Skip to main content
Glama

Diff

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

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, not destructive), the description discloses it probes each entity with ai_visibility_check, ranks by score, returns a ranked list with score/confidence/signal density, and treats the first entity as the 'subject'. 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 plus a short example, front-loaded with the core action. Every sentence earns its place; no redundancy.

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 covers input (entities, models, context, apiKey), behavior (probes, ranks), output (ranked list with score/confidence/signal density), and use case. Complete for an agent to select and invoke correctly.

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%, baseline 3. Description adds meaning for 'entities': explains first entry is the subject and rest are competitors, and clarifies that it probes with ai_visibility_check. For other parameters, description adds minimal value beyond 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 the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It distinguishes from sibling tool ai_visibility_check (single entity) and compare_entities (different comparison).

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 says 'Useful for competitive AI-marketing audits' and implies side-by-side comparison, indicating when to use. It does not explicitly name alternatives or exclusions, but the context of siblings provides differentiation. Clear but not fully explicit.

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

All Diff 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

A3.8/5.0
Disambiguation2/5

Several clusters of tools are hard to tell apart: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve overlapping query paths, and the five polymarket_* tools plus bet_research cover heavily overlapping prediction-market analysis. Some pairs are nearly identical in purpose, like ai_visibility_check vs scan_competitor_ai_presence, and the descriptions must be read closely to avoid misselection.

Naming Consistency2/5

All names are snake_case, but the naming style is highly inconsistent across the set: some use verb_noun (ask_pipeworx, generate_llms_txt, resolve_entity), some are noun phrases (entity_profile, pipeworx_feedback, recent_alerts), and some use a vendor prefix without a clear verb (polymarket_edges, polymarket_edge_tracker). The pattern shifts between domain-specific prefixes (polymarket_*, pipeworx_*) and generic verbs with no predictable rule.

Tool Count2/5

32 tools is too many for a cohesive server, especially when the surface sprawls across unrelated domains: data querying, prediction markets, memory, subscriptions, npm scanning, AI visibility checks, and llms.txt generation. Many tools could be consolidated (the ask_pipeworx family, the polymarket family, the entity-comparison family), which would make the count feel more justified.

Completeness4/5

Within its apparent purpose as a broad data-and-research assistant, the tool set is fairly complete: it covers entity resolution, lookup, grounded verification, deep research, comparisons, memory CRUD, subscription lifecycle, discovery, and feedback. Minor gaps exist, such as no direct tool to fetch a record by its pipeworx:// citation URI (search_within implies fetching happens elsewhere) and no evident update operation for stored memories beyond save/delete.

Resources