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Pombase

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds that it internally calls ai_visibility_check for each entity and returns a ranked list with score, confidence, signal density. This supplements the annotations well.

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: first states the main action, second explains usage and output. No redundant information, well-structured and front-loaded.

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?

No output schema, but description covers the output format (ranked list with metrics). Input constraints (2-8 entities) are mentioned. The tool's role among siblings is clear. All necessary context is provided.

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% with descriptions for all 4 parameters. The description adds nuance: first entity is treated as 'subject' for narrative, and context disambiguates names. This extra context raises the score above the baseline.

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, probes with ai_visibility_check, ranks by score, and surfaces most/least recognized. It provides a concrete example ('does Claude know about us as well as our competitors?') and distinguishes it from single-entity checks.

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 it's useful for competitive AI-marketing audits, implying it should be used when comparing multiple entities. While it doesn't list alternatives directly, the context clarifies the primary use case.

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

A3.8/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes, notably ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded, and the cluster of polymarket tools covers adjacent prediction-market analysis territory. The PomBase-specific tools are distinct, but the overall set creates real selection ambiguity.

Naming Consistency3/5

Most names follow a readable snake_case style and many use verb-first patterns, but there is notable mixing: ask_pipeworx is a brand-style exception, entity_profile and polymarket_arbitrage are noun-first, and remember/recall/forget form an inconsistent trio. Not chaotic, but not a clean predictable convention.

Tool Count2/5

33 tools is excessive for a server named Pombase, where only get_gene and get_reference actually serve that domain. Even as a broad data-research server, the count is above the 25-tool threshold and includes many auxiliary utilities that feel bolted on rather than part of a focused surface.

Completeness2/5

The PomBase-specific coverage is severely thin: only systematic-ID gene lookup and PubMed-ID reference lookup, with no gene-name search, annotations browsing, phenotype data, or sequence access. The broader Pipeworx surface is extensive, but for the apparent Pombase purpose, agents will frequently hit dead ends.

Resources