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Glama

Datalastic Vessel Tracking & Maritime Intelligence

by com.datalastic

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
NameRequiredDescriptionDefault
toNoEnd date YYYY-MM-DD. Optional for request_usage (<=31 days after from); required for inradius_history (<=7 days after from).
latNoCenter latitude. Required for inradius_history.
lonNoCenter longitude. Required for inradius_history.
fromNoStart date YYYY-MM-DD. Optional for request_usage; required for inradius_history.
radiusNoRadius in nautical miles, max 50. Required for inradius_history.
report_typeYesWhich report to generate: request_usage, vessel_list, port_list, or inradius_history.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
messageYes
report_idYes
created_atYes
result_urlYes
updated_atYes
report_typeYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • 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"
+]
  2. First observed

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden, and it delivers: it discloses async execution, immediate return of report_id with _PENDING_ status, no waiting, the 10-pending-report limit, credit consumption, and that the server never downloads report files. This is rich behavioral context beyond what the schema or output schema would reveal.

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 long but every sentence earns its place, with the core async behavior and polling workflow front-loaded before the supported report types. The report_type list is scannable and organized, with no redundant filler or repetition of schema details.

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?

This is a complex async tool, and the description covers the full lifecycle: submission, polling, failure handling, concurrency limit, credit warning, downloading, and routing to the sibling tool intel_report_request. An agent has everything it needs to invoke this correctly and avoid common misuse.

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?

Although schema coverage is 100%, the description adds significant parameter-level meaning: it maps each report_type to its parameters, specifies date ranges (<=31 days, <=7 days), radius limit (<=50 NM), required vs optional conditions, and which report types are free versus credit-consuming. This goes well beyond the schema 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 clear verb and resource: 'Submit an async report job.' It names the exact report_type values (request_usage, vessel_list, port_list, inradius_history) and explicitly distinguishes this tool from report_status and intel_report_request, so an agent can tell them apart without ambiguity.

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 workflow instructions: submit once, poll report_status, do not resubmit while running, and do not auto-submit replacements after FAILED. It also names the alternative for add-on bulk datasets ('use intel_report_request instead') and advises informing the user before submitting credit-consuming reports.

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

All Datalastic Vessel Tracking & Maritime Intelligence toolsInspect
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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: vessel tracking (get_vessel, get_vessel_pro, get_vessel_history, get_vessel_info, estimated_vessel_position), registry search (find_vessels, find_ports), port details (get_port), weather (get_weather), sea route (sea_route), async reports (report_*), and intelligence modules (intel_*). Even similar tools like find_vessels vs get_vessel are well-differentiated by their descriptions.

Naming Consistency5/5

All tool names follow a consistent lowercase underscore pattern with a verb_noun structure (e.g., get_vessel, find_ports, sea_route). The intel_* tools use a uniform prefix for intelligence operations, maintaining predictability across the set.

Tool Count3/5

With 25 tools, this server is at the upper boundary of the borderline range. While the tools are well-organized and necessary for the comprehensive maritime intelligence domain, the count feels slightly heavy compared to an ideal 3-15 tool scope.

Completeness5/5

The tool surface is remarkably complete: it covers vessel tracking (live, historic, estimated), registry search, port details, weather, sea route calculation, async report management, and a full suite of intelligence reports (casualties, inspections, ownership, etc.). There are no obvious gaps for maritime vessel tracking and intelligence tasks.

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