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delimit

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Python
Hybrid

Delimit Intel Query

delimit_intel_query
Read-onlyIdempotent

Search saved intel snapshots by keyword, date, or dataset. Retrieve matching ingested intel, optionally scoped to one dataset.

Instructions

Search saved intel snapshots by keyword, date, or dataset.

When to use: to surface ingested intel matching a query, optionally scoped to one dataset. When NOT to use: to ingest new data (use delimit_intel_snapshot_ingest) or list datasets (delimit_intel_dataset_list).

Sibling contrast: delimit_intel_snapshot_ingest writes; this reads back filtered snapshots.

Side effects: read-only. Calls backends.tools_data.intel_query. Coerces parameters from JSON string to dict via _coerce_dict_arg.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoKeyword search string. Empty = all.
dataset_idNoOptional dataset to scope the query to.
parametersNoOptional dict with date_from, date_to, limit. Accepted as JSON string and coerced.

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 / dataset_id / description
      Previous value: -"Optional dataset to filter by."New value: +"Optional dataset to scope the query to."
    • changedInput schema / properties / parameters / description
      Previous value: -"Optional params (date_from, date_to, limit)."New value: +"Optional dict with date_from, date_to, limit. Accepted as JSON string and coerced."
    • changedInput schema / properties / query / description
      Previous value: -"Keyword search string."New value: +"Keyword search string. Empty = all."
  2. Changed4 schema fields changedv4.5.5
    • addedInput schema / properties / dataset_id / description
      Added value: +"Optional dataset to filter by."
    • changedInput schema / properties / parameters / anyOf
      Previous value: -[
-  {
-    "additionalProperties": true,
-    "type": "object"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "type": "string"
+  },
+  {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  {
+    "type": "null"
+  }
+]
    • addedInput schema / properties / parameters / description
      Added value: +"Optional params (date_from, date_to, limit)."
    • addedInput schema / properties / query / description
      Added value: +"Keyword search string."
  3. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds value by stating the backend call target and the parameter coercion behavior. This gives an agent concrete expectations about internal behavior beyond the annotation flags.

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, front-loaded with the core purpose, and uses labeled sections for usage guidance and side effects. Every sentence earns its place with no redundancy or filler.

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?

With a rich output schema, complete parameter schema, clear annotations, and explicit sibling alternatives, nothing critical is missing. The description fully equips an agent to invoke the tool correctly and understand its side effects.

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

Parameters3/5

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

Schema coverage is 100%, so the input schema already fully documents query, dataset_id, and parameters. The description adds a helpful high-level summary of search dimensions ('keyword, date, or dataset') but does not materially go beyond the schema's parameter descriptions.

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 a specific verb and resource: 'Search saved intel snapshots by keyword, date, or dataset.' It clearly differentiates from siblings by naming exactly what this tool is not (ingest, dataset listing) and contrasting it with delimit_intel_snapshot_ingest.

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 gives explicit 'When to use' and 'When NOT to use' guidance, naming the exact alternative tools for ingestion and dataset listing. This leaves no ambiguity about when to select this tool over its siblings.

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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