Dayze — Life Context

Reject Learned Fact

reject_learned_fact

Soft-reject an owned learned fact (stated + stale). Restorable with confirm_learned_fact. Contact-backed facts must be fixed in Contacts. Supply request_id for safe retries. ($0.10; API key required)

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": {
      "fact_id": "<fact_id>",
      "request_id": "<request_id>"
    },
    "name": "reject_learned_fact"
  }
}

Input Schema

Name	Required	Description
`fact_id`	Yes	Learned fact id, e.g. 3f2a1b4c-8d9e-6f01-c2b3-c4d5e6f70809.
`request_id`	Yes	Client idempotency key; a retry with the same key returns the original result, e.g. req_2026-10-17_001.
`idempotency_key`	No	Alias for request_id.

Output Schema

Name	Required	Description
`ok`	Yes
`code`	No
`fact`	No
`error`	No
`account`	No	The Dayze account this data belongs to. With more than one Dayze connection, name it in the answer.
`provenance`	No	Which connector wrote the record and which connected Dayze account received it.
`recordState`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedNov 9, 2026

addedInput schema / properties / fact_id / description

Added value: +"Learned fact id, e.g. 3f2a1b4c-8d9e-6f01-c2b3-c4d5e6f70809."

addedInput schema / properties / request_id / description

Added value: +"Client idempotency key; a retry with the same key returns the original result, e.g. req_2026-10-17_001."

AddedNov 9, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations only say it is a non-read, non-destructive mutation; the description adds the far more useful traits: the rejection is soft and reversible via confirm_learned_fact, some facts are rejected by a different subsystem, request_id enables safe retries, and the call costs $0.10 with an API key. These are exactly the facts the annotation block cannot convey.

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?

Four short sentences, each carrying distinct information: what it does, how to undo it, the excluded case, and the retry/cost/auth constraints. The scope qualifier is front-loaded and nothing is repeated.

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

Completeness5/5

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

Return values are covered by the output schema, so the description is free to spend its words on reversibility, the contact-backed exception, idempotency, cost, and auth. Nothing an agent needs in order to call this correctly is missing.

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 coverage is 100%, so fact_id and the request_id/idempotency_key alias are already documented. The description still earns credit by explaining why request_id matters ('for safe retries'), turning a mechanical idempotency field into actionable operational advice.

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 gives a specific verb ('Soft-reject') plus the resource ('owned learned fact') and narrows the target with the parenthetical '(stated + stale)'. The word 'soft' and the restore path immediately separate it from hard-delete style behavior and from confirm_learned_fact.

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?

It states a clear precondition (fact must be stated and stale), the reversal path (confirm_learned_fact), and an explicit exclusion (contact-backed facts must be fixed in Contacts). The one gap is that it never distinguishes this from correct_learned_fact, so the agent must infer when to reject rather than correct.

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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