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Prayer Times

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.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare safe read-only, idempotent behavior. The description adds valuable context: it probes entities with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, signal density. 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 sentences, front-loaded with purpose and key capability. Every sentence adds value without 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?

The description fully explains the tool's behavior, parameters, and return value (ranked list with specific fields). No output schema exists, but the description compensates by detailing what is returned. Given the annotations, it is complete.

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 provides 100% description coverage for parameters. The description adds extra meaning: clarifies that the first entity is treated as the 'subject' for narrative, and that _apiKey is passed to api.anthropic.com per probe. This enhances understanding 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, ranks by score, and identifies most/least recognized. This distinguishes it from sibling ai_visibility_check which handles single entities.

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 indicates use for competitive AI-marketing audits and provides an example query. It implies that for single entity checks, ai_visibility_check should be used, but does not explicitly state when not to use this tool or list alternatives.

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
Disambiguation4/5

Most tools have clearly distinct purposes, though there is some overlap between `ai_visibility_check` and `scan_competitor_ai_presence`, and between `bet_research` and `polymarket_edges`. Overall, an agent can distinguish them.

Naming Consistency3/5

Tool names follow snake_case but vary in prefix usage (e.g., `pipeworx_*`, `polymarket_*`, no prefix) and verb presence (e.g., `discover_tools` vs. `generate_llms_txt`). The pattern is readable but inconsistent.

Tool Count2/5

With 24 tools, the server is overloaded for the 'Prayer Times' name. Many tools are unrelated to prayer, suggesting poor scoping relative to the server's implied purpose.

Completeness2/5

For prayer times, the server includes core tools but lacks features like multi-day forecasts or advanced settings. However, the inclusion of many unrelated tools makes the set incoherent and incomplete for any single domain.

Resources