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Glama

Ripe Stat

by io.github.pipeworx-io

Network Info

network_info
Read-onlyIdempotent

"What ASN does IP [X] belong to" / "network info for [IP]" / "what prefix is [IP] in" — get the allocated prefix and originating ASN for an IPv4/IPv6 address. Use for "which network owns this IP", DDoS source attribution, hosting-provider lookup.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resourceYesIPv4/IPv6 address

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoNetwork info including allocated prefix and ASN
statusNoAPI response status

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: +[
+  {
+    "resource": "8.8.8.8"
+  },
+  {
+    "resource": "2001:4860:4860::8888"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "description": "Network info including allocated prefix and ASN",
+      "type": "object"
+    },
+    "status": {
+      "description": "API response status",
+      "type": "string"
+    }
+  },
+  "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 readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds that it returns prefix and ASN, which aligns with annotations. No additional behavioral context (e.g., rate limits) needed for a simple read operation.

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?

Two lean sentences, front-loaded with natural language queries, then formal description and use cases. No filler or redundancy.

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?

For a simple tool with one parameter, full schema coverage, rich annotations, and an output schema (indicated), the description fully explains purpose, input, output, and usage scenarios without gaps.

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 has 100% description coverage for the single 'resource' parameter, including examples. The description echoes that it accepts IPv4/IPv6 addresses but adds no new semantics beyond what the schema provides. 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?

Description starts with concrete example queries ('What ASN does IP [X] belong to') and clearly states the tool retrieves 'the allocated prefix and originating ASN for an IPv4/IPv6 address'. It lists specific use cases (DDoS source attribution, hosting-provider lookup), distinguishing it from siblings like whois or as_overview.

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?

Description explicitly says 'Use for' with three usage scenarios. It provides clear context but does not explicitly exclude cases or mention alternatives. However, given straightforward purpose and sibling tools, this is sufficient.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially in networking and domain-specific queries. However, the multiple `ask_pipeworx` variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and `deep_research` have overlapping functionality, which could cause occasional misselection.

Naming Consistency2/5

Tool names use inconsistent conventions: some are concatenated (geoloc, whois), some use underscores (abuse_contact, as_overview), and some are multi-word (scan_competitor_ai_presence, ask_pipeworx_grounded). No consistent pattern across the set.

Tool Count3/5

39 tools is high for a single server, but justified by the broad scope covering networking, finance, prediction markets, AI visibility, and more. However, many tools are meta-tools for the Pipeworx ecosystem, making the set feel slightly bloated.

Completeness5/5

The server covers an exceptionally wide range of domains comprehensively, with tools for networking, company data, FDA datasets, prediction markets, weather, news, and more. It includes memory and subscription management, leaving few obvious gaps for its stated data-retrieval purpose.

Resources