Skip to main content
Glama
LogicLabsAI

UltraMemory

Official
by LogicLabsAI

Fetch Memory

fetch
Read-onlyIdempotent

Retrieve a stored memory by its unique ID. Returns title, text, URL, and provenance data, or a not-found response for invalid IDs.

Instructions

Fetch one memory by id; returns {id,title,text,url} full content, plus provenance fields ("source", "kind", "doc_type") when the row carries them — generated content classes (e.g. rollup/capture) are identifiable via source/kind. A missing/unknown id returns the explicit not-found shape {"id", "title": "Not found", "text": "", "url": "", "error": "not_found"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesfact_id from search/recall results
scopeNoProject scope id (default 'default')default

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already provide readOnlyHint and idempotentHint, confirming safe read-only behavior. The description adds value by detailing the conditional inclusion of provenance fields and the explicit not-found response shape, which goes beyond the annotations. No contradictions.

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 concise sentences with no extra words. It front-loads the core action ('Fetch one memory by id') and immediately explains the return structure, making it efficient for an agent to parse.

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 the presence of an output schema and the tool's simplicity, the description is complete. It describes the input, conditional output fields, and error handling (not-found shape), leaving no ambiguity about what the tool returns or expects.

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 coverage is 100% with descriptions for both parameters ('fact_id from search/recall results' for id, 'Project scope id (default 'default')' for scope). The tool description does not add new meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 tool fetches one memory by ID and details the return shape. It is specific ('Fetch one memory by id') and distinguishes itself by focusing on single-record retrieval with full content and provenance fields. No siblings exist, so differentiation is not an issue.

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 specifies the input (fact_id from search/recall results) and clarifies behavior for missing IDs (returns not-found shape). While there are no sibling tools, the description does not explicitly state when to use this tool vs alternatives (e.g., search or list tools), but the purpose is clear enough for an agent to infer usage.

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/LogicLabsAI/ultramemory-mcp'

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