Skip to main content
Glama
gamaze-labs

Hicortex - AI Fleet Memory

Official

hicortex_get

Fetch a memory's full content by ID to lazy-load details when a snippet is insufficient. Retrieving marks the memory as used, so fetch only entries that could change your action.

Instructions

Fetch ONE memory's full content by id — use this to lazy-load entries from the '## Memory recall (auto)' index or from search results whose snippet was not enough. Fetching a memory marks it as used (strengthens it), so fetch entries that could change your action — not every shown one. When the memory shapes your answer, cite it to the user (id + date + origin agent) — mark a fetched memory FETCHED and a one-line entry cited unread SNIPPET; don't pass SNIPPET off as established.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesMemory id (as shown in recall index/search results)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.20.7

TDQS

A4.7/5.0
Behavior5/5

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

Because no annotations are provided, the description carries the full burden of behavioral disclosure. It explicitly reveals the important side effect that fetching a memory marks it as used and strengthens it, and it also defines the citation conventions FETCHED vs SNIPPET. This goes well beyond a simple read-operation description.

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 dense but purposeful: it front-loads the core action, then adds usage caution, side-effect disclosure, and a citation rule. Every sentence contributes to correct invocation and follow-up behavior, with no filler or repetition of schema fields.

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

Completeness5/5

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

With only one required parameter and no output schema, the description covers what is needed: what the tool returns ('full content'), when to call it, the side effect of calling it, and how to handle the result for citation. Nothing essential for correct use is missing.

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% and the id parameter is already described in the schema as 'Memory id (as shown in recall index/search results).' The description adds some context about where ids come from and what metadata is relevant for citation, but it does not fundamentally extend the schema's parameter documentation.

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 opens with a specific verb and resource: 'Fetch ONE memory's full content by id.' It also distinguishes itself from sibling tools by stating it is the lazy-load tool for entries from the '## Memory recall (auto)' index or insufficient search snippets, which is clearly different from index/search/recent listing tools.

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 gives explicit when-to-use guidance: lazy-load from the recall index or search results when the snippet was not enough. It also provides a clear exclusion—'not every shown one'—and advises fetching only entries that could change the agent's action, which serves as an effective decision rule.

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