Skip to main content
Glama

Mapbox

by io.github.pipeworx-io

Isochrone

isochrone
Read-onlyIdempotent

"How far can I get in [N] minutes from [point]" / "reachable area in [time]" / "drive-time / walk-time / cycle-time polygons" / "delivery zone within [radius]" / "service area around [location]" — isochrone polygons (reachable area within X minutes or meters) from a starting coordinate by car, walking, or cycling. Use for site-selection, delivery-zone, commute-shed, and "how big a catchment do I have" questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
denoiseNo
profileYes
polygonsNo
generalizeNo
coordinatesYes[lon, lat]
contours_colorsNo
contours_metersNo
contours_minutesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      [
  {
    "contours_minutes": "10,20,30",
    "coordinates": [
      -122.4194,
      37.7749
    ],
    "profile": "driving"
  },
  {
    "contours_minutes": "5,10,15",
    "coordinates": [
      -74.006,
      40.7128
    ],
    "polygons": true,
    "profile": "walking"
  }
]
    • changedOutput schema / (root)
      Before
      null
      After
      {
  "description": "Mapbox Isochrone API response with reachability contours",
  "type": "object"
}
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Despite strong annotations (readOnlyHint, idempotentHint, etc.), the description adds valuable behavioral context: it generates polygons, supports time/distance contours, and profiles. It does not explain data sources or caveats, but with annotations already covering safety, this is adequate.

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 a bit verbose with natural language query examples, but the structure is effective: it opens with example queries, then gives the core definition and use cases. Every part adds context, though it could be trimmed without losing meaning.

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?

The description covers the main functionality, input semantics, and typical use cases. Since an output schema exists, return details are not needed here. It is sufficiently complete for an agent to decide when and how to use the tool, despite some param gaps.

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

Parameters3/5

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

Schema coverage is only 13%, so the description must compensate. It explains key parameters: profile (car/walking/cycling), coordinates (start point), contours_minutes/meters (time/radius), and polygons (output type). However, denoise, generalize, and contours_colors are left undocumented, leaving gaps for those 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 the tool computes isochrone polygons (reachable area within X minutes or meters) from a starting coordinate, with explicit mention of transport modes (car, walking, cycling). It distinguishes this from sibling tools like directions by focusing on area/reachability rather than routes.

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?

Explicit use cases are given: site-selection, delivery-zone, commute-shed, and catchment questions. While it doesn't explicitly mention alternatives, the purpose and examples strongly convey when this tool is appropriate relative to routing tools.

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

All Mapbox toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and several tools serve similar data-retrieval functions, making it difficult for an agent to distinguish which to use.

Naming Consistency4/5

Tool names mostly follow a consistent verb_noun pattern (e.g., geocode_forward, generate_llms_txt, resolve_entity). A few less descriptive names (forget, recall) exist but overall naming is predictable.

Tool Count2/5

38 tools is far too many for a server branded as 'Mapbox'. Only about 8 tools directly relate to map/geospatial functionality; the rest are unrelated (Pipeworx data, Polymarket, memory). The scope is dramatically overextended.

Completeness2/5

The Mapbox-specific tools lack coverage of major features like style management, tilesets, or data upload. The non-Mapbox tools cover their domains moderately, but the server's overall completeness for its named purpose (Mapbox) is severely lacking.

Resources