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by msaad00
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

Exposure Paths

exposure_paths
Read-onlyIdempotent

Retrieve ranked exposure paths as JSON for security investigations. Filters by risk score and supports pagination for headless agents.

Instructions

Return ranked ExposurePath JSON for headless security agents.

    This is the agent-native graph surface: Claude, Cursor, Codex,
    Windsurf, Cortex, and other MCP clients can request the same
    investigation objects used by the dashboard without scraping UI state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of ranked exposure paths to return.
cursorNoContinue with pagination.next_cursor; keep the risk filter unchanged.
scan_idNoOptional graph scan ID. Omit to use the latest snapshot.
min_riskNoMinimum path risk score to include.
tenant_idNoTenant ID for the graph snapshot. Defaults to 'default'.default

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / cursor
      Added value: +{
+  "anyOf": [
+    {
+      "maxLength": 4096,
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Continue with pagination.next_cursor; keep the risk filter unchanged.",
+  "title": "Cursor"
+}
  2. Addedv0.88.4
  3. Removedv0.88.1
  4. Addedv0.87.1

TDQS

A4/5.0
Behavior3/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior, so the description doesn't need to repeat that. It adds minimal extra context: it returns JSON and is agent-native. There is no mention of error conditions, rate limits, or specific response format nuances, but the annotations cover the safety profile. The description doesn't contradict annotations and adds slight value.

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 concise, with the core action ('Return ranked ExposurePath JSON') in the first sentence, followed by a brief context sentence about its purpose. No wasted words; it's well-structured and front-loaded.

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

Completeness4/5

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

Given the tool has a rich output schema and all parameters have descriptions, the description doesn't need to explain return values or parameter syntax. It adequately conveys the tool's purpose and intended use case. The only minor gap is that it doesn't explicitly mention the ranking logic or graph snapshot behavior, but those are covered by 'min_risk' and 'scan_id' descriptions in the schema. Overall, it's sufficiently complete for an agent to call it correctly.

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?

The input schema descriptions cover 100% of parameters, so the schema itself fully documents each parameter's purpose. The description adds no additional parameter-specific meaning beyond what's already in the schema. With high schema coverage, the baseline of 3 is appropriate.

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 clearly states the tool's function: 'Return ranked ExposurePath JSON for headless security agents.' It specifies a concrete verb, resource, and audience. It also mentions it's the 'agent-native graph surface' and distinguishes it from UI scraping, which differentiates it from sibling tools that might simulate UI interactions or other analysis functions.

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 provides clear context that this tool is for agents to obtain investigation objects without scraping UI state, implying it's the preferred method for programmatic access. However, it does not explicitly exclude alternatives or name sibling tools like 'scan' or 'intel_lookup' for comparison. Still, the context is strong enough to guide an agent on when to use it.

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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