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compagnt

ProjectHub MCP Server

by compagnt

list_memories

Retrieve a list of AI-generated knowledge items, including decisions and lessons, from a project. Filter by source, category, or pinned status to find specific memories.

Instructions

List memories (AI-generated knowledge items) in a project. Memories capture decisions, preferences, context, references, and lessons that persist across conversations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNoFilter by source (e.g. 'claude-code', 'mcp-server')
categoryNoFilter by category
is_pinnedNoFilter by pinned status
project_uuidYesUUID of the project
Behavior3/5

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

No annotations, so description must cover behavioral traits. Only states it's a list operation and defines memories; lacks mention of read-only nature, auth requirements, or side effects.

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?

Two sentences with zero wasted words: first states action and scope, second defines the resource. Highly efficient.

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 simple listing tool with 4 well-described parameters and no output schema, the description explains what memories are and the operation scope. Slight gap: no mention of pagination or ordering, but still sufficient.

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 has 100% description coverage with details on filtering by source, category, and pinned status. Tool description adds no additional parameter info beyond schema, meeting baseline.

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?

Description clearly states 'List memories' with a definition of memories as AI-generated knowledge items. This distinguishes from sibling tools like get_memory (single) and list_workspace_memories (different scope).

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?

Implied usage is listing all memories in a project, but no explicit guidance on when to use vs alternatives (e.g., search or get_single_memory), and no when-not-to-use indications.

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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