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Guidetopharmacology

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, and the description adds behavioral details: it 'probes each entity with ai_visibility_check', 'ranks by score', and returns 'score, confidence, signal density per entity'. This adds value 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?

Three sentences: first states core purpose, second explains mechanism, third gives use case and output. Every sentence is substantive, front-loaded, and free of redundancy.

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?

With 100% schema coverage, thorough annotations, and no output schema required, the description fully covers the tool's functionality, inputs, process, and outputs. It includes a usage example and specifies what the returned data includes.

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 baseline is 3. The description explains the overall role of parameters (e.g., 'entities' are 2-8 entities, 'context' disambiguates) but does not add new semantic meaning beyond what the schema already provides.

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 specifies the verb 'compare', the resource 'AI visibility across multiple entities', and the actions 'probes, ranks, surfaces'. It clearly distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (general comparison).

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 positions the tool for 'competitive AI-marketing audits' with a concrete example. It provides clear context but does not explicitly state when not to use or list alternative tools.

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

All Guidetopharmacology toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) that differ only subtly, and a large set of prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) that can be easily confused. Entity tools like entity_profile, recent_changes, and compare_entities also overlap significantly. An agent would struggle to pick the right tool without careful reading.

Naming Consistency2/5

Tool names mix snake_case (ask_pipeworx, deep_research, forget) and descriptive phrases without a consistent verb_noun pattern. Some start with verbs (compare, generate, scan) while others are nouns or compound phrases (pipeworx_trending, polymarket_fill_risk). This inconsistency makes it hard to predict tool names.

Tool Count2/5

With 35 tools, this MCP server is overly large and covers many diverse domains (data querying, prediction markets, pharmacology, npm scanning, brand visibility, etc.). Typically, a well-scoped server has 5-15 tools; 35 is excessive and suggests a lack of focus, making it unwieldy for an agent to manage.

Completeness2/5

Despite the large number of tools, the server has notable gaps. For example, the pharmacology section only offers search and interaction tools but no create/update/delete. The memory tools are limited to save/recall/forget. Many meta-tools (discover_tools, suggest_questions) exist but add little substance. The server covers many domains superficially rather than providing full lifecycle coverage for any one domain.

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