Datalastic Vessel Tracking & Maritime Intelligence

Request an async report

report_request

Submit an async report job. Reports are generated in the background and can take a while (seconds to many minutes), so this returns a report_id and an initial status (PENDING) — it does NOT wait. Submit ONCE, then poll report_status with the returned report_id until DONE, which yields a result_url. Do not resubmit while a job is running. 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; further submissions are rejected until some finish. When done, give the user the result_url to download; the server never downloads report files itself.

Supported report_type values:

request_usage: a FREE log of your account's API usage (endpoint, credits, timestamp); optional from/to (<=31 days), default last month.
vessel_list: the full vessel database (no other parameters).
port_list: the full ports database (no other parameters).
inradius_history: all vessels that passed through an area in a time window; REQUIRES lat, lon, radius (<=50 NM), from and to (<=7 days apart).

Note: vessel_list, port_list and inradius_history consume API credits (vessel_list and inradius_history can be substantial) — it's good to tell the user before submitting. For the add-on bulk datasets (ownership, inspections, etc.) use intel_report_request instead.

Input Schema

TableJSON Schema

Name	Required	Description
`to`	No	End date YYYY-MM-DD. Optional for request_usage (<=31 days after from); required for inradius_history (<=7 days after from).
`lat`	No	Center latitude. Required for inradius_history.
`lon`	No	Center longitude. Required for inradius_history.
`from`	No	Start date YYYY-MM-DD. Optional for request_usage; required for inradius_history.
`radius`	No	Radius in nautical miles, max 50. Required for inradius_history.
`report_type`	Yes	Which report to generate: request_usage, vessel_list, port_list, or inradius_history.

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"
+]

First observedJul 4, 2026

TDQS

A5/5.0

Behavior5/5

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

Annotations are minimal (readOnlyHint=false, openWorldHint=true, etc.), but the description richly discloses behavior: async execution, immediate return of report_id and _PENDING_ status, polling requirement, failure handling, account limit of 10 pending reports, and that the server never downloads files. It also notes credit consumption for some report types. No contradiction with annotations.

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?

Though long, every sentence earns its place. The description front-loads the critical async behavior, then systematically covers report types and parameters, then credit notes. No redundant or filler content; structure is logical and scannable.

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?

Given the complexity (6 params, multiple report types, async behavior, credit implications), the description is fully complete. It covers return behavior, polling, failure handling, limits, and user communication. The presence of an output schema covers return format, and the description complements it well.

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

Parameters5/5

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

Schema coverage is 100% (all params have descriptions), but the description adds substantial per-report-type semantics: which parameters are required/optional, constraints (radius <=50 NM, date ranges), and default behavior (request_usage defaults to last month). This goes well beyond the schema.

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?

States a specific action ('Submit an async report job') and resource ('async report'), and distinguishes itself from intel_report_request and report_status. The description also enumerates the supported report types, making its scope 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 guidance on when to use: submit once, poll with report_status, don't resubmit while running, handle failures by reporting to user, and account limits. It also directs users to intel_report_request for add-on bulk datasets, giving clear alternatives.

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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