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Glama

Overpass

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a raw Overpass QL query against OpenStreetMap. Use for complex spatial queries the helper tools can't express. Example: [out:json][timeout:25]; area["name"="Berlin"][admin_level=4]->.a; node["amenity"="library"](area.a); out body;. Returns the raw Overpass JSON (elements array with node/way/relation).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qqlYesFull Overpass QL query string. Start with `[out:json][timeout:<n>];` and end with `out body;` (or similar output statement).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of elements returned
elementsYesArray of normalized OSM elements

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: +[
+  {
+    "qql": "[out:json][timeout:25]; area[\"name\"=\"Paris\"][admin_level=4]->.a; node[\"amenity\"=\"restaurant\"](area.a); out body;"
+  },
+  {
+    "qql": "[out:json][timeout:30]; way[\"building\"=\"yes\"][\"height\">20](bbox:48.8,2.2,48.9,2.4); out body;"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "count": {
+      "description": "Number of elements returned",
+      "type": "number"
+    },
+    "elements": {
+      "description": "Array of normalized OSM elements",
+      "items": {
+        "properties": {
+          "id": {
+            "description": "OSM element ID",
+            "type": "number"
+          },
+          "latitude": {
+            "description": "Latitude coordinate or null if unavailable",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "longitude": {
+            "description": "Longitude coordinate or null if unavailable",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "name": {
+            "description": "OSM name tag value or null",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "osm_url": {
+            "description": "Direct link to element on OpenStreetMap",
+            "type": "string"
+          },
+          "tags": {
+            "description": "All OSM tags as key-value pairs",
+            "type": "object"
+          },
+          "type": {
+            "description": "OSM element type",
+            "enum": [
+              "node",
+              "way",
+              "relation"
+            ],
+            "type": "string"
+          }
+        },
+        "required": [
+          "type",
+          "id",
+          "osm_url",
+          "latitude",
+          "longitude",
+          "name",
+          "tags"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "count",
+    "elements"
+  ],
+  "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 the tool as read-only, idempotent, and non-destructive. The description adds context about the return format ('Returns the raw Overpass JSON (elements array with node/way/relation)') and includes an example with a timeout, which helps set expectations. It does not disclose potential pitfalls like timeout errors or rate limits, but the annotation coverage reduces the burden.

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 two sentences plus a full example, which is concise and front-loaded with the core action. Every sentence adds value: the action, the context for when to use it, and the output format. The example is self-explanatory and helps demonstrate usage without unnecessary verbosity.

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 complexity and the presence of an output schema, the description is complete. It tells the agent what the tool does, when to use it, what the query format looks like (via example), and what the return format is. Annotations cover safety, and the example addresses potential timeout settings. No significant gaps remain.

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% and the parameter description already explains the expected format ('Start with `[out:json][timeout:<n>];` and end with `out body;`'). The description's example duplicates the schema examples and adds no new meaning about the parameter itself. It does not explain edge cases or variations, so it meets the baseline but does not exceed it.

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 uses a specific verb and resource: 'Run a raw Overpass QL query against OpenStreetMap.' It clearly distinguishes from siblings by noting it is for 'complex spatial queries the helper tools can't express.'

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 states when to use the tool: 'Use for complex spatial queries the helper tools can't express.' This clearly implies that simpler tools like pois_near or places_in_bbox should be preferred when they suffice. However, it does not name specific alternatives or provide explicit 'when not to use' examples, so it falls just short of a 5.

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

A3.8/5.0
Disambiguation3/5

Most tools have distinct purposes, but the ask_pipeworx trio (stable, beta, grounded) are near-identical variants, and the five polymarket_* tools plus bet_research heavily overlap in the edge-finding space. Detailed descriptions help, but an agent could easily misselect among these clusters.

Naming Consistency3/5

Names are all snake_case and readable, but conventions vary: ask_pipeworx_* uses a prefix pattern, polymarket_* is consistent, yet others mix verbs (scan_competitor_ai_presence, generate_llms_txt) with nouns (entity_profile, resolve_entity). No single verb_noun pattern governs the set.

Tool Count2/5

34 tools is well above the 25-tool threshold and feels like multiple servers merged into one: structured data routing, prediction markets, OSM, memory, subscriptions, and AI-visibility checks. The breadth is impressive but the count is heavy for a single tool surface.

Completeness4/5

The surface is notably complete for its blended domain: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe, data access has multiple router modes plus deep research and claim validation, and prediction markets have research, edge, arbitrage, fill-risk, and cross-venue tools. Minor gaps exist (no subscription update, no explicit reverse-geocoding tool), but core workflows are covered.

Resources