What can EMA Agent Identity Verifier v3.1.0 do?

wallet verification for ai agents, domain verification for ai agents, manifest verification for ai agents, real-time bad actor flagging, comprehensive signal breakdown reporting

EMA Agent Identity Verifier v3.1.0

MCP ServerFree

Verifies AI agent wallets, domains and manifests before any transaction. Returns TRUSTED/UNVERIFIED/SUSPICIOUS/BLOCK with full signal breakdown. Connected to EMA shared brain - bad actors flagged here are blocked network-wide instantly.

Open Source

/ 100

5 capabilities

Capabilities5 decomposed

wallet verification for ai agents

Medium confidence

This capability verifies the authenticity of AI agent wallets by cross-referencing them against a decentralized ledger and a shared database of known bad actors. It employs a consensus mechanism to ensure that wallet statuses are updated in real-time, leveraging the EMA shared brain architecture to instantly flag and block malicious wallets network-wide. This ensures that only trusted agents can initiate transactions, enhancing security across the platform.

Solves for

How can I verify the authenticity of an AI agent's wallet before processing a transaction?What steps do I need to take to ensure that the wallet I'm interacting with is not compromised?Can I get real-time updates on the status of an AI agent's wallet?

Best for

developers building secure AI applications requiring wallet verification

Requires

Access to the EMA shared brain API with valid credentials

Limitations

Dependent on the availability of the EMA shared brain for real-time updates; if the network is down, verification may be delayed.

What makes it unique

Utilizes a decentralized ledger and real-time consensus mechanism for wallet verification, ensuring instant updates and blocking of bad actors.

vs alternatives

More secure than traditional wallet verification methods by leveraging a decentralized network for instant updates.

domain verification for ai agents

Medium confidence

This capability checks the legitimacy of domains associated with AI agents by querying a comprehensive database of registered domains and their reputations. It employs a multi-layered validation process that includes DNS lookups and historical data analysis to assess the trustworthiness of a domain before any transactions occur. This proactive approach helps prevent interactions with potentially harmful agents.

Solves for

How can I verify if an AI agent's domain is legitimate before engaging?What tools do I have to check the reputation of a domain linked to an AI agent?Can I receive alerts if a domain associated with an agent becomes suspicious?

Best for

developers ensuring the safety of transactions involving AI agents

Requires

Access to the EMA domain verification API

Limitations

Domain verification may be delayed if DNS queries are slow or if the domain is newly registered.

What makes it unique

Incorporates historical data analysis alongside DNS lookups for a comprehensive assessment of domain legitimacy.

vs alternatives

More thorough than standard domain checks by combining multiple validation techniques for enhanced security.

manifest verification for ai agents

Medium confidence

This capability validates the manifests of AI agents by parsing and analyzing their structure and contents against predefined schemas and security standards. It ensures that all required fields are present and correctly formatted, while also checking for any suspicious elements that could indicate malicious intent. This verification process is crucial for maintaining the integrity of transactions involving AI agents.

Solves for

How can I ensure that an AI agent's manifest is correctly structured and safe to use?What checks are performed on the manifest of an AI agent before I proceed with a transaction?Can I validate the manifest of an agent programmatically before executing any actions?

Best for

developers implementing AI agents that require strict manifest validation

Requires

Access to the EMA manifest validation API

Limitations

Manifest validation is limited to specific schema formats; unsupported schemas will not be validated.

What makes it unique

Employs schema validation alongside content analysis to ensure comprehensive manifest verification, reducing the risk of malicious agents.

vs alternatives

More robust than conventional manifest checks by integrating schema compliance with security assessments.

real-time bad actor flagging

Medium confidence

This capability provides real-time flagging of bad actors by continuously monitoring transactions and interactions across the network. It utilizes a machine learning model trained on historical data to identify patterns associated with malicious behavior, allowing for immediate action to block suspicious agents. This proactive monitoring ensures a safer environment for all transactions.

Solves for

How can I get real-time updates on suspicious activities involving AI agents?What mechanisms are in place to block bad actors instantly across the network?Can I access historical data on flagged agents for analysis?

