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MarkAC007

mcp-server-scf

by MarkAC007

scf_trigger_window_assessment

Queues an AI assessment that scores all files in an evidence item's frequency window as a single portfolio. Requires an existing tracking row with frequency.

Instructions

Queue a windowed AI assessment that scores every file in the evidence item's frequency window as one portfolio (write — editor+ role, async). Returns 422 if tracking or frequency is missing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
org_idYesOrganization UUID — obtain from scf_list_organizations
evidence_idYesEvidence ID (e.g., 'E-IAM-01'). Tracking row with a frequency must exist — set via scf_update_evidence first
assessment_sourceNoOrigin tag for the request (default on_demand)on_demand
Behavior4/5

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

With no annotations, the description adequately discloses it is a write operation (editor+ role), async, and returns 422 for missing data. It does not cover rate limits or idempotency but is transparent about key behaviors.

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 efficient sentence plus a short error condition. Every word adds value: verb, target, role, async, error condition. No wasted text.

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 no output schema, the description covers key aspects: what it does, async nature, role requirement, and error condition. It does not mention response format or follow-up retrieval, but overall adequately complete for a trigger tool.

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% with descriptions already explaining parameters. The description adds some context (e.g., org_id from scf_list_organizations, evidence_id needs tracking/frequency) but does not significantly enhance beyond schema. Baseline 3.

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 it queues a windowed AI assessment scoring files as a portfolio, with role and async behavior. It distinguishes from sibling tools like scf_trigger_evidence_assessment and scf_bulk_assess_evidence by emphasizing 'windowed' and 'portfolio'.

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 description mentions preconditions (tracking/frequency must exist) and error condition, but does not explicitly compare with sibling tools like scf_bulk_assess_windows. Usage context is implied but not clearly delineated.

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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