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Propose scores

propose_scores

For the signed-in client: records scores your assistant proposes for vendors in your evaluation, each a whole number from 1 to 5 or not_applicable, with an optional note. A proposal is never a score: it becomes one only when the person signed in to this account confirms the set on PreShiftIQ's confirm page, from the link this tool returns.

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": {
      "proposals": "<proposals>"
    },
    "name": "propose_scores"
  }
}

Input Schema

NameRequiredDescriptionDefault
proposalsYes
assessment_idNo

Output Schema

NameRequiredDescriptionDefault
errorsNo
statusYes
summaryYes
expires_atYes
confirm_urlYes
proposals_recordedNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare a non-destructive, non-idempotent, closed-world write, and the description adds genuinely new context: entries are provisional, they only become real scores after the account holder confirms on the PreShiftIQ confirm page, and the tool returns a link to that page. That is exactly the behavioral detail an agent needs before writing. It stops short of covering repeat-submission behavior or what happens to an existing proposal set.

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?

Two tight sentences, front-loaded with who and what, followed by the crucial proposal-versus-score distinction. No filler or restated field names beyond what adds meaning.

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?

An output schema exists and the description still usefully notes it returns a confirm link, so return values are covered. For a two-parameter write tool the remaining gap is minor: assessment_id and the per-item identifiers are never characterized, but the critical semantic (proposals are provisional) is fully explained.

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 0%, so the description must carry the load, and it only partially does: it explains the score value domain (1-5 whole numbers or not_applicable) and the optional note, but says nothing about assessment_id, category_id, vendor_id, or the 1-200 item batch limit.

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?

States a specific verb and resource: records proposed vendor scores for an evaluation, scoped to the signed-in client. An agent can tell this creates draft/proposed scores rather than final ones, though it never names or contrasts the nearby siblings (request_proposals, submit_proposal_package) that could plausibly be confused with it.

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 implies usage by explaining the proposal-then-confirm workflow, which signals this is the right tool when the assistant has candidate scores but the human has not yet approved them. It gives no explicit when-to-use or when-not-to-use guidance against alternatives such as submit_proposal_package, so routing still requires inference.

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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