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Cbioportal

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate safe, idempotent, read-only. Description adds default model, free vs. paid probe, and return structure (per-model scores, combined view). 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?

Four sentences, no fluff. First sentence delivers core purpose and scoring, second details model and API key, third describes output, fourth lists use cases. Highly efficient and front-loaded.

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 4 params, no output schema, and rich annotations, the description covers all necessary aspects: purpose, parameters, use cases, and output shape. Missing details like scoring formula or error handling are acceptable for this complexity.

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?

100% schema coverage provides baseline 3. Description enriches all parameters: 'entity' ('brand/business name...'), 'models' ('supported: workers-ai, anthropic'), '_apiKey' ('BYO key... passed straight through'), 'context' ('helps disambiguate'). Adds value beyond 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 uses a specific verb ('probe') and resource ('LLMs... score visibility'), clearly distinguishing it from sibling tools like 'ask_pipeworx' or 'deep_research'. It tells exactly what it does and for what purpose.

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 states use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and default vs. BYO key behavior. Does not explicitly say when not to use, but context is clear enough; no alternative tools mentioned.

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

A3.6/5.0
Disambiguation3/5

Most tools have clearly distinct purposes, but ask_pipeworx_beta is an intentional near-duplicate of ask_pipeworx, and several polymarket/entity tools overlap in scope. The descriptions do enough to disambiguate most pairs, but the duplicate beta routing tool introduces real ambiguity.

Naming Consistency3/5

All names use snake_case, but the pattern varies: verb_noun (get_gene, search_studies), noun_noun (polymarket_edges, pipeworx_trending), and product-prefixed verbs (ask_pipeworx, bet_research). There is no single consistent convention, though the names remain readable.

Tool Count2/5

35 tools is a heavy surface, and the vast majority (31) are unrelated to cBioPortal; only four tools actually belong to the named domain. This makes the count inappropriate for a cancer-genomics MCP server, as the set is bloated with out-of-scope utilities.

Completeness1/5

For a cBioPortal server, only metadata-level tools exist (gene lookup, study details, cancer types, study search); core cBioPortal data access — mutations, copy-number alterations, clinical data, molecular profiles, sample-level queries — is entirely missing. The tool surface severely under-covers the named domain.

Resources