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fastmcp-me

chuk-mcp-geocoder

by fastmcp-me

distance_matrix

Compute haversine distances between multiple geographic points using pure computation. Accepts points as coordinates or named objects, returns distance matrix in metres.

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
Behavior5/5

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

With no annotations provided, the description fully bears the burden of behavioral disclosure. It clearly states that no API calls are made, explains input formats, output mode, and the return value (NxN distance matrix in metres). This is comprehensive for a computation-only tool.

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 reasonably concise, using a docstring format with Args/Returns sections. It could be slightly more front-loaded (the first sentence is clear), but overall every sentence adds value with no redundancy.

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 the tool has only 2 parameters, no output schema, and no annotations, the description is fully complete: it explains both input formats, the output mode, and the return type (distance matrix in metres). No gaps remain.

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 must compensate. It does so thoroughly: details both point formats (array or object), auto-naming behavior, and output_mode options (json/text default). This adds significant meaning beyond the raw 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?

The description clearly states it computes a haversine distance matrix between multiple points. It specifies that it is pure computation without API calls, and provides input format examples. The purpose is distinct from sibling tools (geocoding, routing).

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 says 'Pure computation — no API calls needed,' which helps an agent choose this offline tool over potentially slower or costlier alternatives. It implies appropriate usage but does not list exclusions or when-not-to-use.

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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