Skip to main content
Glama

Data Kcmo

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety. The description adds context: it probes LLMs, returns per-model scores with confidence/signals/raw_response, and that passing an API key probes Anthropic with direct payment. 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?

Three concise sentences, front-loaded with purpose. Every sentence adds value with no redundancy or fluff.

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 full parameter schemas, comprehensive annotations, and no output schema, the description adequately summarizes return structure (per-model + combined view). All necessary information for an agent to use the tool effectively 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% with descriptions for all 4 parameters. The description adds meaning beyond schema by explaining the API key's payment model and context for disambiguation, and noting the default model. This elevates from baseline 3.

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 probes LLMs for knowledge about an entity and scores visibility (0-100). It specifies the resource (LLMs) and action (probe and score), and distinguishes from siblings by mentioning AI-marketing audits, pre-launch checks, and competitive monitoring.

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 indicates usage for AI-marketing audits, pre-launch brand checks, and competitive monitoring. It provides guidance on default model and when to include an API key, but does not explicitly exclude alternatives or compare to sibling tools.

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

All Data Kcmo 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

A3.9/5.0
Disambiguation2/5

The ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research cluster is highly overlapping—beta is explicitly identical right now and grounded differs mainly in answer extraction. Several other pairs (ai_visibility_check vs scan_competitor_ai_presence, and the six prediction-market tools) also blur boundaries, making misselection likely.

Naming Consistency3/5

Names are uniformly snake_case and benefit from clear prefixes (ask_pipeworx_, kcmo_, polymarket_, pipeworx_). However, conventions are mixed between bare verbs (forget, recall, remember), noun phrases (entity_profile, polymarket_edges, recent_alerts), and verb_noun forms, and similar names like polymarket_edges vs polymarket_edge_tracker add confusion.

Tool Count2/5

34 tools is well into the bloat range, and only 3 are actually Kansas City-specific despite the server name. The surface bundles prediction markets, memory, feedback, llms.txt generation, and npm scanning alongside data lookup, making it heavy and unfocused; several meta-tools could be collapsed.

Completeness4/5

As a read-only data research platform, the surface is quite complete: discovery, querying, grounded answers, entity resolution, profiles, comparisons, claim verification, subscriptions, and memory are all covered with few dead ends. Minor gaps exist—no subscription update, no direct citation-URI fetch tool, and a thin KC-specific set—but agents can work around them.

Resources