batch_geolocateLook up locations for up to 100 IP addresses at once. Returns geolocation and ISP data in the same order as input. Use for analyzing multiple IPs efficiently.
| Name | Required | Description | Default |
|---|---|---|---|
| ips | Yes | Array of IPv4 or IPv6 addresses to look up (maximum 100) |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of results returned | |
| results | Yes | Array of geolocation results or error objects |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of results returned",
+ "type": "integer"
+ },
+ "results": {
+ "description": "Array of geolocation results or error objects",
+ "items": {
+ "oneOf": [
+ {
+ "properties": {
+ "as_number": {
+ "description": "Autonomous System number and name",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "city": {
+ "description": "City name",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "country": {
+ "description": "Country name",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "country_code": {
+ "description": "ISO 3166-1 alpha-2 country code",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "ip": {
+ "description": "The queried IP address",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "isp": {
+ "description": "Internet Service Provider name",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "latitude": {
+ "description": "Latitude coordinate",
+ "type": [
+ "number",
+ "null"
+ ]
+ },
+ "longitude": {
+ "description": "Longitude coordinate",
+ "type": [
+ "number",
+ "null"
+ ]
+ },
+ "organization": {
+ "description": "Organization name",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "postal_code": {
+ "description": "ZIP or postal code",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "region": {
+ "description": "State/region name",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "timezone": {
+ "description": "IANA timezone identifier",
+ "type": [
+ "string",
+ "null"
+ ]
+ }
+ },
+ "required": [
+ "ip",
+ "country",
+ "country_code",
+ "region",
+ "city",
+ "postal_code",
+ "latitude",
+ "longitude",
+ "timezone",
+ "isp",
+ "organization",
+ "as_number"
+ ],
+ "type": "object"
+ },
+ {
+ "properties": {
+ "error": {
+ "description": "Error message from lookup failure",
+ "type": "string"
+ },
+ "ip": {
+ "description": "The queried IP address",
+ "type": [
+ "string",
+ "null"
+ ]
+ }
+ },
+ "required": [
+ "ip",
+ "error"
+ ],
+ "type": "object"
+ }
+ ]
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "count",
+ "results"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "ips": [
+ "8.8.8.8",
+ "1.1.1.1",
+ "208.67.222.222"
+ ]
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is well-covered. The description adds valuable behavioral context by stating the result order matches input order and the maximum batch size of 100, which is not solely derivable from annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three short, front-loaded sentences. Each sentence contributes uniquely: the core action, the output behavior/order, and the intended use case. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single-parameter tool with full schema coverage, an output schema, and clear annotations, the description covers all necessary aspects: purpose, batch limit, output contents, ordering guarantee, and usage context. The tool is simple enough that nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides a complete description of the 'ips' parameter ('Array of IPv4 or IPv6 addresses to look up (maximum 100)'). The description repeats the limit but adds no extra parameter-specific semantics, so it does not go beyond the schema's coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('look up locations') and resource ('IP addresses'), with clear scope ('up to 100 at once'). It distinguishes itself from sibling geolocate_ip by emphasizing batch processing and return of geolocation and ISP data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use for analyzing multiple IPs efficiently,' which clearly implies batch usage. It does not explicitly mention the alternative single-IP tool geolocate_ip, but the sibling context and batch emphasis make the appropriate use case clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tool families have genuine boundary ambiguity: ask_pipeworx and ask_pipeworx_beta are currently identical in behavior, and the five Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) all live in the 'find/pursue an edge' space, requiring an agent to parse very long descriptions to choose correctly. The IP, memory, and subscription tools are clearly distinct, but the overlap-prone families cause real misselection risk.
All names are snake_case and each family is internally consistent (polymarket_*, ask_pipeworx_*, subscribe/unsubscribe, remember/recall/forget). However, conventions mix across the set: verb_noun (geolocate_ip, resolve_entity, validate_claim) coexists with noun-first names (entity_profile, pipeworx_feedback, recent_alerts) and bare verbs (remember, forget), so there is no predictable global pattern.
At 33 tools the set exceeds the heavy threshold, and the server name 'iplookup' covers only 2 of them; the remaining 31 form a sprawling data-research, prediction-market, memory, and subscription platform with tangential utilities like generate_llms_txt and scan_dependency. The broad scope means few tools are individually useless, but the server reads as a kitchen sink rather than a focused toolkit.
As an IP-lookup service the surface is thin: only geolocation and ISP data, with no WHOIS, reverse DNS, proxy/VPN detection, or reputation records. As a data-research platform the surface is strong (universal query routing, grounded verification, entity profiles, comparisons, subscription lifecycle, memory). This lopsidedness makes the actual domain ambiguous and leaves the namesake use case under-covered.