Maritime Vessel Data MCP Server

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fleet_track

Retrieve historical AIS positions for every vessel over a specified time window, with identity and positions oldest first, for playback and verification against observed data.

Instructions

Where every vessel has been over the last few hours.

Returns JSON with a "data" block and a "fleet" list, one entry per vessel, each with its identity and its positions oldest first. Intended for playback and for checking work against what was actually observed.

Each position is one AIS report that was received. The gaps between them are real: the median vessel reports only a handful of times an hour, so two fixes an hour apart are two observations and not a path. Nothing is interpolated.

When "truncated" is true the row cap was reached and this is not the whole picture. Narrow the window rather than assuming the missing vessels are gone.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`hours`	No	How far back to look, in hours. A wider window returns proportionally more, so narrow it rather than raising the limit.
`limit`	No	Maximum positions to return across the whole fleet. When the cap is reached the response says so, rather than quietly returning less than was asked for.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 21, 2026

TDQS

A4.5/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full burden—and it succeeds. It discloses that positions are raw AIS reports, that gaps are real and not interpolated, that nothing is extrapolated, and that 'truncated' indicates the cap was reached. This gives the agent crucial expectations about data completeness and meaning beyond what the schema provides.

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 front-loaded with the core purpose, then gives output shape, intended use, data semantics, and truncation handling in a logical order. Every sentence adds value—none are filler or redundant with the schema. It is slightly longer than minimal, but the extra detail earns its place.

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 read-only fleet history tool with two well-described parameters and an output schema, this description is complete. It covers output structure, ordering, data fidelity, gaps, and truncation behavior. Nothing an agent needs to call it correctly or interpret results 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?

Schema coverage is 100%, and the schema already documents both parameters ('hours' and 'limit') with clear descriptions and defaults. The description adds behavioral context around truncation and window narrowing, but it does not add new parameter-level semantics. A baseline of 3 is appropriate because the schema does the heavy lifting.

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 colloquial summary ('Where every vessel has been') and then specifies the resource: JSON with a 'fleet' list, one entry per vessel, each with identity and positions. It is clearly distinct from siblings like vessel_track (single vessel) and vessels_in_area (current location) by emphasizing fleet-wide historical positions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states intended use cases: 'playback and for checking work against what was actually observed.' It also gives practical advice to narrow the window when truncation occurs. It does not name alternatives for exclusion, such as 'use vessel_track for a single vessel,' but the context is clear enough for an agent to select this tool appropriately.

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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