Compile
Audit your public URL and retain canonical product facts, machine-readable surfaces, findings, and evidence URLs.
Public surfaces / Evidence ledger Agent distribution infrastructure
Be the app
agents find next.
Start from one verified product identity. Make your software legible, discoverable, and testable wherever AI agents search for capabilities.
Start with a URL. No rebuild required.
Live distribution proof
Technical proof liveDeploy to Agents
FIRST CUSTOMERUS
01Published
02Audited
03Search submitted
04Registry verified
What is proven today?Its public technical surfaces passed its own audit.Inspect the 100/100 technical receipt ↗Verify its official MCP Registry identity ↗Open its public Smithery listing ↗First unbranded recommendation observed July 31, 2026: one provider, primarily supported by this owned source, not yet reproduced across providers.
One canonical product profileMCPA2AARDOpenAIClaudeGemini+ the open web
The control planeFrom software that exists
From software that exists
to software agents choose.
Distribution is no longer a list of submissions. It is a continuously tested system connecting your product to the problems it can solve.
Publish
Turn observed source gaps into owner-reviewed directory submissions, article briefs, and agent handoffs.
Observed sources / Reviewable actionsVerify
Test whether providers retrieve, mention, and recommend your app for non-branded user jobs; keep invocation separate.
Retrieval / Mention / RecommendationMeasure
Retain the observable prompt, query, source, verification step, and recommendation with explicit evidence limits.
Prompt -> source -> observed outcomeAnswers for real distribution jobs
Build the surfaces agents already research.
Each guide answers one distinct non-branded intent with verified implementation facts, owner-assisted actions, and an explicit account of what the evidence does not prove.
How to make an app discoverable by AI agents
Agent discoverability is not a single file or directory submission. It is the observable path from a user problem to a public source, a correctly resolved product identity, and a useful answer.
Read the evidence-bounded guide ->How to earn app recommendations from AI agents
Recommendations are downstream of retrieval and trust. The defensible path is to answer a real user intent with verifiable facts, observe which sources support the answer, and test whether the result repeats.
Read the evidence-bounded guide ->How to make an app invocable by AI agents
Being found answers what can help. Being invocable answers whether an agent can call a real capability safely and correctly. They are separate systems with separate tests.
Read the evidence-bounded guide ->How to measure AI-agent recommendations
A recommendation metric is useful only when its prompt, provider, sources, answer, and limitations can be inspected later.
Read the evidence-bounded guide ->What an AI-agent distribution platform should do
Agent distribution is the operational loop between verified product facts, the public sources agents consult, and repeatable evidence of what those agents returned.
Read the evidence-bounded guide ->How to publish an app to AI-agent directories
Directory work should begin with an exact branded presence check and end with a generic retrieval test. Submission history alone proves neither publication nor discoverability.
Read the evidence-bounded guide ->How to improve MCP server discoverability
An MCP server becomes discoverable through accurate public metadata, a reachable implementation, downstream indexes, and user-intent language?not through the protocol name alone.
Read the evidence-bounded guide ->MCP Registry vs. an agent distribution platform
The Official MCP Registry and an agent distribution platform solve different layers of the same problem. One is a metadata authority for MCP servers; the other coordinates evidence and experiments across channels.
Read the evidence-bounded guide ->Choose by job, not by slogan
Different layers of the agent distribution stack.
These products overlap, but they are not interchangeable. This comparison links to each product's primary documentation and describes where each one is strongest.
Deploy to Agents
- Best fit
- Apps that need cross-channel publication, verification, and recurring recommendation measurement — with or without MCP.
- How it differs
- Closes the loop from machine-readable surfaces to observed agent recommendations and evidence receipts.
Alpic
- Best fit
- Teams that need to deploy, operate, monitor, and distribute hosted MCP servers and ChatGPT Apps.
- How it differs
- A deployment and operations platform; complementary when Deploy to Agents measures broader discovery.
Prowl
- Best fit
- Agents and vendors that want a searchable, benchmarked index of SaaS services and APIs.
- How it differs
- An external discovery and evaluation destination rather than a cross-channel publishing control plane.
FlarePort
- Best fit
- Service marketplaces that need agents to discover, quote, book, and trace transactions.
- How it differs
- Specialized in marketplace execution, protocol gateways, and production transaction telemetry.
Last checked July 22, 2026. No placement is paid. Product scope changes; verify the linked primary source before deciding.
Simple portfolio pricingStart with proof.
Start with proof.
Scale with discovery.
Every plan keeps technical readiness separate from real retrieval and recommendation evidence. No plan promises that an AI provider will rank or recommend an application.
Plan
Free
$0forever
For developers validating one public app.
- 1 application
- Technical distribution audit
- Public evidence receipt
- Branded discovery baseline
Most useful for launch
RecommendedPro
$30per month
For makers actively improving agent discovery.
- Up to 3 applications
- Daily OpenAI and Grok experiments
- Citation and source tracking
- Evidence-backed action prompts
Plan
Business
$300per month
For teams managing a software portfolio.
- Up to 25 applications
- Organization workspace
- Cross-product discovery history
- Priority onboarding and evidence review
Plan
Enterprise
Customannual agreement
For larger portfolios and controlled environments.
- Custom application volume
- Provider and policy configuration
- Security and deployment review
- Dedicated implementation support
Early-access pricing in USD. Discovery results depend on external providers, public sources, query wording, and time; publication never guarantees retrieval or recommendation.
The non-negotiable principle
We are customer zero.
Deploy to Agents must be discoverable by any LLM or agent helping a developer distribute an app. Every recommendation should carry a verifiable explanation: which external source caused discovery, and how our own distribution system published and verified it.
Start the loop