Skip to main content
Glama

memory_lesson

Capture structured lessons, incidents, or decisions in one call. Fills the matching section template from your field values and stores it with deduplication.

Instructions

Capture a structured lesson or incident in one call: fills the matching section template (incident → Symptom/Root Cause/Fix/Prevention; lesson → What/Why it matters/How to apply) from your field values and stores it through the normal write path (deduped — a repeat capture is a NOOP). Unknown document_types use a generic scaffold.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
document_typeNoSection template to fill: lesson | incident | decision | bug-fix | meeting | session. Unknown types use a generic Summary/Details/Notes scaffold.lesson
fieldsYesSection values keyed by section name (snake_case ok), e.g. {symptom, root_cause, fix, prevention} for an incident or {what, why_it_matters, how_to_apply} for a lesson. Omitted sections keep a placeholder.
titleNoOptional title (auto-derived from the first field value when omitted)
scopeNoMemory scope for isolationglobal
namespaceNoNamespace within scope (e.g., project name, team name)
departmentNoDepartment (e.g., legal, engineering, hr, sales, finance)
tagsNoTags for categorization
sourceNoOrigin of the content (e.g., file path, URL, system name)
access_levelNoAccess classification levelinternal
importance_scoreNoManual importance 0–1 (higher surfaces first in reflection/recall)
Behavior4/5

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

The description discloses key behaviors: deduplication (repeat capture is a NOOP), template filling based on document_type, and a generic scaffold for unknown types. Annotations provide no destructiveHint or readOnlyHint, so the description carries the burden and meets it reasonably.

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?

Two sentences with no wasted words. The first sentence packs purpose, template behavior, and dedup, while the second handles edge cases. Front-loaded with the most important information.

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?

For a tool with 10 parameters (1 required) and no output schema, the description covers core behavior well. It explains template usage and dedup. However, it doesn't mention return values or error conditions, which could be helpful.

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%, so baseline is 3. The description adds value by explaining how fields map to sections and that omitted sections get a placeholder, which is beyond the parameter descriptions in 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 captures a structured lesson or incident by filling a section template. It distinguishes from generic memory tools by specifying template mapping and dedup behavior, but doesn't explicitly differentiate from siblings like memory_append or memory_store.

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?

Usage context is implied: use when capturing lessons or incidents with structured fields. The description notes that repeat captures are NOOP, which guides against redundant calls, but doesn't explicitly state when not to use or provide alternatives.

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/YonasValentin/mcp-memory-graph'

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