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Latent Defense MCP Server

Latent Defense MCP Server

Official
by latent-defense
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

oracle_search_nodes

Search infrastructure nodes by text similarity using MiniLM embeddings to locate relevant components in the graph.

Instructions

Search nodes by text similarity using MiniLM embeddings.

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": {
      "node_description": "<node_description>"
    },
    "name": "oracle_search_nodes"
  }
}

Input Schema

NameRequiredDescriptionDefault
node_descriptionYes
node_typeNoall
top_kNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It only mentions 'text similarity' but lacks details on side effects, idempotency, required session state, or search scope (e.g., current graph vs. all nodes).

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?

One sentence, 7 words, front-loaded with the action. Efficient but could be slightly more informative without losing conciseness.

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

Completeness2/5

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

Given the complexity of a similarity search tool, the description omits critical context such as prerequisites (e.g., loaded graph), filtering behavior of 'node_type', and interpretation of 'top_k'. Output schema exists but is not shown; still, the description should provide more operational context.

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

Parameters2/5

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

Schema description coverage is 0%, and the description adds no per-parameter information. While parameter names are somewhat intuitive, the description fails to compensate by explaining what 'node_description' or 'node_type' mean in context.

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?

Description clearly states the verb 'search', resource 'nodes', and method 'text similarity using MiniLM embeddings'. It distinguishes from siblings like 'oracle_list_nodes' but does not differentiate from the sibling 'search_nodes', which could be confusing.

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 tool versus alternatives like 'search_nodes' or 'oracle_list_nodes'. No context on prerequisites or exclusions.

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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