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Glama

Mapbox

by io.github.pipeworx-io

Map Matching

map_matching
Read-onlyIdempotent

"Snap GPS trace to roads" / "clean up noisy GPS coordinates" / "match track to road network" — takes a sequence of GPS points and returns the most likely path along the road network. Use for vehicle telematics, fitness-tracker cleanup, or "what road did this driver actually take" questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tidyNo
stepsNo
profileNo
overviewNo
radiusesNo
geometriesNo
timestampsNo
annotationsNo
coordinatesYes

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
      [
  {
    "coordinates": [
      [
        -122.4194,
        37.7749
      ],
      [
        -122.4189,
        37.7751
      ],
      [
        -122.4183,
        37.7754
      ]
    ]
  },
  {
    "coordinates": [
      [
        -74.006,
        40.7128
      ],
      [
        -74.0055,
        40.7135
      ],
      [
        -74.005,
        40.7142
      ]
    ],
    "overview": "full",
    "profile": "driving",
    "steps": true
  }
]
    • changedOutput schema / (root)
      Before
      null
      After
      {
  "description": "Mapbox Map Matching API response with snapped GPS trace",
  "type": "object"
}
  2. First observed

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds the 'most likely path' probabilistic nuance and noise-cleanup intent, but lacks details on road network coverage, accuracy, or rate limits. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence with front-loaded problem framings. Every phrase adds value, and it avoids any redundant repetition of schema or annotation info.

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

Completeness2/5

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

While an output schema exists, the tool has 9 parameters with zero descriptions in the schema and almost no parameter guidance in the description. The agent can understand the tool's purpose but cannot confidently configure optional inputs like profile or overview, making the description incomplete for a tool of this complexity.

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

Parameters2/5

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

Schema description coverage is 0%, and the description only references 'a sequence of GPS points', which vaguely maps to the required coordinates parameter. It provides no explanation for the other 8 parameters (profile, overview, radiuses, tidy, steps, etc.), leaving the agent without critical configuration guidance.

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 explicitly states the tool takes a sequence of GPS points and returns the most likely path along the road network. The alternative phrasings ('Snap GPS trace to roads', 'clean up noisy GPS coordinates') clarify intent and distinguish it from sibling tools like directions or geocoding.

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 provides clear use cases: vehicle telematics, fitness-tracker cleanup, and 'what road did this driver actually take' questions. This gives the agent strong contextual signals for when to use the tool, though it does not explicitly mention when not to use it.

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

All Mapbox toolsInspect
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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