Exogram — Personal AI Agent & Workspace

get_neural_cluster

Look up an entity in Exogram's Layer 2 Knowledge Graph. Returns connected entities and directional relationships.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "entity_name": "<entity_name>"
    },
    "name": "get_neural_cluster"
  }
}

Input Schema

Name	Required	Description	Default
`entity_name`	Yes	Name of the entity to look up (e.g., "Next.js", "PostgreSQL", "SQLite WAL").

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 6, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden. It implies a read operation but says nothing about traversal depth, result limits, whether the lookup is exact-match or fuzzy, or whether it errors on unknown entities — all material for an agent deciding whether to call it.

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?

Two short sentences, front-loaded with the action and followed by the return shape. Efficient, though the second sentence is doing return-value work the missing output schema would otherwise cover.

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?

For a one-parameter lookup with full schema coverage, the description adequately explains input and output. It omits result scoping (depth/limit) and sibling routing, which matter given several graph-related siblings in the tool set.

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 the single entity_name parameter is already documented with examples. The description adds no syntax, casing, or matching semantics beyond that, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb and resource ("Look up an entity") plus where it lives ("Exogram's Layer 2 Knowledge Graph") and what comes back ("connected entities and directional relationships"). It stops short of differentiating from close siblings like query_graph and list_entities, which also operate on the same graph.

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

Usage Guidelines2/5

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

No guidance on when to use this versus query_graph, list_entities, or search_memory_vault, all of which could plausibly surface related entities. The agent is left to infer that this is the single-entity expansion lookup.

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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