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contextstream

ContextStream MCP Server

Remember this

session_remember

Store information in memory using natural language. Remember preferences, decisions, and context for retrieval across conversations.

Instructions

Quick way to store something in memory. Use natural language. Example: "Remember that I prefer TypeScript strict mode" or "Remember we decided to use PostgreSQL"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesWhat to remember (natural language)
importanceNoInput parameter: importance.
project_idNoProject ID (UUID).
workspace_idNoWorkspace ID (UUID).
await_indexingNoIf true, wait for indexing to complete before returning. This ensures the content is immediately searchable.
Behavior2/5

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

Annotations already indicate readOnlyHint=false (write operation) and destructiveHint=false (non-destructive). The description adds minimal context beyond 'store something in memory', without disclosing whether it overwrites or appends, or any side effects. For a write tool, more behavioral detail is needed given annotation coverage.

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?

Description is extremely concise: one sentence plus two examples. Every sentence adds value by explaining the tool's purpose and usage pattern. No unnecessary words or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema is provided, yet the description does not mention what the tool returns after storing (e.g., confirmation, memory ID). For a tool that writes data, this is a significant gap. Parameter descriptions are complete, but the overall behavioral outcome is unclear.

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 description coverage is 100%, so parameters are well-documented in the schema. The description adds natural language guidance for the 'content' parameter but does not elaborate on other parameters like 'importance', 'await_indexing', etc. Baseline of 3 is appropriate as schema does the heavy lifting.

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?

Description states the tool stores something in memory using natural language, with examples. It clearly identifies the verb 'store' and resource 'memory', distinguishing it from siblings like 'memory_create_doc' by emphasizing quick, natural language input. However, no explicit sibling differentiation is provided.

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?

Description implies usage when you want to quickly store something using natural language (e.g., 'Quick way to store something in memory'). It provides examples but no explicit when-not-to-use or alternative tools. Lacks exclusions or guidance on when to choose this over siblings like 'memory_create_doc' or 'reminder'.

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

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