Skip to main content
Glama

Opentargets

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context such as the default free model, the cost implication ('you pay Anthropic directly'), and the return structure. This exceeds the bare annotation hints without contradicting them, so 4.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, front-loaded with the core purpose and then progressing to configuration, output, and use cases. It is efficient and free of fluff, though slightly longer than strictly necessary, justifying a 4.

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 the tool has four parameters, no output schema, and strong annotations, the description covers purpose, configuration, costs, return shape, and use cases. It lacks a detailed scoring explanation but that is not essential for invoking the tool, so 4.

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 coverage is 100% with descriptions for all four parameters, so the baseline is 3. The description does mention the default model and API key requirement, but this largely repeats schema information (e.g., 'workers-ai' free default, '_apiKey' for Anthropic) and does not add new parameter-level meaning, so 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model,' which is a specific verb and resource. It clearly conveys the tool's function but does not explicitly contrast it with sibling tools, so it stops short of a 5.

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 provides explicit use cases: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' This gives clear when-to-use context, but no mention of when not to use it or alternatives is included, so 4.

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

All Opentargets 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

A3.9/5.0
Disambiguation2/5

Several clusters of tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer data questions; entity_profile, compare_entities, and recent_changes all pull company data; polymarket_edges, polymarket_arbitrage, and bet_research all analyze prediction markets. Though descriptions are detailed, the boundaries are subtle and the beta variant is nearly identical to the stable one.

Naming Consistency3/5

Most names are snake_case, but there's no consistent verb_noun pattern: some are bare nouns (disease, target, drug, search), some are verb phrases (resolve_entity, validate_claim, generate_llms_txt), and some are domain-prefixed (ask_pipeworx_*, polymarket_*, target_*). The mixed conventions make it hard to predict tool names.

Tool Count2/5

38 tools is far beyond the typical well-scoped server, and the set mixes Open Targets lookup, a general data platform (Pipeworx), prediction markets, npm package checks, and AI-marketing utilities under the name 'Opentargets'. Many tools are unrelated to the server's apparent core purpose.

Completeness4/5

The Open Targets drug-discovery workflow is well covered: search for IDs, get disease/drug/target profiles, and get associations/known drugs. However, the broader platform lacks some lifecycle operations (no create/update/delete since it's read-only), and the unrelated utilities (generate_llms_txt, scan_dependency) appear tacked on rather than filling domain gaps.

Resources