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Imf Portwatch

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and no destructiveness. The description adds transparency about the internal process (probes each entity with ai_visibility_check) and output format (ranked list with score, confidence, signal density). No contradictions with annotations.

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?

The description is concise at three sentences, each serving a purpose: main action, operational details, and use case. No redundant information; front-loaded with the core purpose.

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?

Given no output schema, the description adequately covers output structure (ranked list with score, confidence, signal density). It explains how the tool works (calling ai_visibility_check) and provides a realistic example. All necessary context for an agent to understand when and how to use it is present.

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%, but the description adds valuable context: the first entity is the 'subject', array size constraint (2-8), example for context, and usage guidance for models ('Omit for just workers-ai'). These details enhance understanding beyond the schema.

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 a specific verb ('Compare', 'Probes', 'ranks', 'surfaces') and resource ('AI visibility across multiple entities'). It explicitly describes the tool as a side-by-side comparison, distinguishing it from siblings like ai_visibility_check (single entity) and compare_entities (generic).

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 provides a clear use case ('competitive AI-marketing audits') and implies that for a single entity one should use ai_visibility_check instead. It mentions treating the first entity as the subject. While not explicitly stating when not to use, the purpose is well-defined.

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

A4.2/5.0
Disambiguation4/5

Most tools have clear distinct purposes, but the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are very similar, and there is some overlap between bet_research and polymarket_edges. Overall, the majority are well-differentiated.

Naming Consistency5/5

Tool names follow a consistent snake_case verb_noun pattern with only minor deviations (e.g., 'chokepoints_list' vs 'chokepoint_daily_traffic'). The naming convention is predictable and clear.

Tool Count2/5

At 37 tools, the surface is overly large for a focused server. Many tools are meta-tools that could have been consolidated, and the count exceeds the recommended range (25+), making it feel heavy and unwieldy.

Completeness5/5

The server covers maritime chokepoints comprehensively (list, status, daily, compare, disruptions), and the Pipeworx-based tools provide broad coverage across financial, drug, prediction market, and general query domains. There are no obvious gaps for the stated scope.

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