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Usgs Water

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as safe, read-only behavior. The description adds that it internally calls ai_visibility_check and returns a ranked list with score, confidence, signal density. This provides helpful behavioral context beyond the annotations without contradiction.

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: first states action and result, second explains process and use case, third describes output. No redundancy, front-loaded, every sentence earns its place. Very concise for a tool with 4 parameters.

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 4 parameters (1 required) and no output schema, the description adequately explains the output structure. It also mentions conditions for using _apiKey. Could be slightly more detailed on output format or error scenarios, but it's sufficiently complete for an analysis tool of this complexity.

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%, so the bar is lower. The description adds meaning beyond schema by explaining that the first entity in the 'entities' array is treated as the 'subject' for narrative and that 'models' defaults to workers-ai. These nuances help the agent use parameters correctly.

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 it compares AI visibility across multiple entities side-by-side, ranking them. It distinguishes from the sibling tool 'ai_visibility_check' which is for a single entity. The verb 'compare' and resource 'AI visibility across entities' are specific.

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 gives a concrete use case ('competitive AI-marketing audits') and explains it probes entities using ai_visibility_check. However, it doesn't explicitly state when not to use it or mention alternatives, but the context is clear enough for the agent to infer when to choose this over sibling tools.

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

A3.5/5.0
Disambiguation2/5

Several clusters of tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route/discover questions across the same 5,743 tools, differing mainly in mode or betaness. The polymarket_* family (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) similarly overlaps in prediction-market edge detection. An agent would frequently struggle to pick the right tool from these near-duplicate groups despite verbose descriptions.

Naming Consistency3/5

Snake_case is used throughout, but patterns are mixed: some tools are verb-first (ask_pipeworx, find_sites, recall, forget, subscribe), some are noun phrases (current_conditions, entity_profile, bet_research), and some use a domain prefix (pipeworx_*, polymarket_*). The version-suffixed ask_pipeworx_beta is also a minor deviation from the otherwise clear descriptive style.

Tool Count2/5

34 tools is excessive for a server named 'Usgs Water' since only 3 tools (current_conditions, daily_values, find_sites) actually relate to USGS water data. Even as a general Pipeworx platform server, the count is heavy, with many tools dedicated to niche prediction-market trading and meta-routing that inflate the surface.

Completeness1/5

Against the stated USGS Water purpose, the surface is severely incomplete: it lacks water-quality samples, groundwater data, site metadata details, historical statistics, rating curves, parameter code lookup, and flood/alert data. The remaining 31 tools cover an entirely different domain (SEC filings, drugs, prediction markets, npm scans, memory), so agents using this server for water data will hit dead ends almost immediately.

Resources