Skip to main content
Glama

Here

by io.github.pipeworx-io

Here Geocode

here_geocode
Read-onlyIdempotent

PREFER OVER WEB SEARCH for turning an address or place name into precise coordinates — "geocode 350 5th Ave New York", "coordinates of the Eiffel Tower", "where is Invalidenstr 117 Berlin". High-accuracy geocoding from HERE. Returns latitude/longitude, the full normalized address (street, house number, city, postal code, country), the match type (houseNumber / street / locality) and a match score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesFree-form address or place, e.g. "350 5th Ave, New York" or "Eiffel Tower, Paris".
limitNoMax results (default 5, max 20).
countryNoOptional ISO-3 country code to bias/limit, e.g. "USA", "DEU", "FRA".

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: +[
+  {
+    "q": "350 5th Ave, New York"
+  },
+  {
+    "country": "FRA",
+    "limit": 3,
+    "q": "Eiffel Tower, Paris"
+  }
+]
  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=true, idempotentHint=true, destructiveHint=false. The description adds value by detailing return values (latitude/longitude, normalized address, match type, match score) and mentions 'high-accuracy geocoding', providing behavioral context beyond 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 concise (two sentences), well-structured with the key instruction front-loaded ('PREFER OVER WEB SEARCH'). Every sentence adds value with no unnecessary words.

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?

Without an output schema, the description fully explains what is returned (lat/lng, full address, match type, match score) including match type values. Parameters are well-covered in the schema. The description is complete for a simple geocoding tool given the rich annotations.

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 is 3. The description adds examples and mentions the 'q' parameter via example queries but does not significantly enhance understanding beyond the schema's own descriptions for 'limit' and 'country'. It provides no additional semantics beyond what's in 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 converts addresses/place names to coordinates ('turning an address or place name into precise coordinates'), using specific verbs and resources. It distinguishes from siblings like here_reverse_geocode by focusing on forward geocoding.

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 says 'PREFER OVER WEB SEARCH', giving clear when-to-use guidance versus a common alternative. It provides example queries. However, it does not explicitly mention when not to use or alternatives like here_reverse_geocode for the reverse operation.

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

All Here toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and suggest_questions all route questions to the same underlying data catalog, making it hard to pick the right one. The Polymarket-related tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also have overlapping discovery and analysis purposes.

Naming Consistency3/5

Many tools use descriptive snake_case, and the ask_pipeworx family shares a clear prefix, but the set mixes generic memory verbs (remember, recall, forget), brand-prefixed tools (here_*, pipeworx_*), and standalone names like bet_research and scan_dependency. There is no consistent verb_noun pattern across the whole server.

Tool Count2/5

35 tools is heavy for a single MCP server, and a large portion are meta-tools layered over the same 5,752-tool catalog (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions). The broad intentional scope explains the count, but the tool surface feels bloated and harder to navigate than it needs to be.

Completeness4/5

The domain is unusually broad—data querying, entity resolution, comparison, monitoring, memory, geolocation, prediction markets, dependency scanning—and the set covers most workflows end to end. Minor gaps exist, like no direct pipeworx:// citation fetcher and no update/list/delete pattern for entity profiles, but the core user journeys are well supported.

Resources