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compagnt

ProjectHub MCP Server

by compagnt

create_memory

Save AI-generated knowledge items like decisions, preferences, and lessons to a project for persistent context across conversations.

Instructions

Create a new memory in a project. Memories are AI-generated knowledge items — decisions, preferences, context, references, or lessons — that persist across conversations. Humans can view them in the platform.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoTags for categorization and search
titleYesShort descriptive title for the memory
sourceNoSource identifier (e.g. 'claude-code', 'mcp-server')
contentYesMemory content in plain text
categoryYesMemory category
is_pinnedNoPin memory for higher visibility (default: false)
project_uuidYesUUID of the project
is_workspace_wideNoMake memory visible across all projects in the workspace (default: false)
Behavior3/5

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

With no annotations, the description carries full burden. It mentions that memories persist across conversations and are viewable by humans, but does not disclose whether the tool is idempotent, any rate limits, or what the return value is. This leaves behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short (two sentences) and front-loaded with the action. The second sentence provides helpful context but is slightly verbose for a tool description. Still, no wasted words.

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

Completeness3/5

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

Given 8 parameters (4 required) and no output schema or annotations, the description is moderately complete. It explains the concept of memories but omits details like typical return values, error conditions, or that project_uuid must reference an existing project.

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 baseline is 3. The description adds context about memory categories but does not elaborate on parameter semantics beyond what the schema already provides. For example, 'source' or 'tags' usage is not clarified.

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 'Create a new memory in a project' and explains that memories are AI-generated knowledge items (decisions, preferences, etc.). This distinguishes it from siblings like list_memories, get_memory, update_memory, and delete_memory.

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?

The description implies use for storing persistent knowledge but provides no explicit guidance on when to use this over alternatives (e.g., creating a note or discussion). There are no when-not-to-use statements or context about prerequisites.

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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