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Glama

ukpolice

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.

Schema Changelog

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

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already establish read-only/idempotent/non-destructive behavior. The description adds valuable context beyond that: the default model is free, probing Anthropic requires the user's own API key, and 'you pay Anthropic directly for those calls' highlights a cost implication. It also outlines the per-model return structure, giving good transparency for a safe read operation.

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 sentences deliver the core purpose, default/configuration detail, return shape, and use cases with no filler. The information is front-loaded and every sentence adds value.

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?

For a read-only probing tool with rich annotations and 100% schema coverage, the description covers the essential extras: return format, default behavior, cost note, and typical scenarios. It is slightly shy of a 5 because it does not mention potential caveats (e.g., hallucinated answers, latency) or clarify whether 'signals' are textual, but the provided context is sufficient for correct use.

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 description coverage is 100%, so all four parameters are already documented in the input schema. The description reinforces the default model ('Workers AI Llama-3.3-70b (free)') but adds no new parameter-specific meaning beyond what the schema provides, earning the baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Probe' and the resource ('LLMs for what they know about a business / brand / product / topic') plus the output concept ('score visibility (0-100) per model'). It is specific and actionable, though it does not explicitly differentiate from sibling tools like scan_competitor_ai_presence, so it loses the top point for explicit sibling distinction.

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 concrete use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and states the default model and when to pass _apiKey ('pass `_apiKey` to also probe Anthropic'). It does not mention when not to use it or name alternatives, so it falls short of a 5.

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

A3.6/5.0
Disambiguation2/5

Several clusters of tools overlap heavily: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all answer questions, and the five polymarket_* tools require careful reading to distinguish. The three UK police tools are clear, but they sit among many near-duplicate data-query and memory utilities.

Naming Consistency2/5

Mixed conventions: snake_case verb_noun (get_crimes) coexists with brand-style names (ask_pipeworx), noun phrases (polymarket_arbitrage), and bare verbs (forget, recall). The polymarket_* and ask_pipeworx_* families are internally consistent, but the overall set lacks a single pattern.

Tool Count2/5

34 tools is heavy, and only three relate to the server's stated ukpolice domain, while the rest form a general-purpose data platform. Many meta-tools (suggest_questions, discover_tools, pipeworx_feedback, pipeworx_trending, memory) add bulk relative to the core purpose.

Completeness3/5

For the actual Pipeworx scope, coverage is strong: query, research, comparisons, subscriptions, memory, and feedback are all present. But for the ukpolice name, it is missing many UK police endpoints (neighborhoods, stop-and-search, etc.) and has no write/update operations, so the surface feels mismatched.

Resources