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usecortex

Hydra DB MCP Server

by usecortex

Ingest Conversation (deprecated)

hydra_db_ingest_conversation

Ingest conversation turns into Hydra DB memory to extract insights, preferences, and knowledge graph entities for later recall.

Instructions

DEPRECATED — use hydradb_ingest instead. Ingest one or more user-assistant conversation turns into Hydra DB memory. Hydra DB will extract insights, preferences, and knowledge graph entities from the conversation. Use this to store conversation history so it can be recalled later. Each turn is a pair of user message and assistant response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
turnsYesArray of conversation turns, each with a 'user' and 'assistant' field
source_idYesSource identifier to group all turns from the same session together
user_nameNoOptional name of the user for personalisation (default: 'User')
Behavior3/5

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

No annotations provided, so description carries burden. It discloses that tool extracts insights/preferences/KG entities. However, does not discuss side effects (e.g., idempotency, performance) typical for a mutation tool.

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?

Single paragraph, front-loaded with deprecation warning. Efficient and informative, though could be slightly more structured (e.g., bullet points for clarity).

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?

Given low complexity (3 params, no output schema), description covers purpose, deprecation, functionality, and usage. Missing return value info but acceptable for a simple ingestion tool.

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 coverage is 100%, so parameters are well-documented in schema. Description adds context about turn pairs and source grouping, but no significant additional meaning beyond what schema provides.

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 the tool ingests conversation turns into Hydra DB memory, with specific verb and resource. Deprecation and explicit alternative (hydradb_ingest) distinguish it from siblings.

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?

Deprecation strongly guides agent to use an alternative. Also describes when to use (store conversation history). However, lacks detailed when-not-to-use beyond deprecation.

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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