Skip to main content
Glama

Landprice

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that the tool internally calls ai_visibility_check, requires an API key for the 'anthropic' model, and returns a ranked list with score, confidence, and signal density. This provides meaningful behavioral context beyond the annotations.

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 with no redundant words. It front-loads the core purpose and follows with specific use case and output format details. Every sentence is valuable and concise.

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 the tool has no output schema, the description adequately covers the return format (ranked list with score, confidence, signal density per entity). It explains the internal mechanism (calling ai_visibility_check) and provides a concrete example question. For a tool with 4 parameters, the description is fully 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 coverage is 100% (all parameters described). The description adds semantic value by explaining that the first entity in the array is the 'subject' for narrative, context disambiguates common names, models defaults to 'workers-ai', and _apiKey is only needed for the 'anthropic' model.

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, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (other aspects).

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 recommends the tool for competitive AI-marketing audits and gives an example question ('does Claude know about us as well as our competitors?'). It implies appropriate use cases but does not mention when to avoid it or name alternative tools.

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

All Landprice 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

Several tool clusters have unclear boundaries. `ask_pipeworx_beta` is explicitly described as currently identical to `ask_pipeworx`, `discover_tools` overlaps with `suggest_questions`, and the six Polymarket tools (`bet_research`, `polymarket_edges`, `polymarket_arbitrage`, etc.) blur together for opportunity-finding. An agent would struggle to pick the right tool without reading every description carefully.

Naming Consistency4/5

Naming is overwhelmingly consistent snake_case with a verb_noun or noun pattern (`ask_pipeworx`, `list_subscriptions`, `validate_claim`, `recent_changes`). The `polymarket_*` and `ask_pipeworx_*` families follow clear conventions. Minor deviations like `bet_research`, `entity_profile`, and `landprice_points` being noun-first are still readable and predictable.

Tool Count2/5

32 tools is heavy for a server named 'Landprice' when exactly one tool (`landprice_points`) actually concerns land prices. The vast majority of tools constitute an unrelated general-purpose data research and prediction-market platform, making the count feel bloated and scattershot relative to the server's stated purpose.

Completeness3/5

For the actual broad scope revealed by the tools — structured data lookup, grounded verification, deep research, entity resolution, comparison, monitoring, and memory — the surface is reasonably complete with no obvious dead ends. However, for the 'Landprice' domain implied by the server name, coverage is nearly absent: only Japan is covered, with no other countries, address search, or property-level data.

Resources