Skip to main content
Glama
tmeng7777

uctx

by tmeng7777

save_context

Store user preferences, facts, and notes in durable memory so any agent can recall them in future sessions.

Instructions

Save a durable fact, preference, or note about the user so any agent can recall it later.

Use this whenever the user states something worth remembering across sessions and tools (e.g. "I prefer Python", "I'm based in Boston", "my project is a job-hunting agent").

Args: content: The thing to remember, in a self-contained sentence. type: One of "preference", "fact", or "note". tags: Optional short keywords to aid later search (e.g. ["coding", "style"]). source_app: The app/agent saving this (e.g. "claude-desktop", "cursor").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
typeNonote
contentYes
source_appNounknown

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 carries the full burden. It clearly indicates a write operation ('Save') and 'durable' suggests persistence, but it does not disclose whether saving the same content again duplicates, overwrites, or merges existing entries, nor any permission requirements.

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 well-structured: purpose first, then usage guidance, then parameter details with examples. Every sentence contributes useful information; no filler or redundancy.

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 save operation with an output schema, this is nearly complete. It covers purpose, usage, and all parameters. It lacks only minor behavioral details (e.g., duplicate handling), but those are not critical for basic 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?

Schema coverage is 0%, so the description must fully explain each parameter. It does: 'content' is self-contained, 'type' is one of three values, 'tags' are optional keywords, and 'source_app' identifies the saving agent. This adds meaning well beyond 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 states a clear purpose: 'Save a durable fact, preference, or note about the user so any agent can recall it later.' This distinguishes it from siblings (forget_context, search_context, list_context) by focusing on durable storage for later retrieval.

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?

It explicitly says 'Use this whenever the user states something worth remembering across sessions and tools' and provides concrete examples. It does not explicitly exclude alternatives, but the context is clear enough for an agent to choose this tool over search or list operations.

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/tmeng7777/uctx'

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