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log_record

Store hard data (invoices, deadlines, measurements) as records that override recalled facts on conflict. Upserts on kind, title, and date.

Instructions

Store hard data (invoice, deadline, measurement…). Records beat recall: when one disagrees with a remembered fact, the record wins. Upserts on (kind, title, date).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYes
titleYes
amountNo
occurred_onYes
payload_jsonNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden. It usefully discloses the upsert key (kind, title, date), which tells the agent existing records with that key will be overwritten. However, it says nothing about permissions, conflict resolution details, or what happens to non-key fields on upsert.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences with the core purpose front-loaded and examples inline. The value proposition sentence earns its place by clarifying precedence over memory, though it could be trimmed slightly.

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

Completeness3/5

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

An output schema exists, so return values need no explanation. But for a 5-parameter mutation tool with zero annotation and schema-description coverage, the description covers only three parameters and omits amount/payload_json semantics and error behavior, leaving real gaps.

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

Parameters2/5

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 for all five parameters. It only references kind, title, and the date via the upsert key, leaving amount and payload_json completely unexplained, which is a significant gap for a 5-param tool.

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 (store/log) and resource (hard data records) with concrete examples like invoice, deadline, and measurement. It is distinguishable from memory-oriented siblings such as write_memory and log_friction, though it never names them explicitly.

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 line 'Records beat recall: when one disagrees with a remembered fact, the record wins' implies when this tool should take precedence over memory tools, but it gives no explicit when-to-use or when-not-to-use guidance relative to siblings like write_memory or log_friction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.