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Glama

Openrouteservice

by io.github.pipeworx-io

Elevation Point

elevation_point
Read-onlyIdempotent

"Elevation / altitude at [coordinates]" / "how high is [location]" / "height above sea level for [lat,lng]" — elevation (meters above sea level) at a single point. Use for "what's the elevation of Denver", terrain queries, or attaching altitude to GPS records.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geometryYesPer format_in (point: [lon, lat])
format_inYespoint | geojson

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": "point",
+    "geometry": [
+      8.681495,
+      49.41461
+    ]
+  },
+  {
+    "format_in": "geojson",
+    "geometry": {
+      "coordinates": [
+        2.352222,
+        48.856613
+      ],
+      "type": "Point"
+    }
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "Elevation at point response from OpenRouteService",
+  "type": "object"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the description adds value by specifying the output unit ('meters above sea level') and the single-point constraint. No contradictions 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 sentence with embedded examples and usage hints, fully front-loaded with the key terms. Every word is purposeful and contributes to clarity.

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, straightforward purpose) and the presence of an output schema, the description is complete. It covers the main use case and parameter details sufficiently.

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 100%, so baseline 3 applies. The description provides example geometries and explains the 'format_in' values, but these details are also in the schema. No significant extra meaning beyond the schema.

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 returns elevation/altitude at a single point, provides example queries ('how high is [location]'), and distinguishes from the sibling tool 'elevation_line' by specifying 'at a single point'.

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 mentions use cases like 'what's the elevation of Denver', terrain queries, and attaching altitude to GPS records. It does not explicitly state when not to use, but the context and sibling list imply alternatives like elevation_line for lines.

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.

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