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Create Data Type

create_data_type

Define a custom data type to track new events or metrics with your own fields, so you can record and organize personalized data in Fulcra.

Instructions

Create a user-defined data type so the user can track something new.

For new tracking prefer base_type "event" (something that happens, at a time or over a range) or "metric" (a measured number); both can add custom fields. The older annotation bases "moment", "duration", "boolean", "numeric" and "scale" (a 1-5 rating) still work but take no fields.

Record with record_data (custom fields go in its fields); get_data_catalog(data_type=) lists a type's fields; get_records reads it back. Reversible with archive_data_type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesHuman-readable name (e.g. "Caffeine Intake").
tagsNoTag names to attach; missing tags are created. Annotation bases only.
unitNoUnit for recorded values (e.g. "mg"). metric/numeric only.
scaleNoValue range, {"min": 1, "max": 10, "step": 1}. metric only.
fieldsNoJSON Schema of custom fields. event/metric only. E.g. {"properties": {"mood": {"type": "string"}}, "required": ["mood"]}.
base_typeYesWhat kind of values this type records (see above).
value_mapNoLabels for values, e.g. {"1": "low", "5": "high"}. metric only.
descriptionNoWhat the type tracks. Required for event/metric.
metric_kindNo"cumulative" or "discrete". metric/boolean/numeric/scale only.
scale_labelsNoExactly 5 labels for the 1-5 scale values. scale only.
default_valueNoValue pre-filled when recording. boolean/numeric only.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.1.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations only supply destructiveHint=false, so the description carries most of the behavioral load: it discloses reversibility via archive_data_type and the structural constraint that older bases reject custom fields. It does not cover name-uniqueness behavior, permission requirements, or side effects on existing records, keeping it short of a 5.

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?

Three short, front-loaded paragraphs: purpose, base_type choice, then tool workflow. No filler sentences; each line either routes the agent or prevents a misuse (e.g., fields on legacy bases).

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, so return values need no explanation, and the description covers the surrounding workflow and reversibility. Missing only edge-case behavior (duplicate names, tags auto-creation semantics on legacy bases) for a mutation tool with sparse annotations.

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 3 is the floor, but the description goes beyond the schema by explaining the base_type lifecycle tradeoff (new vs. legacy bases and which accept `fields`), which is decision-relevant guidance the per-parameter descriptions don't frame. It does not add syntax detail beyond the schema, so it stops short of 5.

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 opening sentence states a specific verb and resource ('Create a user-defined data type') plus the intent ('so the user can track something new'). It cleanly separates itself from record_data (writing values), get_data_catalog (reading metadata), and archive_data_type (removal).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit routing: prefer base_type 'event' or 'metric' for new tracking, with the older annotation bases named and characterized ('take no fields'). It also names the follow-on and reversal tools (record_data, get_data_catalog, get_records, archive_data_type), so the agent knows the full workflow around this call.

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