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chrishayuk

chuk-mcp-geocoder

by chrishayuk

distance_matrix

Compute haversine distance matrix between multiple points. Returns distances in meters for all pairs.

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?

No annotations exist, so the description bears full responsibility. It discloses that the computation is pure (no API calls), and the return format (distance matrix in metres). However, it does not mention potential performance constraints, maximum points, or error behavior. This is adequate but not exceptional.

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 front-loaded with the core purpose, followed by structured details. It is somewhat verbose with a multi-line docstring, but every sentence adds value. Could be slightly more concise, but overall well-organized.

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 the tool's simplicity (2 parameters, no output schema), the description covers input formats, output unit (metres), and computational nature. Limitations like maximum points or performance are not mentioned, but for a straightforward computation tool, this is largely sufficient. Sibling tools are distinct, so no additional context needed.

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?

The input schema provides no descriptions for parameters (0% coverage). The description compensates fully by explaining the points parameter in detail (two accepted formats: [lat,lon] pairs or objects with name/lat/lon) and notes the output_mode default and options ('json' or 'text'). This adds significant meaning beyond the 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 'Compute haversine distance matrix between multiple points.' The verb 'compute' and resource 'haversine distance matrix' are specific. This tool is distinct from sibling tools like geocode or reverse_geocode, which handle location lookups rather than pairwise distance calculations.

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 notes 'Pure computation — no API calls needed,' providing some context for when to use it (local computation). However, it does not explicitly state when to use this tool versus alternatives, nor does it give usage limitations or prerequisites. Sibling tools are different enough, but explicit 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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