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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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and no destructiveness. The description adds that it probes with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density, providing good behavioral context beyond 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?

Two sentences that are concise and front-loaded. Every sentence adds value: first states purpose and method, second gives use case and output. No wasted words.

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?

Given no output schema, the description explains the return format (ranked list with score, confidence, signal density). It covers usage context and method. Could mention the first entity as subject (already in schema) but not critical for completeness.

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 coverage is 100%, so the schema already describes parameters. The description does not add parameter-level details beyond what the schema provides, meeting the baseline for high coverage.

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 compares AI visibility across multiple entities side-by-side, using specific verbs like 'Compare', 'Probes', 'ranks', and 'surfaces'. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities by focusing on competitive audits and ranking.

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 states the use case ('competitive AI-marketing audits') and hints at when to use it. However, it does not explicitly mention when not to use it or alternatives like compare_entities, leaving slight ambiguity.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, and deep_research, which all query Pipeworx data in different modes. However, their descriptions clearly differentiate them. Similarly, memory and subscription tools are separate. Overall, an agent can distinguish tools with moderate effort.

Naming Consistency4/5

Tool names mostly follow verb_noun pattern with snake_case, such as ask_pipeworx, compare_entities, generate_llms_txt. However, a few tools like 'datasets', 'metadata', and 'query' are single nouns, breaking the pattern. Overall, naming is consistent enough for an agent to predict behavior.

Tool Count3/5

33 tools is above the typical range for an MCP server, but the server covers a wide domain (SEC, FRED, FDA, prediction markets, etc.) with specialized tools. The count is borderline heavy but justifiable given the scope. Some tools like memory and subscription management add to the count but serve necessary auxiliary functions.

Completeness4/5

The tool surface is comprehensive for its intended domain of structured data querying and analysis. It covers data retrieval, entity resolution, comparison, news, subscriptions, and memory. Minor gaps include dependency scanning only for npm and lack of direct web search, but meta-tools like ask_pipeworx fill many needs. Overall, it supports common workflows well.

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