Skip to main content
Glama

memory_ratify

Approve a pending proposal to turn it into a recallable memory, applying optional overrides. Invalid proposals are refused with their reason and remain pending, so only validated memories become canon.

Instructions

Ratify a pending proposal into a REAL memory. Loads the proposal, applies any optional overrides (your edits on accept), and commits it through the same validated write path as remember — so an invalid proposal is refused with its typed reason and stays pending. On success the memory becomes recallable and the proposal is removed from the queue. This is the gate: no proposal becomes canon without it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesProposal id (from memory_proposals)
ttlNoOverride the proposed TTL on accept
titleNoOverride the proposed title on accept
contentNoOverride the proposed content on accept
projectNoOverride the proposed project on accept
categoryNoOverride the proposed category on accept

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv0.5.0
    • changedInput schema / properties / category / enum
      Previous value: -[
      -  "user",
      -  "project",
      -  "self",
      -  "feedback",
      -  "reference",
      -  "pursuit"
      -]New value: +[
      +  "user",
      +  "project",
      +  "self",
      +  "feedback",
      +  "reference",
      +  "pursuit",
      +  "episode"
      +]
  2. First observedv0.4.1

TDQS

A4.4/5.0
Behavior5/5

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

No annotations are present, so the description carries the full burden—and it delivers. It discloses side effects: the proposal is loaded, overrides applied, committed through the same validated path as remember, invalid proposals stay pending with a typed reason, and successful ratification removes the proposal from the queue and makes the memory recallable.

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 the core purpose, then behavior, then significance. Every sentence contributes: the first says what it does, the second explains mechanics and failure handling, the third communicates its critical role.

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 six parameters and no output schema or annotations, the description covers the essential workflow, success conditions, failure behavior, and side effects. It is slightly light on what the tool returns on success and on edge cases like a nonexistent id, but the core information needed to call it correctly is present.

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?

Input schema coverage is 100%, with each parameter already described as an override or the proposal id. The description adds the helpful framing that the optional fields are 'your edits on accept,' but it does not add significant meaning beyond what the schema already states.

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?

States a specific verb and resource: 'Ratify a pending proposal into a REAL memory.' It also establishes the tool's unique role as 'the gate' between proposal and canonical memory, clearly distinguishing it from memory_propose, memory_proposals, and memory_reject.

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?

Clearly implies when to use it: when a pending proposal has been reviewed and should become a real memory. It does not explicitly list alternatives like memory_reject for refusals, but the workflow context is unmistakable and the 'gate' statement reinforces its place in the proposal lifecycle.

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

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/sleepunit-agents/loom'

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