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Hicortex - AI Fleet Memory

Official

hicortex_ingest

Store a new memory in long-term storage for explicitly requested knowledge, decisions, or learnings, with optional links to correct or supersede existing memories.

Instructions

Store a new memory in long-term storage. Use for Knowledge, Decisions, or Learnings. Capture is automatic (nightly) — use this ONLY for explicitly requested learnings, never routine content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesMemory content to store
projectNoProject this memory belongs to
correctsNoID (8-char prefix or full UUID) of an existing memory this one CORRECTS — records a corrected_by link and retracts the old memory (deterministic, no LLM). Mutually exclusive with supersedes.
supersedesNoID (8-char prefix or full UUID) of an existing memory this one SUPERSEDES — records a superseded_by link and marks the old memory superseded (deterministic, no LLM). Mutually exclusive with corrects.
memory_typeNoType of memory (default: Experience). Accepted: Knowledge/Experience/Decisions/Learnings (legacy raw enum also accepted, normalized to the canonical term).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.20.7

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the main effect (stores a memory) and notes the automatic nightly capture as background, but does not mention potential side effects of corrects/supersedes (retraction), idempotency, authentication, or error behavior. The description is not misleading but lacks depth regarding the tool's broader behavioral impact.

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 deliver the core purpose and usage constraint with zero fluff. The critical usage caveat ('never routine content') is front-loaded, ensuring the agent sees it immediately. Every word earns its place.

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 create operation with no output schema, the description covers the essential context: what to store, when to use it, and when not to. It does not explain the memory_type default or the retraction semantics of corrects/supersedes, but these are already in the schema. Given the absence of annotations, the description is sufficient but could add a note about the return value or error handling to reach full completeness.

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 the input schema already documents all five parameters (content, project, corrects, supersedes, memory_type) with clear descriptions. The description adds no additional parameter-level information, which is acceptable given full schema coverage; baseline 3 is appropriate.

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's core function: 'Store a new memory in long-term storage.' It specifies the content categories (Knowledge, Decisions, Learnings) and explicitly distinguishes it from automatic nightly capture. This is a specific verb-resource pair that leaves no ambiguity about what the tool does, and it differentiates from sibling tools like search, get, update, delete by focusing on ingestion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance: 'use this ONLY for explicitly requested learnings, never routine content.' It also explains that routine content is handled automatically (nightly), giving a clear alternative path. This is a strong when-to-use/when-not-to-use directive, far beyond a mere implied context.

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