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mcp-server-scf

by MarkAC007
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

scf_get_assessment_review_queue

Read-only

Retrieve a prioritized queue of AI assessment verdicts awaiting human review, sorted by risk (worst first). Filter by status and paginate to manage decision workflows.

Instructions

List AI verdicts awaiting a human decision, worst first (read — viewer role). tier=file (default) lists per-file verdicts; tier=window is the web app's Awaiting-confirmation queue.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNofile (default): entries carry file_id, act with scf_review_evidence_assessment. window: entries carry window_assessment_id, act with scf_review_window_assessment_verdictfile
limitNoPage size (1–200, default 50)
offsetNoPagination offset (default 0)
org_idYesOrganization UUID — obtain from scf_list_organizations
statusNoQueue filter (default awaiting)awaiting

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.5.0
    • addedInput schema / properties / tier
      Added value: +{
+  "default": "file",
+  "description": "file (default): entries carry file_id, act with scf_review_evidence_assessment. window: entries carry window_assessment_id, act with scf_review_window_assessment_verdict",
+  "enum": [
+    "file",
+    "window"
+  ],
+  "type": "string"
+}
  2. Addedv3.4.2

TDQS

A4.2/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=true, so the description goes further by specifying the viewer role, 'worst first' ordering, and the behavioral difference between tier=file and tier=window. It stops short of describing response shape or pagination behavior, but the existing annotation lowers the burden.

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: two sentences with no filler. It front-loads the core purpose, ordering, and access role, then efficiently covers tier behavior.

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?

For a read-only list tool, the description covers the essential selection context: what is listed, in what order, with what access, and how tiers differ. The schema fully documents parameters, so invocation details are available; only the absence of an output schema leaves minor uncertainty about exact response fields.

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 covers 100% of parameters with useful descriptions, so the baseline is 3. The description adds a slight extra meaning for tier by noting it maps to the web app's Awaiting-confirmation queue, but it does not materially improve on the schema for limit, offset, status, or org_id.

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 the specific verb 'List' with a clear resource: AI verdicts awaiting a human decision. It also conveys ordering ('worst first'), access level ('read — viewer role'), and the two tier modes, making it easy to distinguish from nearby review-action tools.

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 clear context for when to use this tool: when retrieving AI verdicts that need human review. It also explains the tier distinction between file-level and window-level queues, but it does not explicitly state exclusions or name alternative review tools in the description itself.

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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