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Bhupendrajee07

MapmyIndia Mappls MCP Server

Mappls Get Directions

mappls_get_directions

Get driving, walking, biking, or trucking directions between any two points in India using coordinates or eLocs, with optional waypoints.

Instructions

Get driving, walking, biking, or trucking navigation directions between origin and destination coordinates/eLocs in India.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originYesStart location as "longitude,latitude" or eLoc (e.g. "77.2090,28.6139" or "DEL110").
profileNoTransport profile mode: "driving", "biking", "walking", or "trucking". Default is "driving".driving
resourceNoRoute resource type: "route_adv", "route_eta", or "route". Default is "route_adv".route_adv
waypointsNoOptional semicolon-separated intermediate coordinates or eLocs (e.g. "77.25,28.62;77.30,28.63").
destinationYesEnd location as "longitude,latitude" or eLoc (e.g. "77.3725,28.6273" or "MMI123").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are present, so the description carries the full behavioral burden. It only discloses vehicle modes and India coverage; it does not mention route semantics (e.g., turn-by-turn instructions, traffic/toll behavior), API requirements, or limits. This is a thin behavioral profile for a routing tool.

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 tight single sentence that front-loads the action and packs in modes, endpoints, and region. Every word contributes to core meaning; there is no redundant or filler text.

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

Completeness3/5

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

The schema fully documents parameters and an output schema covers return shape, so the minimal description is serviceable. However, it omits sibling differentiation and usage context, and with no annotations the behavioral side is thin, leaving the definition merely adequate.

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 description coverage is 100%, and each parameter is already documented in the schema. The description simply restates origin/destination as coordinates/eLocs without adding new meaning, so the schema baseline applies.

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 opens with a specific action, 'Get ... navigation directions', and identifies the resource and supported modes (driving/walking/biking/trucking) plus the geographic scope. This makes it distinct from the geocoding/search siblings and from mappls_distance_matrix, which focuses on distance/time 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 Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool does but never states when an agent should choose it over mappls_distance_matrix, snap_to_road, or other siblings. There are no exclusions, prerequisites, or route-selection scenarios, leaving the selection decision entirely to inference.

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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