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Hicortex - AI Fleet Memory

Official

hicortex_identity

Retrieve your standing identity — hand-edited personality, rules, and preferences — after context compaction or to look up a specific rule. Fetch all sections, a named section, or per-agent scoped identity.

Instructions

Fetch your standing identity — the hand-edited 'who you are + how you work' layer (personality, rules, preferences). Returns all sections or a specific one. Use this to re-read your identity after context compaction or to look up a specific rule. On multi-agent installs, pass agent to fetch a specific agent's scoped identity; omit for the global identity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoFetch a specific identity section by name (e.g. 'rules'). Omit for all sections.
agentNoFetch a specific agent's identity scope (for per-agent installs). Omit for global.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.20.7

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden, and it does well: 'Fetch' signals a read operation, and it discloses the global vs. per-agent scoping behavior and the optional-section return behavior. It does not discuss side effects, but the read-only intent is clear from the verb and resource.

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 compact and well-structured: it states the resource, the return behavior, the primary use cases, and the parameter guidance in two sentences. Every sentence contributes actionable information with no redundancy.

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?

For a two-optional-parameter read tool with no output schema and no annotations, the description is complete. It explains the return shape, when to call, and how each parameter affects the result. The examples of section types ('personality, rules, preferences') give enough grounding for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value beyond the schema by clarifying that omitting `agent` returns the global identity and passing it fetches a scoped identity on multi-agent installs, and that omitting `name` returns all sections. This is meaningful semantic enrichment.

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 the specific verb 'Fetch' and a clearly defined resource: the hand-edited identity layer covering personality, rules, and preferences. It distinguishes this from sibling tools by scoping it to the agent's own standing identity and explaining the 'all sections or a specific one' behavior.

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 gives explicit use cases: re-read identity after context compaction or look up a specific rule. It does not explicitly state when not to use it relative to siblings like hicortex_get or hicortex_search, but the usage context is specific enough to guide an agent.

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