Skip to main content
Glama

Shodan

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that it internally calls ai_visibility_check and returns a ranked list with score, confidence, and signal density, providing context beyond annotations without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is three sentences, front-loading the main action and methodology. It is concise with no filler, though it could be slightly more structured (e.g., bullet points for output).

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?

Without an output schema, the description adequately explains the return format (ranked list with fields). It covers use case and methodology. However, it omits details on error handling or rate limits, which are not critical given the annotations.

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 description coverage is 100%, so baseline is 3. The description does not add significant meaning beyond the schema; it mentions entities but the schema already explains the first entry as subject. No extra parameter context is provided.

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 clearly states the tool's purpose: to compare AI visibility across multiple entities side-by-side, probe with ai_visibility_check, rank by score, and surface most/least recognized. It distinguishes from sibling tool ai_visibility_check, which handles single entities.

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 gives a concrete use case (competitive AI-marketing audits) and implies it is for multiple entities, contrasting with single-entity checks. However, it does not explicitly exclude single entities or name ai_visibility_check as the alternative, which would make guidance stronger.

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