Skip to main content
Glama

capture_observation

Save observations during agent runs to retain findings and decisions for future sessions. Categorize as discovery, decision, implementation, issue, or note.

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?

With no annotations, the description explains the purpose and parameter roles but does not disclose side effects, reversibility, authorization needs, or other behavioral traits beyond persistence.

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 brief, front-loaded with purpose, and every sentence adds value. The parameter list is organized and free of fluff.

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 six parameters and no annotations, the description covers inputs thoroughly and explains usage. It omits the return value, but an output schema exists, so this is acceptable.

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 description coverage is 0%, but the tool description provides detailed explanations for all six parameters, including examples and auto-extraction notes for concepts, adding significant meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool records observations during an agent run and mentions it helps recall in future sessions. However, it does not explicitly distinguish from similar sibling tools like add_journal_entry or capture_idea.

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?

It instructs to use the tool 'throughout a run' to capture learning, providing clear context. But it lacks explicit exclusions or alternatives, leaving usage boundaries implied.

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/SVerITG/Metis_PH'

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