Skip to main content
Glama

Data Bloomington

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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false, but the description adds behavioral details: each entity is probed with ai_visibility_check, results include score/confidence/signal density, and the first entity is treated as the 'subject' for narrative. No contradiction with 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, front-loaded with the primary action and use case. Every sentence adds value without redundancy or extraneous detail.

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?

Despite no output schema, the description explains the return format (ranked list with score, confidence, signal density) and covers the tool's complexity (multiple entities, models, API key option, shared context). It is sufficiently complete for an agent to understand the tool's behavior and results.

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 the baseline is 3. The description adds contextual meaning for the 'entities' parameter (first entry is subject) and explains the 'context' parameter, but does not significantly enrich understanding beyond the 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's purpose: 'Compare AI visibility across multiple entities side-by-side.' It uses specific verbs ('compare', 'probes', 'ranks') and distinguishes itself from the sibling tool 'ai_visibility_check' by explicitly noting it probes multiple entities and produces a ranked list.

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 explains when to use the tool: 'Useful for competitive AI-marketing audits' with an example question. It implicitly contrasts with 'ai_visibility_check' (single entity) but does not explicitly mention or exclude alternatives like 'compare_entities' or specify when not to use it.

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

All Data Bloomington 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

A3.7/5.0
Disambiguation2/5

Multiple tool clusters overlap heavily — ask_pipeworx_beta is functionally identical to ask_pipeworx right now, and the six Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, etc.) have blurry boundaries. The entity-research family (entity_profar, compare_entities, recent_changes) is also easy to misselect despite detailed descriptions.

Naming Consistency3/5

All names are snake_case, but conventions are mixed: verb_noun (resolve_entaty, validate_claim), bare verbs (remember, recall, query), adjective_noun (recent_alerts, recent_changes), and brand-prefixed nouns (polymarket_edges, pipeworx_trending). Family prefixes and verbs help readability, but the overall pattern is not uniform.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold and spans loosely related domains — Bloomington open data, Pipeworx research, prediction markets, AI marketing, memory, npm scanning, and llms.txt generation. The scope feels heavy and unfocused relative to the 'Data Bloomington' server name.

Completeness4/5

The core research lifecycle is well covered: routing (ask_pipeworx), grounded verification, deep research, entity profiles/comparisons/changes, claim validation, entity resolution, subscriptions, and memory all exist. Minor gaps remain — no standalone tool for fetching a pipeworx:// citation URI and no API-key management — but agents can work around them.

Resources