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Glama

Openrouteservice

by io.github.pipeworx-io

Elevation Line

elevation_line
Read-onlyIdempotent

"Elevation profile along a route" / "hiking trail elevation gain" / "topographic profile of [path]" / "how hilly is this route" — elevation samples along a geometry (linestring). Pass geojson / polyline / encodedpolyline5 / encodedpolyline6. Use for hiking elevation gain, cycling grade analysis, trail planning, terrain profiles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geometryYesPer format_in
format_inYesgeojson | polyline | encodedpolyline5 | encodedpolyline6

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
      Added value: +[
+  {
+    "format_in": "geojson",
+    "geometry": {
+      "coordinates": [
+        [
+          8.681495,
+          49.41461
+        ],
+        [
+          8.687872,
+          49.420318
+        ]
+      ],
+      "type": "LineString"
+    }
+  },
+  {
+    "format_in": "polyline",
+    "geometry": "u{~vFvyys@fS"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "Elevation along line response from OpenRouteService",
+  "type": "object"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds input format details (geojson, polyline, etc.) and usage scenarios but does not elaborate beyond the annotations' safety profile.

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?

A single, well-structured sentence that front-loads the purpose with quoted synonyms, followed by a list of use cases. Every phrase adds value without redundancy.

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

Completeness5/5

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), annotations covering safety, and an existing output schema, the description provides all necessary context: purpose, input formats, and typical applications.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by explicitly listing supported geometry formats and indicating the geometry is a linestring, reinforcing the schema's format_in enum and geometry description.

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 provides elevation samples along a linestring geometry using verbs like 'elevation profile', 'elevation gain', and 'topographic profile'. It distinguishes from the sibling 'elevation_point' by focusing on routes vs points.

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 explicitly lists use cases: hiking elevation gain, cycling grade analysis, trail planning, terrain profiles. It does not provide exclusions or direct comparisons, but the sibling 'elevation_point' implicitly covers when not to use this tool.

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

All Openrouteservice toolsInspect
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TDQS

B3.2/5.0
Disambiguation1/5

The tool set is a chaotic mix of geographic routing, AI visibility, betting analysis, memory storage, and random utilities. Many tools overlap in purpose (e.g., multiple data lookup tools like ask_pipeworx, discover_tools, resolve_entity), and the domain is completely inconsistent, making it nearly impossible for an agent to distinguish which tool to use for a given task.

Naming Consistency1/5

Tool names follow no consistent pattern; they mix snake_case (ai_visibility_check, ask_pipeworx), camelCase (generate_llms_txt), and arbitrary verbs without a clear verb_noun structure. Some names are vague (processV2-like patterns are absent, but e.g., 'forget' is a single verb). This chaotic naming prevents an agent from predicting tool functions.

Tool Count1/5

With 27 tools covering routing, AI marketing, betting, memory, and more, the count is extremely mismatched for the server's implied purpose ('Openrouteservice'). Even ignoring the name, the number is high and the scope is far too broad, making the set unwieldy and unfocused.

Completeness1/5

No coherent domain can be inferred from the tool set; it is an arbitrary collection. The routing tools are present but overshadowed by unrelated tools. For any single domain (e.g., betting or routing), the surface is either incomplete or includes extraneous tools, leaving the set severely lacking a clear purpose.

Resources