Elva vs moar: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Elva and moar — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Elva

Theneo

Freemium

Reads your repositories to discover every API, scores and governs them, then exposes them to developers and AI agents via hosted MCP servers.

Spec-Free API Discovery: Elva scans repository code directly to find endpoints and generates OpenAPI 3.1 as output, so no existing spec is needed to start.
Endpoint Scoring: Every collection is graded on design, developer experience, AI readiness, security and performance, with the weakest collection surfaced first.
AI Fix Pass: A one-click agent writes missing descriptions from code, types response schemas and documents auth, then rescores the collection.
API Contracts: Per-audience contracts pin the exact endpoints and fields a partner, internal team, public developer or MCP client receives, excluding PII and internal fields.
Breaking Change Enforcement: Each commit is diffed against published contracts, showing the schema diff, affected consumers and tools, and blocking publish by policy.
Hosted MCP Servers: Contracts generate MCP servers hosted behind Elva's gateway with OAuth2, scoped keys, per-tool authorization and exportable call logs.
MCP Playground and Agent Feedback: Test the server with a live model, then read the complaints agents file about confusing or failing tools, scored back into the catalog.
Multi-Target Publishing: One approved contract ships as OpenAPI spec, Theneo docs, MCP server, Postman collection and a typed TypeScript SDK in sync.

API Inventory Audit: Discover undocumented or forgotten endpoints across a large codebase and get a ranked list of what to fix first.
Agent Enablement: Expose an internal service to Claude, Cursor or ChatGPT as a governed MCP server instead of hand-writing tool wrappers.
Partner Integration Safety: Publish a restricted contract to an external partner and have Elva block commits that would break their integration.
PII Scoping: Keep customer emails and internal ops annotations out of a public or agent-facing surface while the same endpoints serve them internally.
Zombie Endpoint Retirement: Prove no active consumer references an endpoint before deleting it, using contract and call-log evidence.
Enterprise Security Review: Satisfy SOC 2, ISO 27001 and GDPR questions and wire agent access into an existing SSO and SCIM identity provider.
Documentation Drift Control: Keep docs, SDKs and Postman collections regenerated from code on every merge instead of maintained by hand.

moar (getmoar.ai)

Free

Privacy-first Chrome extension that converts documents to AI-ready Markdown, reducing size up to 95% for more conversations across major chat models.

Document Compression: Converts arbitrary documents into AI-ready Markdown, reducing size by up to 95% to fit more content into model context windows.
Meaning Preservation: Uses transformation techniques that maintain semantic content and intent so compressed documents retain zero loss of meaning for downstream tasks.
Multi-Model Compatibility: Output is formatted to work seamlessly with ChatGPT, Claude, Gemini and other conversational LLMs, enabling consistent results across models.
Browser Integration: Privacy-first Chrome extension that performs conversion in-browser with zero setup, letting users optimize content directly where they work.
Conversation Density Increase: By reducing document size, enables up to 5× more conversational turns or more documents per single model session, avoiding context truncation.
Zero Setup Workflow: Immediate usability without configuration—install the extension and start converting documents into compact, chat-ready Markdown.
Converts documents into AI-ready Markdown
Reduces document size up to 95% while aiming to preserve meaning
Increases number of chat conversations per document (advertised 5×)
Zero-setup usage model (instant conversion)
Free Chrome extension for in-browser conversion
Designed to work with ChatGPT, Claude, Gemini and other chat models

Feeding Long Documents to Chatbots: Convert manuals, reports, or whitepapers into compressed Markdown so ChatGPT/Gemini can consume the full content in a single session.
Research and Q&A: Prepare academic papers and technical documents for fast question answering and summarization without losing critical details.
Knowledge Base Compression for Support: Shrink internal knowledge articles to allow conversational agents to reference complete answers within model context limits.
Sales and Product Enablement: Condense product sheets and pricing documents into compact formats that sales assistants can query during live customer interactions.
Personal Note Consolidation: Compress and organize large personal notes or meeting transcripts into chat-ready snippets for follow-up queries and summaries.
Cross-Model Workflows: Standardize document input for workflows that switch between ChatGPT, Claude, Gemini, or other LLMs to ensure consistent comprehension.
Feeding long documents into chat models for Q&A without hitting context limits
Reducing token/context usage when interacting with ChatGPT, Claude, Gemini
Preparing knowledge-base or documentation for conversational assistants
Research and note preparation to maximize chatbot interaction per source document
Faster prototyping of chat integrations by compressing source documents