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Delimit Intel Dataset Register

delimit_intel_dataset_register

Register a new dataset in the file-based intel registry at the start of collection, with optional schema and description, so future ingest calls write to it.

Instructions

Register a new dataset in the file-based intel registry.

When to use: at the start of an intel collection effort, to declare a named dataset that future ingest calls will write to. When NOT to use: to write data into an existing dataset (use delimit_intel_snapshot_ingest) or to inventory datasets (delimit_intel_dataset_list).

Sibling contrast: delimit_intel_dataset_list inventories; delimit_intel_dataset_freeze locks; this creates.

Side effects: writes a registry entry via backends.tools_data.intel_dataset_register. Coerces schema from a JSON string to a dict via _coerce_dict_arg.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesDataset name (key). Required.
schemaNoOptional JSON schema as dict or JSON string.
descriptionNoHuman-readable description for the registry.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv4.7.9
    • changedInput schema / properties / description / description
      Previous value: -"Human-readable description."New value: +"Human-readable description for the registry."
    • changedInput schema / properties / name / description
      Previous value: -"Dataset name."New value: +"Dataset name (key). Required."
    • changedInput schema / properties / schema / description
      Previous value: -"Optional JSON schema for the dataset."New value: +"Optional JSON schema as dict or JSON string."
  2. Changed4 schema fields changedv4.5.5
    • addedInput schema / properties / description / description
      Added value: +"Human-readable description."
    • addedInput schema / properties / name / description
      Added value: +"Dataset name."
    • changedInput schema / properties / schema / anyOf
      Previous value: -[
-  {
-    "additionalProperties": true,
-    "type": "object"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "type": "string"
+  },
+  {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  {
+    "type": "null"
+  }
+]
    • addedInput schema / properties / schema / description
      Added value: +"Optional JSON schema for the dataset."
  3. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already convey readOnlyHint=false and destructiveHint=false, but the description adds useful behavioral detail: it writes a registry entry through a specific backend path and coerces schema from JSON string to dict. It does not fully describe duplicate-name behavior or error handling, but it goes beyond what annotations alone provide.

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?

The description is compact and well-structured with labeled sections for usage, non-usage, sibling contrast, and side effects. Every part adds decision-relevant information, and important content is front-loaded in the opening sentence.

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?

For a registration tool with a small input schema and an output schema, this description is complete enough: it tells the agent when to use it, when not to, how it contrasts with siblings, and what side effects to expect. The presence of an output schema means return-value details are not required in the description.

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 the baseline is 3, but the description adds meaningful parameter-related context by explicitly noting that the schema argument is coerced from a JSON string to a dict. This helps the agent understand how to supply the schema parameter beyond the schema's own type declaration.

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 opens with the specific action 'Register a new dataset' and names the target resource ('file-based intel registry'). It further distinguishes the tool from siblings by stating 'this creates' relative to inventory and freeze operations, so there is no ambiguity about what the tool does.

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?

The description explicitly provides 'When to use' and 'When NOT to use' guidance, naming the exact alternative tools for writing data (delimit_intel_snapshot_ingest) and inventorying datasets (delimit_intel_dataset_list). This gives an agent clear decision criteria and removes reliance on 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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