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June 9-10, 2026
Bengaluru, India
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IMPORTANT NOTE: Timing of sessions and room locations are subject to change.


Wednesday June 10, 2026 11:25am - 11:50am IST
When humans hire someone, they ask questions first. Check reviews, compare, negotiate. AI agents can't do any of this.

An MCP flight booking server says: "I book flights." Another agent can't ask: How many routes? Success rate? Can you get business class upgrades?

I tore down top MCP servers across mcp.so, Smithery, Glama, PulseMCP. The #1 server has 52K stars but exposes 47 capabilities with zero verifiable metrics. Tool schema: 1,020 tokens of bloat.

The problem: MCP tool schemas describe WHAT but not HOW WELL. No capability layer for agents to evaluate each other before committing.

What's needed — structured capability endpoints:

"I book flights"
→ 147 routes, 96.2% completion, 23% avg savings
→ Business class upgrades: 340 secured, 41% success
→ Savings by route queryable, methodology documented
→ Full transaction log for independent verification

Exposed as MCP resource endpoints —
capability/summary returns structured metrics, capability/evidence/{tool} returns methodology, capability/raw/{tool} returns verifiable logs.

I'll show real endpoint teardowns, what's missing from tool schemas, and a draft capability-metadata spec builders can implement.
Speakers
avatar for Manav Agarwal

Manav Agarwal

Founder & Independent Researcher, AgentProof
Creator of AgentProof, an independent benchmarking and trust analysis project for AI agents. Mapped 10 agent directories containing 1.24M+ listings, deep-dived 3 agent categories, and health-checked 65+ MCP servers. Previously analyzed 14 agentic payment protocols (x402, Stripe ACP... Read More →
Wednesday June 10, 2026 11:25am - 11:50am IST
Scarlet 1
  Security Identity + Trust

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