Skip to main content
Glama
legionultramax

Harris HawkEye MCP

search_entities

Find knowledge graph entities by name or description, optionally filtered by type, to support detection engineering and threat intelligence correlation.

Instructions

Search knowledge graph entities by name or description

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoOptional: filter by entity type
queryYesSearch query

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral disclosure burden. It does add useful scope information: matching is performed on entity name or description, and 'search' implies a read-oriented operation. However, it does not disclose result behavior, matching semantics, pagination, or limits.

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 a single sentence with no filler. Every phrase ('knowledge graph', 'entities', 'by name or description') is meaningful, and the core scope is front-loaded.

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 simple two-parameter search tool, the description and schema are functional. However, with no annotations and no output schema, the definition is missing any return-shape or disambiguation details that would help an agent fully understand what to expect or when to choose this over similar search tools.

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% for both parameters, so the baseline is 3. The description adds that the query applies to 'name or description', but it does not clarify type value format, behavior, or how the optional filter interacts with the query.

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 uses a specific verb ('Search') against a concrete resource ('knowledge graph entities') and adds the matching scope ('by name or description'). This distinguishes it from the many sibling search_* tools, such as search_detections or search_threat_groups.

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

Usage Guidelines3/5

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

The intended context is clear — search entities in the knowledge graph — but the description does not explicitly say when to prefer this tool over alternatives, nor does it provide exclusions or when-not-to-use guidance. The optional type filter is also left entirely to the schema.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/legionultramax/Detection-Engineering-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server