How do I get a Mapillary access token?

Sign up at [**mapillary.com/developer**](https://www.mapillary.com/developer) and create an application to get your access token.

What is a bounding box?

A bounding box defines a geographic area using min_longitude, min_latitude, max_longitude, max_latitude. Example: "-0.15,51.50,-0.10,51.52" covers central London.

What map features are available?

Mapillary detects traffic signs (stop, speed limit, yield, etc.), road objects (fire hydrants, mailboxes, etc.) and road markings. Use get_map_features to search by area.

Mapillary MCP Server for Pydantic AI 7 tools — connect in under 2 minutes

Name: Mapillary
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Mapillary through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Mapillary "
            "(7 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Mapillary?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Mapillary MCP Server

Connect to Mapillary and access the world's largest street-level imagery platform through natural conversation.

Pydantic AI validates every Mapillary tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Image Search — Search street-level images by geographic bounding box
Image Details — Get image metadata including GPS coordinates, capture date, compass angle and sequence
Sequence Search — Find image sequences (connected images along routes) by area
Map Features — Search detected traffic signs, objects and road markings by area
Object Detections — Get all detected objects in a specific image

The Mapillary MCP Server exposes 7 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Mapillary to Pydantic AI via MCP

Follow these steps to integrate the Mapillary MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 7 tools from Mapillary with type-safe schemas

Why Use Pydantic AI with the Mapillary MCP Server

Pydantic AI provides unique advantages when paired with Mapillary through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Mapillary integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Mapillary connection logic from agent behavior for testable, maintainable code

Mapillary + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Mapillary MCP Server delivers measurable value.

Type-safe data pipelines: query Mapillary with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Mapillary tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Mapillary and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Mapillary responses and write comprehensive agent tests

Mapillary MCP Tools for Pydantic AI (7)

These 7 tools become available when you connect Mapillary to Pydantic AI via MCP:

get_detection_value

Returns the detected value, type, GPS coordinates and image association. Get details for a specific object detection

get_image

Returns image ID, capture date/time, GPS coordinates, compass angle, sequence ID, organization and thumbnail URL. Use fields parameter to request additional data like "geometry,compass_angle,captured_at,sequence,thumb_256_url,thumb_1024_url,altitude". Get details for a specific Mapillary image

get_image_detections

Returns detection values, types, geometry and confidence scores. Get object detections for a specific image

get_map_features

Returns feature type, value, GPS coordinates and detection confidence. Useful for traffic sign inventory and road infrastructure analysis. Search map features (traffic signs, objects) by area

get_sequence

Returns sequence ID, creation date and related images. Get details for a specific image sequence

search_images

Returns image IDs, coordinates, capture dates, compass angles and sequence IDs. Bbox format: min_lon,min_lat,max_lon,max_lat (e.g. "-0.15,51.50,-0.10,51.52" for central London). Search street-level images by geographic area

search_sequences

Returns sequence IDs and metadata for all sequences that pass through the area. Search image sequences by geographic area

Example Prompts for Mapillary in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Mapillary immediately.

"Find street-level images in central London."

"Search for traffic signs in São Paulo."

"Get object detections for image abc123."

Troubleshooting Mapillary MCP Server with Pydantic AI

Common issues when connecting Mapillary to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Mapillary + Pydantic AI FAQ

Common questions about integrating Mapillary MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Mapillary MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.