Best for

security teams monitoring AI agent interactions

Requires

Access to the EMA monitoring API

Limitations

Real-time monitoring may introduce latency during peak transaction times due to increased data processing.

What makes it unique

Incorporates machine learning for pattern recognition in real-time, allowing for proactive blocking of bad actors based on historical behavior.

vs alternatives

More efficient than static monitoring systems by adapting to new threats through continuous learning.

comprehensive signal breakdown reporting

Medium confidence

This capability generates detailed reports on the verification status of AI agents, including a comprehensive signal breakdown that explains the rationale behind each status. It aggregates data from multiple sources, providing insights into the factors contributing to an agent's trustworthiness. This transparency helps users make informed decisions regarding their interactions with AI agents.

Solves for

How can I access detailed reports on the verification status of an AI agent?What factors are considered in determining the trustworthiness of an agent?Can I obtain a summary report of all flagged agents for review?

Best for

auditors and compliance teams reviewing AI agent interactions

Requires

Access to the EMA reporting API

Limitations

Reporting features may be limited to a specific number of requests per hour.

What makes it unique

Offers a unique signal breakdown that combines multiple verification metrics into a single comprehensive report, enhancing transparency.

vs alternatives

More informative than basic verification reports by providing in-depth analysis of trust factors.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with EMA Agent Identity Verifier v3.1.0, ranked by overlap. Discovered automatically through the match graph.

Repository24

AI Manifest

Optional reference for /.well-known/ai.json + OpenAPI/JSON Schema discovery (with MCP/agents.json...

jwks-based cryptographic signature verification for manifest authenticityjson schema validation cli for manifest compliance checkingwellknownai public registry with optional mirroring support

3 shared capabilities

Agent28

Spec27 – Spec-driven validation for AI agents

Hi HN! We’re a team of ML validation specialists and we’ve been building /Spec27, a tool for testing whether AI agents still do their job safely and reliably as models, prompts, tools, and surrounding systems change.We started working on this because a lot of current LLM evaluation work seems a

spec-driven agent behavior validationmulti-agent specification consistency checking

2 shared capabilities

Web App39

WellKnownAI

Registry/spec examples and public snapshots (no PII; mirroring...

ai manifest schema validation with structured error reportingai manifest registry indexing and discovery

2 shared capabilities

Product26

Agntor

What agntor MCP provides: Agent discovery and certification Trust and payment rail for AI agents Identity verification Escrow and settlement Reputation management Security audit tools including input validation, output redaction, and tool authorization

identity verification for agents

1 shared capability

Product20

Openwork

AI agents hire each other, complete work, verify outcomes, and earn tokens.

work execution and task completion verification

1 shared capability

Template23

create-shieldedvault

Scaffold an AI agent with split-key custody, attestation, payments, and MCP tool discovery. ShieldedVault in 2 minutes.

implement attestation for transactions

1 shared capability

Best For

✓developers building secure AI applications requiring wallet verification
✓developers ensuring the safety of transactions involving AI agents
✓developers implementing AI agents that require strict manifest validation
✓security teams monitoring AI agent interactions
✓auditors and compliance teams reviewing AI agent interactions

Known Limitations

⚠Dependent on the availability of the EMA shared brain for real-time updates; if the network is down, verification may be delayed.
⚠Domain verification may be delayed if DNS queries are slow or if the domain is newly registered.
⚠Manifest validation is limited to specific schema formats; unsupported schemas will not be validated.
⚠Real-time monitoring may introduce latency during peak transaction times due to increased data processing.
⚠Reporting features may be limited to a specific number of requests per hour.

Requirements

Access to the EMA shared brain API with valid credentialsAccess to the EMA domain verification APIAccess to the EMA manifest validation APIAccess to the EMA monitoring APIAccess to the EMA reporting API

Input / Output

Accepts: wallet address, domain name, manifest file (JSON, XML), transaction data, agent identifiers

Produces: status (TRUSTED/UNVERIFIED/SUSPICIOUS/BLOCK) with signal breakdown, alert notifications, flagged agent details, detailed report (structured data)

UnfragileRank

Adoption5%(25% weight)

Quality25%(25% weight)

Ecosystem59%(15% weight)

Match Graph25%(30% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

5 capabilities

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