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Glama

Tomtom

by io.github.pipeworx-io

Route

route
Read-onlyIdempotent

Traffic-aware routing / directions: calculate the best route between two points and return distance plus travel time computed with live traffic. Reports traffic delay so you get realistic ETAs, not free-flow estimates. Supports car, truck, pedestrian, and bicycle travel modes. Example: route({ from_lat: 40.748, from_lon: -73.985, to_lat: 40.689, to_lon: -74.044 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
to_latYesDestination latitude
to_lonYesDestination longitude
_apiKeyNoOptional — your own TomTom API key for higher limits; omit to use the shared Pipeworx key.
from_latYesOrigin latitude
from_lonYesOrigin longitude
travel_modeNoTravel mode for traffic-aware routing (default "car")

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-tomtom-api-key",
+    "from_lat": 40.748,
+    "from_lon": -73.985,
+    "to_lat": 40.689,
+    "to_lon": -74.044
+  },
+  {
+    "_apiKey": "your-tomtom-api-key",
+    "from_lat": 51.5074,
+    "from_lon": -0.1278,
+    "to_lat": 51.5165,
+    "to_lon": -0.0945,
+    "travel_mode": "pedestrian"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds that it uses live traffic and reports traffic delay, providing behavioral context beyond annotations. No contradictions.

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 three sentences plus an example, front-loading key information (traffic-aware, returns distance/time). No redundant words.

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

Completeness4/5

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

Given the tool's complexity, schema covers all parameters, and annotations provide safety profile. Description explains return values (distance, travel time, traffic delay) sufficiently. No output schema needed.

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 descriptions cover all 6 parameters (100% coverage). Description adds an example and mentions travel modes, but does not significantly enhance parameter meaning beyond the schema. Baseline 3 is appropriate.

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 it provides traffic-aware routing/directions, calculates best route, returns distance and travel time with live traffic. It distinguishes from sibling tools like geocode (address to coordinates) and search_poi (POI search) by focusing on route calculation between two 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 specifies when to use (for live traffic ETAs, delays) and mentions supported travel modes. However, it does not explicitly exclude alternatives among siblings, but since no other routing sibling exists, this is adequate.

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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TDQS

A3.9/5.0
Disambiguation3/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical behavior), and ask_pipeworx_grounded are variants of the same router, and the six polymarket_* tools plus bet_research all operate in the same prediction-market space. The extremely detailed descriptions help an agent differentiate, but misselection risk remains real.

Naming Consistency4/5

Snake_case is used consistently and most tools follow a verb_noun pattern (resolve_entity, validate_claim, discover_tools), with predictable polymarket_ and pipeworx_ family prefixes. Minor deviations exist — entity_profile and recent_alerts are noun/adjective phrases, generate_llms_txt embeds a file extension, and single-word verbs (remember, route, geocode) break the strict pattern — but overall naming is coherent.

Tool Count2/5

At 35 tools, the server exceeds the comfortable range and bundles many unrelated domains: data lookup, prediction markets, geocoding/navigation, memory, subscriptions, AI visibility, npm scanning, and llms.txt generation. While every tool has a distinct purpose, the surface is heavy and would benefit from splitting into focused servers.

Completeness4/5

Each major cluster has strong lifecycle coverage: data lookup (router, grounded mode, deep research, discovery), company research (resolve, profile, compare, changes), prediction markets (research, arb, edges, fill risk, cross-venue spread), memory (remember/recall/forget), and subscriptions (subscribe/list/unsubscribe/alerts). Minor gaps exist — no direct Polymarket order placement and no explicit tool for fetching pipeworx:// URIs (left to resources) — but agents can accomplish the stated purposes.

Resources