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absorb_observations

Capture and persist observations from sub-agents to analyze findings and identify scar candidates for continuous improvement.

Instructions

Capture observations from sub-agents and teammates. The lead agent parses findings from sub-agent responses, then calls this to persist and analyze them. Identifies scar candidates.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "observations": "<observations>"
    },
    "name": "absorb_observations"
  }
}

Input Schema

Name	Required	Description	Default
`task_id`	No	Linear issue or task identifier (optional)
`observations`	Yes	Array of observations from sub-agents/teammates

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.6.4Jun 25, 2026

TDQS

A4/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions 'persist' implying state change, but does not disclose side effects, idempotency, required permissions, or failure behavior. More detail needed for full transparency.

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, front-loaded with purpose, followed by workflow and outcome. Every sentence adds essential information without redundancy.

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 has 2 parameters (1 required) and no output schema, the description adequately explains the input context and processing. It lacks details on return values or error handling, but is sufficient for a tool of this complexity.

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% with descriptions for all properties. The description adds value by explaining the purpose of the observations (analysis, scar candidate identification), which goes beyond the schema's structural definitions.

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 it captures observations from sub-agents and teammates, with specific verbs: capture, persist, analyze, identify. It distinguishes from siblings like confirm_scars or reflect_scars by focusing on the initial capture and analysis step.

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 provides context: it is called after the lead agent parses findings from sub-agent responses. However, it does not explicitly exclude alternatives or mention when not to use it, though the workflow implication is clear.

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