Skip to main content
Glama

sage_pipe

Send work requests like research, analysis, or review to another agent through a pipeline, placing tasks in the target's inbox for retrieval.

Instructions

Send work to another agent via SAGE pipeline. The target agent will see this in their inbox on their next sage_turn or sage_inbox call. Address by provider name (e.g. 'perplexity', 'chatgpt') or by agent_id. SAGE journals the exchange when complete but does NOT store the full payload as memory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentNoWhat you want done: 'research', 'summarize', 'analyze', 'review', etc.
payloadYesThe work content to send
toYesTarget: provider name (e.g. 'perplexity', 'chatgpt') or agent_id hex
ttl_minutesNoTime-to-live in minutes (default: 60, max: 1440)
Behavior4/5

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

With no annotations, the description carries full burden for behavioral disclosure. It reveals that SAGE journals the exchange but does not store the full payload as memory, and that the target sees it on their next sage_turn or sage_inbox call. This adds important context beyond the input schema.

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 two sentences with no fluff. The first sentence states the core function, and the second adds key behavioral details. Every word 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 absence of annotations and output schema, the description provides adequate context: what the tool does, how to address targets, what happens to the payload, and target visibility. It could mention error handling or result feedback, but overall it is sufficiently complete for a simple send operation.

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?

Schema coverage is 100% with parameter descriptions. The description echoes the 'to' parameter addressing format and confirms the 'intent' purpose, but does not add new information beyond what the schema already provides. Baseline score of 3 is appropriate.

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: 'Send work to another agent via SAGE pipeline.' It specifies the verb (send), resource (work to another agent), and includes details about how the target receives it (inbox on next turn). This distinguishes it from sibling tools like sage_inbox (which reads inbox) and sage_turn (which processes turns).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains how to address the target ('by provider name or agent_id') and mentions the default TTL, but does not explicitly state when to use this tool versus alternatives or provide usage conditions. The guidance is implied rather than explicit.

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/l33tdawg/s-age'

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