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Glama

Oeis

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

A4.8/5.0
Behavior5/5

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

Annotations declare readOnly, idempotent, openWorld, non-destructive. Description adds that it internally calls ai_visibility_check and returns 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?

Two sentences plus parenthetical example. Concise and front-loaded with purpose and method. Every sentence provides value.

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?

Covers purpose, method (probes with ai_visibility_check), output (ranked list with score/confidence/signal density). No output schema but description sufficient for a comparison tool with good annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%. Description adds context: first entity is subject, models default to workers-ai, context disambiguates, _apiKey optional. Adds meaningful information beyond schema descriptions.

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?

Clearly states it compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks, and surfaces recognition. Concrete example ('does Claude know about us as well as our competitors?') distinguishes it from single-entity sibling ai_visibility_check.

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?

Explicitly says 'Useful for competitive AI-marketing audits' and implies comparison use case. No explicit when-not or alternatives mentioned, but sibling list includes ai_visibility_check for single entity, allowing inference.

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

All Oeis toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap, especially among ask_pipeworx variants (ask_pipeworx, ask_pipeworx_grounded, ask_pipeworx_beta) and prediction market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research). While descriptions help distinguish them, the boundaries are somewhat fuzzy.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use verb_noun (search_sequence, compare_entities), others use noun_verb (entity_profile, recent_changes), and some have prefixes (pipeworx_feedback, pipeworx_trending) while others do not (remember, forget). There is no uniform pattern, which reduces predictability.

Tool Count2/5

The server name 'Oeis' suggests a focus on integer sequences, but only 2 of 33 tools are OEIS-related. The remaining tools cover a vast array of unrelated domains (data retrieval, prediction markets, memory, subscriptions). This mismatch between name and scope makes the tool count feel excessive and poorly scoped.

Completeness4/5

Within the broad domains covered, the tool surface is quite complete. For memory, there are remember/recall/forget; for subscriptions, subscribe/unsubscribe/list_subscriptions/recent_alerts; for data retrieval, multiple entry points. Minor gaps exist, such as lack of OEIS sequence contribution tools, but overall the set is well-rounded.

Resources