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Overall score
49.8Poor
—
Rank
#400
Prev: #0
saas avg
41.8
360 peers
Agent runs / week
1
Across 1 providers

The Python Package Index (PyPI) is a repository of software for the Python programming language.

Locales
en
Opportunity cost · SaaS

PyPI could be leaving ~$3,413,600 in new ARR on the table every year

Stunt Double’s 2027 agent-traffic model projects 34% of trial, signup and plan selection sessions in the SaaS sector will be initiated or completed by AI agents[1][2]. PyPI currently scores 49.8 on the Stunt Double Index[3], 0.4 points below the SaaS peer average, with a 50-point gap to the ideal agent experience (100). The loss figure below applies that gap to the projected agent-driven slice of a typical annual recurring revenuebaseline for this sector — it is a directional estimate, not a measured conversion rate.

Gap to leader
50.2 pts
Below SaaS avg
0.4 pts
Modelled new ARR at risk
$3,413,600

Estimate assumes a $20M ARR baseline with agent-sourced expansion. Claim your domain to replace this placeholder with your reported new ARR.

Sources
  1. [1]Gartner (2025) — Intelligent Agents in AI Really Can Work Alone. Here’s How.
  2. [2]Adobe Analytics (2025) — Generative AI traffic to U.S. retail sites (weekly trend)
  3. [3]Stunt Double (2026) — Stunt Double Index methodology & scoring rubric

Category breakdown

Brand awarenessCan agents recognise you exist?
40
DiscoveryWill they pick you?
65
Information retrievalCan they read your site?
85
Market rankingWhere do you sit in the lineup?
15
AccuracyDo they tell the truth about you?
80
Task completionCan an agent complete a task on behalf of a user?
55
Delegated accessDo you let agents in, safely?
0
Contact & communicationCan an agent reach a human?
15

By agent provider

By agent provider
Session quality, 30-day rolling, N ≥ 10 per provider
Claude
Anthropic
0.0
ChatGPT Agent
OpenAI
100.0
Gemini
Google
0.0
Perplexity
Perplexity
Copilot
Microsoft
Browserbase Operator
Browserbase

Where agents get stuck

Public summary · full session replay available to verified owners
mediumBrand awareness
No Organization JSON-LD. Agents can’t tell who owns the site at a glance.
lowBrand awareness
Missing Open Graph tags. Link previews degrade in agent chats.
lowBrand awareness
Fewer than two social profiles linked. Agents struggle to triangulate the brand.
lowDiscovery
No llms.txt. You miss the emerging standard for giving agents a curated map of the site.
mediumInformation retrieval
No JSON-LD structured data. Agents must infer entities from markup.
mediumMarket ranking
No Product/Service schema on the homepage. Agents can’t easily compare offerings.
lowMarket ranking
No review or aggregateRating schema. Agents have no signal for ranking vs peers.
mediumMarket ranking
No price visible to non-JS clients. Agents may report "pricing not disclosed".
mediumAccuracy
No JSON-LD at all. Every factual claim must be inferred from prose.
highTask completion
No reachable entry point for delegated tasks — nothing at `/pricing`, `/signup`, `/cart`, `/book`, or similar responds without auth.
mediumTask completion
No mention of delegated auth primitives (passkey, OAuth, MCP). Agents must drive a full browser session.
highDelegated access
No MCP manifest. Agents can’t auto-discover tools or delegated capabilities.
highDelegated access
No public API docs detected. Agents have no scoped way in; they must drive a browser.
lowDelegated access
robots.txt doesn’t mention any agent user-agents. The policy for agents is implicit.
highContact & communication
No contact page linked or reachable. Agents acting on a user’s behalf have nowhere to send a message.
mediumContact & communication
No contact email visible in server-rendered HTML. Agents can’t fall back to email.