Skip to main content
Glama

Shodan

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds behavioral details: return type (per-model scores, confidence, signals, raw_response, combined view), free default model, and BYO key requirement for Anthropic. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first sentence covers core function and output, second adds model details and use cases. Every sentence is necessary, concise, and front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 parameters, 1 required, and no output schema, the description compensates by outlining the return structure and typical use cases. It is nearly complete, though it could mention pagination or rate limits (if any).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all parameters. The description adds value beyond the schema by specifying the default model ('Workers AI Llama-3.3-70b') and clarifying the API key payment model ('you pay Anthropic directly').

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 ('probe') and names the resource ('one or more LLMs'), clearly scoping to brand/entity visibility scoring. It distinguishes from siblings like 'compare_entities' by focusing on LLM knowledge probing.

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 lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to provide an API key. However, it does not compare with similar tools like 'scan_competitor_ai_presence' or mention alternatives.

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

All Shodan 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

A3.5/5.0
Disambiguation2/5

The server name 'Shodan' misleadingly implies a narrow focus on network security scanning, yet the tool set includes many unrelated tools for data lookup (ask_pipeworx, deep_research, etc.), memory management, and subscriptions. Within the pipeworx tools, there is overlap between ask_pipeworx, ask_pipeworx_grounded, and deep_research, which can confuse agents about which to choose.

Naming Consistency2/5

Tool names mix conventions: some use underscores (ai_visibility_check, shodan_host), others use lowercased phrases without clear partitioning (ask_pipeworx, deep_research, entity_profile). Verb-noun patterns are inconsistent (e.g., 'scan_competitor_ai_presence' vs 'compare_entities'). This lack of a predictable naming scheme increases cognitive load.

Tool Count2/5

With 33 tools covering distinct domains (Shodan scanning, pipeworx data, memory, subscriptions), the server feels overloaded and unfocused. While each tool may individually be useful, the bundling contradicts the principle of a single-purpose server. A more appropriate count for a focused Shodan server would be under 10 tools.

Completeness2/5

For the Shodan domain, only three tools are provided (host, host_count, host_search), missing key capabilities like DNS lookups, vulnerability search, or API key management. The pipeworx tool set is extensive but not the server's advertised purpose, leaving gaps in both areas. The addition of memory and subscription tools adds unrelated functionality without completing any single domain.

Resources