Skip to main content
Glama
Hebbrix

Hebbrix MCP Server

Official
by Hebbrix

hebbrix_export

Read-onlyIdempotent

Export full collection data—memories, knowledge-graph entities, compiled profile—in one call. Choose JSON or Markdown to back up or migrate.

Instructions

Export EVERYTHING in a collection in one call — all memories, the knowledge-graph entities, and the compiled profile. Data portability: use it to back up or migrate a memory space, nothing is locked in.

format="json" (default) returns structured data; format="markdown" returns a single human-readable document under the "document" key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNojson
collection_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv0.3.20

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already define it as read-only, idempotent, and non-destructive. The description adds that 'nothing is locked in' and explains the markdown return format, corroborating the annotations. It lacks details on potential pagination or output size limits, but the core behavior is transparent.

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 concise, consisting of two sentences that deliver purpose and key parameter details. It is well-structured, with no redundancy or irrelevant information.

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 the absence of an output schema and minimal description of collection_id, the description is not fully complete. It mentions 'structured data' for JSON but does not detail the structure or specify how collections are identified. The markdown return key is noted, but overall context for a complex export operation is lacking.

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?

The description explains the 'format' parameter (json vs markdown) and its impact on the return structure. However, the 'collection_id' parameter is not described beyond the vague 'collection' in the purpose, leaving ambiguity about its role (e.g., what happens when null, or whether it is required for non-default collections).

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 exports everything in a collection (memories, knowledge-graph entities, compiled profile), making its core function unambiguous. This distinguishes it from sibling tools like get, list, or search which handle subsets.

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 explicitly mentions data portability for backup or migration, giving a clear use case. However, it does not explicitly contrast with alternatives or state when not to use it, though the purpose is clear enough.

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

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/Hebbrix/hebbrix-mcp'

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