ipSearch urlscan.io past scan results for a specific IP address (no API key required); returns scan records for pages hosted at or contacting that IP.
| Name | Required | Description | Default |
|---|---|---|---|
| ip | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| took | No | Query execution time in milliseconds | |
| total | No | Total number of results for this IP | |
| results | No | Array of scan results for the IP | |
| has_more | No | Whether more results are available |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "ip": "192.168.1.1"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Search results for a specific IP address",
+ "properties": {
+ "has_more": {
+ "description": "Whether more results are available",
+ "type": "boolean"
+ },
+ "results": {
+ "description": "Array of scan results for the IP",
+ "items": {
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "took": {
+ "description": "Query execution time in milliseconds",
+ "type": "number"
+ },
+ "total": {
+ "description": "Total number of results for this IP",
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds useful behavioral context like 'no API key required' and specifies the return type (scan records for pages hosted at or contacting that IP). 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. Every word adds value.
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 the tool's simplicity, the output schema exists, and the parameter is straightforward, the description provides all necessary context: what it does, how it works (no API key), and what it returns.
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 has 0% description coverage for the 'ip' parameter. The description only mentions 'specific IP address' without adding format details (e.g., IPv4/IPv6). For a simple string parameter, more context would help, but the parameter name and type are self-explanatory.
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 clearly states the tool searches urlscan.io past scan results for a specific IP address, with a specific verb and resource. It distinguishes from siblings like 'domain' by focusing on IP addresses.
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 provides clear context for when to use the tool (to search for an IP address) but does not explicitly mention when not to use it or alternative tools. It gives enough guidance for typical usage.
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.
Many tools have distinct purposes with thorough descriptions, but there is meaningful overlap among ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research, all of which route questions to data sources. The multiple polymarket tools (edges, arbitrage, fill_risk, edge_tracker, kalshi_spread) also require careful reading to differentiate. The urlscan tools (domain, ip, search, submit, result) are distinct, but the overall set mixes several unrelated domains, increasing misselection risk.
Naming is a mix of conventions: short urlscan verbs (domain, ip, search, submit), noun-first names (entity_profile, recent_changes, deep_research), verb_noun names (compare_entities, resolve_entity, generate_llms_txt), and prefixed families (polymarket_*, pipeworx_*). There is no uniform verb_noun pattern or consistent prefix convention across the set. This inconsistency makes it hard to predict what a tool does from its name alone.
With 36 tools, the set is far above the 3-15 range typical for a coherent server, even for a broad data API. The inclusion of meta-tools like discover_tools and suggest_questions suggests the count is so high that agents need help navigating it. The load is compounded by tools spanning urlscan.io, Pipeworx, prediction markets, memory, subscriptions, and feedback, making the server feel like a grab bag rather than a focused service.
The urlscan portion is complete for searching, submitting, and retrieving scan results, and the Pipeworx side covers a wide range of data and analysis capabilities. However, the server is named 'Urlscan Io' while most tools are unrelated to urlscan, creating a mismatch between the stated purpose and the actual surface. There are no obvious gaps for the included features, but the lack of a coherent domain makes it hard to assess what 'complete' means for this set.