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Glama

Mapbox

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. Changed4 schema fields changed
    • addedInput schema / properties / _apiKey
      {
  "description": "Optional Anthropic API key (sk-ant-...) — only needed if \"anthropic\" is in models. Passed straight through to api.anthropic.com.",
  "type": "string"
}
    • changedInput schema / properties / context / description
      Before
      "Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\", \"Polish painter\"). Helps disambiguate common names."
      After
      "Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\"). Helps disambiguate common names."
    • changedInput schema / properties / entity / description
      Before
      "The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\", \"the company behind ChatGPT\"."
      After
      "The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\"."
    • addedInput schema / properties / models
      {
  "description": "Which models to probe. Supported: \"workers-ai\" (free default), \"anthropic\" (requires _apiKey). Omit for just workers-ai.",
  "items": {
    "type": "string"
  },
  "type": "array"
}
  2. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so safety is covered. The description adds valuable behavioral details beyond annotations: the default model (Workers AI Llama-3.3-70b) is free, probing Anthropic costs money via BYO key, and the return structure is disclosed. This gives a clear picture of what the tool does and its side effects (cost).

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 sentences, front-loaded with the core action and output, and every sentence contributes essential information: purpose, default/cost behavior, return format, and use cases. No fluff or repetition.

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?

Given there is no output schema, the description appropriately details the return structure (per-model score/confidence/signals/raw_response + combined view) and gives concrete examples of when to use it. It does not define what 'confidence' or 'signals' mean, but the overall context is sufficient for an agent to decide to call the tool. Minor gap: it doesn't clarify how scores are computed or how to interpret them.

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 coverage is 100%, so the baseline is 3. The description reinforces the _apiKey parameter's role (BYO, pay direct) and explains the context parameter's purpose (disambiguation), but these are largely duplicative of the schema descriptions. The additional cost note is a slight enhancement, but not enough to raise the score.

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 opens with a specific verb+resource: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' This clearly distinguishes it from sibling tools like ask_pipeworx (which likely answers questions) and scan_competitor_ai_presence (which may focus on competitors). It also gives concrete output details (score, confidence, signals, raw_response) and use cases.

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 states when it is useful ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and provides operational context (default model is free, Anthropic requires BYO key). However, it does not explicitly name alternatives or state when not to use it relative to siblings like scan_competitor_ai_presence, leaving some ambiguity.

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

Many tools have overlapping purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and several tools serve similar data-retrieval functions, making it difficult for an agent to distinguish which to use.

Naming Consistency4/5

Tool names mostly follow a consistent verb_noun pattern (e.g., geocode_forward, generate_llms_txt, resolve_entity). A few less descriptive names (forget, recall) exist but overall naming is predictable.

Tool Count2/5

38 tools is far too many for a server branded as 'Mapbox'. Only about 8 tools directly relate to map/geospatial functionality; the rest are unrelated (Pipeworx data, Polymarket, memory). The scope is dramatically overextended.

Completeness2/5

The Mapbox-specific tools lack coverage of major features like style management, tilesets, or data upload. The non-Mapbox tools cover their domains moderately, but the server's overall completeness for its named purpose (Mapbox) is severely lacking.

Resources