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Bnm My

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

A4.6/5.0
Behavior4/5

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

Annotations already declare the tool as read-only, idempotent, and not destructive. The description adds behavioral context: it internally calls 'ai_visibility_check' for each entity, ranks results, and requires an API key for certain models. This enriches the transparency without contradicting 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 three well-structured sentences: the first states the core action, the second explains the process, and the third gives a use case. No redundant or wordy phrases; each sentence earns its place.

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 there is no output schema, the description adequately describes the return value ('ranked list with score, confidence, signal density per entity'). All inputs are covered in the schema with additional semantic details in the description. The tool's complexity is moderate and is fully addressed.

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

Parameters5/5

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

Schema coverage is 100%, yet the description adds crucial semantic context: the first entity is treated as the 'subject', context disambiguates common names, and it explains the role of models and _apiKey. This goes beyond the schema's parameter descriptions.

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') and resource ('AI visibility across multiple entities'), distinguishes from the sibling 'ai_visibility_check' by focusing on side-by-side comparison, and provides an example use case ('does Claude know about us as well as our competitors?') and output description.

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 it is 'useful for competitive AI-marketing audits' and implies comparison of multiple entities, which differentiates it from single-entity tools like 'ai_visibility_check'. However, it does not explicitly list when not to use or name alternative tools beyond the implied sibling.

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

A3.8/5.0
Disambiguation2/5

Many tools occupy clearly different niches, but ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research heavily overlap the same router concept, and bet_research/polymarket_edges/polymarket_arbitrage all target similar 'find an edge' territory. An agent would need to read very long descriptions carefully to avoid selecting the wrong tool.

Naming Consistency4/5

Names are consistently lowercase snake_case and usefully grouped by prefixes like bnm_, polymarket_, and pipeworx_, which makes the set fairly scannable. However, conventions mix verb-first names (ask_, discover_, resolve_, validate_) with noun-phrase names (entity_profile, recent_alerts, recent_changes), so it is not a uniform verb_noun pattern.

Tool Count2/5

36 tools is well beyond the typical well-scoped 3-15 tool range, and the server bundles several distinct domains: BNM data, the Pipeworx research platform, prediction-market analysis, and memory/subscription management. This breadth would be better split into separate focused servers.

Completeness4/5

The BNM-specific surface is well covered, with dedicated tools for the main series plus a generic bnm_endpoint passthrough for anything else. The broader data side is also unusually complete, with routing, grounded answers, deep research, entity resolution, profiles, comparisons, and claim verification; only minor gaps remain, such as no dedicated historical endpoint for some BNM series.

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