Minor gaps exist: large local files are delegated to an external HTTP API rather than handled by a tool, and there is no delete/expiry management for shared content.

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