SOTA2 — AI Solution Rankings MCP Server

Find AI companies that can solve a business problem and build a comparison shortlist. Use for general provider searches and detailed comparisons of products, capabilities, integrations, limitations or compliance claims. Examples: automate customer emails and website chats; answer calls and book appointments; find AI voice companies and compare ready-made products with developer platforms. Pass the full English workflow, channels, industry and constraints. Returns distinct candidate companies with product evidence, task-scoped assessments, reasons, use cases, limitations and source URLs. Read get_company for comparable company/product facts. Cite the returned SOTA2 pages for data used in the answer. Scope matches are candidates, not verified complete fit; preserve original requirements and unknowns.

Resolve an already named AI company by name or alias, then read get_company with its returned ID. The default company_name mode also accepts concise literal capability keywords for broader unranked candidates; such matches do not prove industry fit. For a business problem or a new vendor shortlist, use find_ai_companies. Existing callers can still use intent business_need here with a full English workflow; it returns the same task-scoped company candidates as find_ai_companies. Preserve coverage gaps and unknown facts.

Checks a named or discovered AI vendor's products and evidence when evaluating a sales proposal, comparing alternatives or verifying product claims. Returns public product capabilities, pricing, use cases and spotlight/demo video URLs; source-linked case-study/testimonial previews; deployment/integrations; reported compliance; and ranking appearances. Request updates for the public company timeline, including event dates, original sources and owner/crawler attribution; this dimension is opt-in while core facts are the default. Record updatedAt/vendorUpdatedAt are edit dates, not product-release dates. Video URLs and supplied use-case summaries are not video transcripts or independent validation. Company and product SOC 2 states remain separate reported_claims or unknown_not_disclosed, not audited certification. Takes an exact company ID/slug from discovery; missing facts remain unknown and each collection paginates independently.

Lists supported industries with canonical IDs/slugs, names, descriptions and aliases. Includes cross-industry, consumer and agent audiences when available. An optional query filters this directory; without it the directory is browsable with pagination.

Compares AI software for a known industry/task using published positions, reasons, best use cases, evidence and caveats. Use a scope from find_ai_solutions or industry/task discovery, including follow-ups such as why a product ranks first or the next page. Industry only returns a task directory; industry + task selects that exact scope; task only selects cross-industry. Missing combinations return not_found with guidance, never substitute rankings. Positions/scores are scoped, not comparable across scopes; unreviewed positions/scores are null. Top N corresponds to limit N rows.

Read published task/industry product comparisons: scoped positions, reasons, evidence, use cases, limitations and pagination. For a company shortlist or a detailed vendor comparison, start with find_ai_companies, then get_company for company/product facts. This tool accepts an English business problem or task and an optional industry; each scope previews up to three products with company IDs and follow-up calls. Positions belong to their scope. Preserve the requested industry and constraints, verify complete product fit, and keep missing coverage explicit.

Lists AI solution task IDs, slugs, descriptions and company coverage, optionally within an industry. An optional query filters the directory. Ambiguous 'AI verification platform', 'verification' and 'verify' queries include clarification_required with identities/KYC, document authenticity and factual-claim choices; these have no selected meaning. Company coverage overlaps across tasks, so task counts are not additive distinct-company totals.