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capture_observation

Record observations during agent runs to capture discoveries, decisions, implementations, issues, or notes for recall in future sessions.

Instructions

Record a typed observation during an agent run.

Use this throughout a run to capture what you are learning so it can be
recalled in future sessions without re-reading history.

Args:
    observation_type: One of: discovery, decision, implementation, issue, note.
      - discovery:      something new you found out
      - decision:       a choice made and the reasoning behind it
      - implementation: what was built or changed
      - issue:          a bug, blocker, or failure found
      - note:           a general observation that doesn't fit above
    content:        The observation in 1–3 sentences.
    agent_slug:     Which agent is recording (e.g. 'librarian').
    session_id:     Current pipeline session ID (optional).
    concepts:       Comma-separated concept tags — auto-extracted if blank.
    related_files:  Comma-separated file paths this observation relates to.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
observation_typeYes
contentYes
agent_slugNo
session_idNo
conceptsNo
related_filesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It implies persistence ('recalled in future sessions') but doesn't mention safety, permissions, or side effects. For a simple write tool, this is adequate but not rich.

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 front-loaded with purpose and usage, then organized into an Args section. It's informative without being verbose, though could be slightly more concise.

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

Completeness5/5

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

Given 6 parameters (2 required) and an output schema, the description covers all inputs and usage context thoroughly. It leaves no ambiguity for correct invocation.

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

Parameters5/5

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

With 0% schema description coverage, the description excels by listing all 6 parameters with detailed explanations, types, and example values. It adds far more meaning than the bare 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?

The description starts with 'Record a typed observation during an agent run', clearly stating the verb-resource combination. It distinguishes from sibling tools like add_journal_entry or add_memory_entry by focusing on recording observations for future recall.

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 says 'Use this throughout a run to capture what you are learning so it can be recalled in future sessions without re-reading history.' It provides clear context on when to use, though it doesn't explicitly exclude alternatives.

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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