Skip to main content
Glama
IBM

Chuk MCP Maritime Archives

by IBM

maritime_aggregate_track_tortuosity

Aggregate and compare tortuosity of historical ship tracks to test whether marine chronometers reduced meandering or if wind changes improved routes.

Instructions

Aggregate route tortuosity across CLIWOC tracks with optional comparison.

Tests the chronometer hypothesis: if marine chronometers improved navigation, tortuosity should decrease over time. If tortuosity stays constant while speed DiD shows asymmetric gains, that confirms wind change rather than better routing.

Args: group_by: "decade", "year", "direction", "nationality" lat_min/lat_max/lon_min/lon_max: Bounding box nationality: Filter by nationality code year_start/year_end: Filter by year range direction: Filter by "eastbound" or "westbound" month_start/month_end: Month filter (supports wrap-around) min_speed_km_day: Minimum speed filter (default: 5.0) max_speed_km_day: Maximum speed filter (default: 400.0) min_positions: Minimum positions in bbox (default: 5) r_min: Minimum tortuosity R to include (e.g. 1.0 excludes artifacts) r_max: Maximum tortuosity R to include (e.g. 5.0 excludes loiterers) period1_years: First period as "YYYY/YYYY" range or "YYYY,YYYY,..." list period2_years: Second period as "YYYY/YYYY" range or "YYYY,YYYY,..." list n_bootstrap: Bootstrap iterations (default: 10000) output_mode: Response format - "json" (default) or "text"

Returns: JSON or text with per-group tortuosity stats, optional comparison

Tips for LLMs: - group_by="decade" to see tortuosity trends over time - Use direction="eastbound" vs "westbound" separately - r_min=1.0, r_max=5.0 focuses on normal transit voyages - period1_years/period2_years for formal comparison with CI - Periods accept "YYYY/YYYY" ranges or "YYYY,YYYY,..." year lists - Combine with maritime_did_speed_test for complete decomposition - min_positions=5 filters out short transits

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
group_byNodecade
lat_minNo
lat_maxNo
lon_minNo
lon_maxNo
nationalityNo
year_startNo
year_endNo
directionNo
month_startNo
month_endNo
min_speed_km_dayNo
max_speed_km_dayNo
min_positionsNo
r_minNo
r_maxNo
period1_yearsNo
period2_yearsNo
n_bootstrapNo
output_modeNojson
Behavior4/5

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

No annotations provided, so description must disclose behavior. It explains aggregation, hypothesis, and parameter effects. However, it does not explicitly state that it is a read-only operation, though it is implied by the analysis nature. Adequately transparent.

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 well-organized with sections (main description, Args, Returns, Tips). It is somewhat long but each sentence adds value. Minor redundancy could be trimmed (e.g., repeating period format in Args and Tips).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 20 parameters and no output schema, the description covers purpose, parameters, and tips comprehensively. It lacks detailed return format beyond 'JSON or text', but the hypothesis context compensates. Mostly complete.

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 0%, so the description fully explains each parameter, including defaults, constraints, and examples. The 'Args' section adds meaning beyond the schema, covering all 20 parameters.

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 clearly states that the tool aggregates route tortuosity across CLIWOC tracks to test the chronometer hypothesis. It distinguishes itself from sibling tools like 'maritime_track_tortuosity' (singular track) and 'maritime_did_speed_test' (speed decomposition) by specifying the aggregate and comparison functionality.

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?

Explicitly explains when to use: for testing chronometer hypothesis, comparing periods. Provides usage guidelines and tips, including combining with other tools like maritime_did_speed_test. Implicitly advises against using for individual track analysis.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/IBM/chuk-mcp-maritime-archives'

If you have feedback or need assistance with the MCP directory API, please join our Discord server