Datalastic Vessel Tracking & Maritime Intelligence

Request a bulk intelligence report (async)

intel_report_request

Submit an async job to export a FULL bulk dataset for one of the Maritime Reports add-on datasets (dry_dock_dates, casualty, inspections, sales_purchase_demolitions, ownership, class_society, engine, companies). This returns the ENTIRE dataset (all records), not one vessel — for a single vessel use the matching lookup tool instead (e.g. intel_ownership, intel_inspections). Like all reports it is asynchronous: this returns a report_id and a PENDING status without waiting; submit ONCE, then poll report_status with that report_id until DONE, which yields a result_url to hand to the user to download. Do not resubmit while a job is running, and if a report comes back FAILED, do not automatically submit a replacement — report the message to the user first. At most 10 reports may be pending per account at once (across every report type), so a retry loop can exhaust the queue. The server never downloads the file itself. Part of the Maritime Reports add-on.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`report_type`	Yes	Which bulk dataset report to generate. One of: dry_dock_dates, casualty, inspections, sales_purchase_demolitions, ownership, class_society, engine, companies.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`message`	Yes
`report_id`	Yes
`created_at`	Yes
`result_url`	Yes
`updated_at`	Yes
`report_type`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 3, 2026

addedOutput schema / properties / message

Added value: +{
+  "type": [
+    "null",
+    "string"
+  ]
+}

changedOutput schema / required

Previous value: -[
-  "report_id",
-  "report_type",
-  "status",
-  "result_url",
-  "created_at",
-  "updated_at"
-]New value: +[
+  "report_id",
+  "report_type",
+  "status",
+  "message",
+  "result_url",
+  "created_at",
+  "updated_at"
+]

AddedJul 14, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations only indicate non-read-only status. The description adds critical async behavior (returns report_id and _PENDING_), idempotency warnings (do not resubmit), failure handling (report _FAILED_ to user), queue limits (10 pending), and the server never downloading. This richly discloses behavioral traits beyond annotations.

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?

The description is long but information-dense, with each sentence earning its place: purpose, alternative, async contract, retry warnings, queue limit, and note that the server never downloads. It is front-loaded with the core purpose then flows into critical operational details; slightly verbose but justified by tool complexity.

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 complex async job submission tool, the description covers the full lifecycle: submission, polling via report_status, result_url handoff, failure handling, queue constraints, and scope (whole dataset vs single vessel). With an output schema present, no return-value details are needed, and nothing critical is missing.

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?

Input schema already has 100% coverage for the single report_type parameter, including the full dataset list. The description repeats the list but adds no semantic detail about individual datasets, meeting the baseline for full schema coverage.

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 states a specific action ('Submit an async job to export a FULL bulk dataset') with the exact resource (Maritime Reports add-on datasets) and explicitly contrasts single-vessel lookups. While it doesn't differentiate from the sibling report_request, the add-on qualifier and dataset list make the tool's role unambiguous.

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?

Provides explicit when-to-use guidance: points to lookup tools (intel_ownership, intel_inspections) for single vessels, and gives detailed workflow instructions (submit once, poll report_status, handle failures, avoid resubmission due to queue limits). This is exemplary usage guidance.

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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