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Record a Prashna outcome

record_prashna_outcome

Closes the Prashna feedback loop using the private confirmation token returned by calculate_prashna. The token is hashed at rest and is distinct from the consultation ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNo
outcomeYes
resolvedAtNo
confirmationTokenYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
statusYes
recordedAtYes
consultationIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare this is a non-read-only, non-idempotent, non-destructive mutation. The description adds useful security context—the token is private, hashed at rest, and distinct from the consultation ID—but does not explain effects on the underlying Prashna record or whether an outcome can later be revised.

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 efficiently packed sentences with the action and token precondition front-loaded. Every clause contributes distinguishing or operational context without redundancy.

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?

With an output schema present and annotations covering the safety profile, the description need not explain return values or mutation flags. It supplies the key prerequisite and token semantics, though it could more explicitly separate this tool from record_consultation_outcome and clarify outcome update behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does so well for the critical confirmationToken parameter by explaining its origin and handling; the remaining parameters (outcome enum, notes, resolvedAt) are largely self-explanatory from the schema types and enum values.

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 action ('Closes the Prashna feedback loop') and resource (Prashna outcome), and ties the token to calculate_prashna. It clearly distinguishes the confirmation token from the consultation ID, though it does not explicitly differentiate itself from the sibling record_consultation_outcome.

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?

Provides the prerequisite that the confirmation token must be returned by calculate_prashna, and clarifies it is not the consultation ID. However, it gives no explicit when-not guidance and does not mention the alternative sibling record_consultation_outcome, leaving usage largely implied.

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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