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capture_learning

Record reusable insights from pipeline decisions and milestones. Categorize patterns with evidence and confidence to inform future decisions.

Instructions

Capture a reusable learning/pattern to learnings.json. Called after key decisions, verdicts, and pipeline milestones.

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": {
      "pipeline": "<pipeline>",
      "stage": "<stage>",
      "category": "<category>",
      "pattern": "<pattern>",
      "evidence": "<evidence>",
      "confidence": "<confidence>",
      "applies_to": "<applies_to>",
      "tags": "<tags>"
    },
    "name": "capture_learning"
  }
}

Input Schema

Name	Required	Description
`tags`	Yes	Searchable keywords for retrieval
`stage`	Yes	Which SOP/stage generated this learning (e.g. 'scout', 'stress-test')
`pattern`	Yes	One sentence — the reusable insight
`category`	Yes	Learning category
`evidence`	Yes	What data supports this pattern
`pipeline`	Yes	Market name / pipeline that produced this learning
`applies_to`	Yes	What future decisions this informs
`confidence`	Yes	Confidence level based on evidence strength

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Jul 5, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations are all false (readOnly, destructive, etc.), providing no behavioral cues. The description adds that the tool 'captures' data to a file, implying a write operation, but lacks details on behavior like idempotency or side effects. With limited annotations, more transparency would be beneficial.

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, front-loaded with the core purpose, and contains no extraneous information. It earns its place efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 8 required parameters and no output schema. The description does not explain the return value or provide guidance on populating the parameters (e.g., how to format evidence or tags). While clear for simple use, it lacks completeness for a parameter-heavy 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?

Schema description coverage is 100%, so the schema already documents all 8 parameters. The description does not add any parameter-specific meaning beyond the schema, resulting in a baseline score of 3.

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 verb 'capture' and the resource 'reusable learning/pattern to learnings.json'. It also provides context ('after key decisions, verdicts, and pipeline milestones'), distinguishing it from sibling tools like 'lessons' which may be more general.

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 explicitly states when to use the tool ('after key decisions, verdicts, and pipeline milestones'). However, it does not mention when not to use it or provide alternatives, which would improve clarity further.

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