Skip to main content
Glama

sui_stream_checkpoints

Subscribe to new checkpoints on the Sui blockchain and receive batches of checkpoint summaries with cursors. Resume streaming by passing the cursor from a prior call.

Instructions

Subscribe to new checkpoints and return a window of up to 10 frames (or until 30s elapse, whichever comes first). Each frame carries the cursor + checkpoint summary. Call repeatedly to tail the chain — pass the last cursor back as 'after_cursor' for resumable reads.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_framesNoMax frames to collect this call. Cap is SUI_MCP_STREAM_MAX_FRAMES (current: 10).
max_secondsNoMax wall-clock seconds. Cap is SUI_MCP_STREAM_MAX_SECONDS (current: 30).
after_cursorNoResume cursor returned by a prior call. Omit to subscribe from the live tip.
read_mask_pathsNoCheckpoint fields to populate. Defaults to ['sequence_number','digest','summary'].
networkNoSui network. Defaults to the server's configured default.
Behavior5/5

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

No annotations provided, so description fully discloses behavior: windowing (max_frames, max_seconds), each frame carries cursor + summary, resumption with after_cursor, and server caps. Transparent about all key traits.

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: first conveys core behavior, second explains windowing, third usage pattern. No wasted words, front-loaded with essential information.

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?

Covers all parameters and usage pattern well despite no annotations or output schema. Could elaborate on output format details (e.g., what fields are in the checkpoint summary), but sufficient for an AI agent to use correctly.

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?

Schema coverage is 100%, but description adds meaning: explains max_frames/seconds caps, after_cursor for resumption, default fields for read_mask_paths, and network default. Adds useful context beyond schema.

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?

Description clearly states it subscribes to new checkpoints and returns a window of up to 10 frames (or 30s). It distinguishes from siblings like sui_recent_checkpoints by explicitly stating the streaming pattern and resumable reads.

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 clear instructions to call repeatedly and pass the last cursor back as 'after_cursor' for resumable reads. Implicitly differentiates from one-shot checkpoints but lacks explicit when-not guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/abhinavg6/sui-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server