Skip to main content
Glama
IBM

chuk-mcp-geocoder

by IBM

distance_matrix

Compute a haversine distance matrix between multiple geographic points, returning distances in metres for every pair.

Instructions

Compute haversine distance matrix between multiple points.

    Pure computation — no API calls needed. Accepts points as either
    [lat, lon] pairs or {"name": ..., "lat": ..., "lon": ...} objects.

    Args:
        points: JSON array of points. Each point is either:
                - [lat, lon] pair (auto-named "Point 1", "Point 2", ...)
                - {"name": "Label", "lat": 40.0, "lon": -105.0}
        output_mode: "json" (default) or "text"

    Returns:
        NxN distance matrix in metres between all point pairs
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pointsYes
output_modeNojson
Behavior3/5

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

With no annotations, description carries full burden. It discloses input formats and return type, but omits limitations such as maximum number of points or error handling for invalid coordinates.

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?

Structured with purpose, args, and returns sections. Every sentence adds value, though the Args section could be more terse. Overall efficient.

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?

Covers input, output (NxN matrix in metres), and behavior (pure computation). Lacks constraints like maximum points, but sufficient for a simple calculation tool without output schema.

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%, but description compensates thoroughly: explains points as JSON array with two alternative formats and clarifies output_mode default. This adds critical meaning beyond the bare 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?

Description clearly states it computes a haversine distance matrix between points, specifying 'Pure computation — no API calls needed.' It distinguishes itself from sibling tools like geocode and route_waypoints by focusing on straight-line distances.

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?

Mentions 'no API calls needed' implying offline use, but lacks explicit guidance on when to use versus siblings like route_waypoints for along-route distances. No conditions or exclusion criteria are provided.

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-geocoder'

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