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attestify-mcp

MCP server exposing Attestify OS agents — and Attestify Trust's no-wallet agent identity + signed evidence — as callable tools for Claude Desktop, Cursor, Windsurf, and any MCP-compatible host.

Attestify OS is infrastructure for AI agents operating in financial, compliance, and regulated workflows. Every governed run delivers: routing → governance enforcement → budget check → SLA check → execution → output verification → immutable receipt → on-chain settlement evidence.

Live base URL: https://attestifyos.com

This package is a real Model Context Protocol stdio server — your MCP host spawns it as a subprocess and talks JSON-RPC to it directly. It is not a plain HTTP client library.

Quick start

Add to your MCP host's config (e.g. Claude Desktop's claude_desktop_config.json):

{
  "mcpServers": {
    "attestify": {
      "command": "npx",
      "args": ["-y", "attestify-mcp"],
      "env": {
        "ATTESTIFY_BASE_URL": "https://attestifyos.com",
        "ATTESTIFY_API_KEY":  "<your-tenant-api-key>",
        "TRUST_AGENT_ID":     "<optional — see Attestify Trust below>",
        "TRUST_PRIVATE_KEY":  "<optional — see Attestify Trust below>"
      }
    }
  }
}

ATTESTIFY_API_KEY is a tenant API key issued via POST /api/keys — it's sent as the X-API-Key header on every run. Without one, runs are unauthenticated and subject to the plan/x402 limits that apply to anonymous callers.

TRUST_AGENT_ID / TRUST_PRIVATE_KEY are optional and unlock two more tools — see Attestify Trust below.

Related MCP server: agent-audit-trail

Tools

attestify_research

Runs a deep research query through the Attestify Research Agent (researcher-v2) via POST /api/run. Returns a structured briefing with executive summary, key findings, and caveats. Each call is metered at $0.023 USDC and logged to the Attestify evidence ledger.

Arguments:

Field

Required

Description

query

Yes

The research question, topic, or subject to investigate.

url

No

Optional source URL to include as context for the research run.

session_id

No

Optional session ID for conversation continuity.

Returns: a JSON blob with summary, loop_id, run_id, input_hash, output_hash, price_usdc, agent_id, receipt_url, and created_at.

attestify_trust_submit_evidence / attestify_trust_verify

Available whenever TRUST_AGENT_ID and TRUST_PRIVATE_KEY are both set. A separate concern from attestify_research above — no x402, no lanes, no spend, no wallet at any point.

attestify_trust_submit_evidence signs (Ed25519, via Node's built-in node:crypto — no extra dependency) and submits evidence that the agent completed real work, returning a signed, timestamped, publicly verifiable receipt.

Field

Required

Description

summary

Yes

Plain-language description of the work done. Gets signed and permanently recorded.

schema

No

Evidence schema version. Default work-completion/v1.

action_basis

No

explicit (default) or discretionary.

attestify_trust_verify independently re-verifies any receipt by ID — public, no API key needed, works for receipts from any agent.

Field

Required

Description

receipt_id

Yes

The receipt ID to verify.

Getting TRUST_AGENT_ID / TRUST_PRIVATE_KEY: run this once, outside of any MCP session — never generate a fresh identity per session, it would both break the agent's own verified-active streak and add noise to Attestify's public census instead of a real, citable number:

npx attestify trust-init --name "My MCP Agent"

This registers a free Trust agent, generates its Ed25519 signing key locally, and prints the two values to set in your MCP host's config. The private key never leaves your machine except as a signature.

Enterprise self-serve

Issue a tenant key, set a budget, attach policies — every subsequent run through this server is auto-governed:

1. POST /api/keys      → issue tenant API key
2. POST /api/budgets   → set USDC spend ceiling
3. POST /api/policies  → attach governance rules
4. (this MCP server)   → every run auto-enforced

Links

Available Tools

1 tool
attestify_researchA

Run a deep research query through the Attestify Research Agent (researcher-v2). Returns a structured briefing with executive summary, key findings, and caveats. Each call is metered at $0.023 USDC and logged to the Attestify evidence ledger.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlNoOptional source URL to include as context for the research run.
queryYesThe research question, topic, or subject to investigate.
session_idNoOptional session ID for conversation continuity.

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description carries full burden and adds significant behavioral context: metering cost ($0.023 USDC) and logging to an evidence ledger. It also discloses output structure. This is strong, though it omits potential side effects beyond cost/logging.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the action and outcome, and each clause adds distinct value: what it does, what it returns, and the cost/logging side effects. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 3 parameters and no output schema, the description covers the return format (structured briefing with executive summary, key findings, caveats) and a notable operational detail (cost/logging). It lacks explicit output schema, but the description sufficiently compensates, making it fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% per context signals, so the baseline is 3. The description adds no additional meaning about the query, url, or session_id parameters, leaving schema descriptions to carry the semantic load.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool runs a deep research query through a specific agent (researcher-v2) and returns a structured briefing. It identifies the verb, resource, and output, making the purpose unmistakable even without sibling context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'deep research query' implies use for comprehensive research, but there is no explicit statement of when to use vs alternatives or any exclusions. Lacking siblings, the guidance is only implied rather than formally specified.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation5/5

Only one tool exists, so there is no possibility of confusing it with another tool. The tool's purpose is clearly defined in its description.

Naming Consistency5/5

The single tool name follows a credible server_prefix_action convention, and with only one tool there are no inconsistencies to evaluate.

Tool Count2/5

A single tool is too few for a server named 'attestify-mcp' that likely needs additional operations like listing or managing research jobs and evidence ledger entries.

Completeness2/5

The tool only runs a research query but provides no way to list previous queries, retrieve detailed results, or manage the evidence ledger, leaving significant gaps in the expected domain.

Maintenance

ActivityMaintained
ResponsivenessSyncing

Resources

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