Maritime Vessel Data MCP Server

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search_vessels

Search maritime vessels by type, flag, or status. Get JSON with match and returned counts, plus vessel details. Truncated results are clearly indicated.

Instructions

Search the vessel dataset by type, flag state, and/or navigational status.

Any argument left blank is ignored, and surrounding whitespace is ignored. Returns JSON with a "data" block stating the source, a "matches" count of everything that matched, a "returned" count of how many are included, and a "vessels" list. When returned is less than matches the list was capped by the limit argument: raise it or narrow the search to see the rest. When data.source is "snapshot" the positions are from a fixed sample dataset and are not current: say so when answering. Fields that AIS has not reported yet are null, never guessed.

Input Schema

TableJSON Schema

Name	Required	Description
`flag`	No	Flag state to match, case insensitive and partial, for example Malaysia or Singapore. Leave blank to match any flag.
`limit`	No	Maximum number of vessels to return, between 1 and 200. The response always reports how many matched, so a truncated answer is visible rather than silent.
`status`	No	Navigational status to match, case insensitive and partial. Examples: Under way, At anchor, Moored. Leave blank to match any status.
`vessel_type`	No	Ship category to match, case insensitive and partial. Examples: Tanker, Cargo, Passenger, Tug, Fishing. Leave blank to match any type.

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?

With no annotations, the description carries the full burden of behavioral disclosure, and it excels: it describes the JSON response shape, the cap/truncation behavior with a visible returned-vs-matches distinction, the snapshot data caveat, and the guarantee that unreported fields are null rather than guessed. These are exactly the non-obvious behaviors an agent needs to know.

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 dense but every sentence earns its place. It front-loads the core purpose, then systematically covers response structure, truncation, data freshness, and null handling without repetition 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?

Given four optional parameters, no annotations, and an output schema, the description covers everything needed to call the tool correctly: matching semantics, whitespace handling, result counting, the limit cap, the snapshot caveat, and null-field behavior. No critical operational context is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds meaningful global parameter semantics beyond the schema: blank arguments are ignored and surrounding whitespace is ignored, which affects how the agent should construct call arguments. It also clarifies the limit's effect on the result count, adding value over the schema alone.

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 names a specific verb and resource (search the vessel dataset) and precisely defines the filtering dimensions: type, flag state, and navigational status. It clearly differentiates this tool from area- or track-based sibling tools without needing to open their schemas.

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

Usage Guidelines3/5

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

The description implies when to use the tool: when searching by flag, status, or vessel type. However, it never names alternative sibling tools or states when not to use this tool, so explicit routing guidance is missing.

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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