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Velarion — Executive Compensation Intelligence

Velarion — Executive Compensation Intelligence

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intel.velarion.aiFinance0Updated 15w ago

About Velarion — Executive Compensation Intelligence

Velarion provides structured executive-compensation and governance data for US public companies to agents and analysts. Its tools derive deterministic benchmarks and say-on-pay signals from proxy filings, with coverage limits stated rather than guessed.

Key features

  1. 01Look up executive compensation
  2. 02Benchmark CEO pay against disclosed peers
  3. 03Assess say-on-pay risk and trends
  4. 04Compare companies' compensation and governance
  5. 05Generate governance snapshot cards
  6. 06Quote and retrieve paid research artifacts

Use cases

  1. 01Benchmark a company's CEO pay against peers
  2. 02Review say-on-pay approval trends
  3. 03Compare governance across covered companies
  4. 04Request a custom peer-cohort audit

MCP support

Velarion exposes compensation, benchmarking, governance, and artifact-ordering tools through a streamable-HTTP MCP server. MCP clients connect using an agent token.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://api.velarion.ai/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://api.velarion.ai/mcpAuth required

Tools

9
ParameterTypeRequiredDescription
tickerstringYes
fiscal_yearanyNo
agent_tokenstringNo

Details

From the project README

Velarion Company Intelligence — MCP Server

Executive-compensation, say-on-pay, and governance data for US public companies, exposed to AI agents over the Model Context Protocol.

An agent connects, self-issues a token in one HTTP call (no human in the loop), and immediately queries structured comp and governance data. Read tools and three free Governance Alpha Cards per day work on the free tier; deeper artifacts settle through a metered pay rail.


What it covers

A 3,000-company universe — ~1,200 active tickers across 12 industries plus their disclosed peer companies. Most have extracted executive compensation, director compensation, and say-on-pay history. Data is sourced from annual proxy filings and refreshed nightly; every response is current as of the most recently processed filing.

A company outside the covered universe returns not_in_coverage rather than a guess — the server never fabricates a figure for a company it does not track.

12 industries: Banking · Real Estate · Tech (Software) · Biopharma & Life Sciences · Tech (Hardware) · Insurance · MedTech & Diagnostics · Utilities · Asset Management · Gaming & Lodging · Payments & Financial Infrastructure · Specialty Finance.


Tools

ToolWhat it returnsTier
lookup_company_compensationFull Summary Comp Table for every named executive: salary, bonus, stock/option awards, non-equity incentive, total.Free (read)
benchmark_executive_payCEO pay percentile vs. the company's disclosed peer set, TSR percentile, pay-for-performance gap, alignment label.Free (read)
predict_say_on_pay_riskSay-on-pay approval trend, year-over-year change, risk band, governance-friction signals.Free (read)
compare_companiesSide-by-side comp and governance across two or more covered companies.Free (read)
generate_governance_alpha_cardA one-page governance snapshot for a company.3 free/day
price_productQuote a custom artifact (peer-cohort audit, extended governance pull, say-on-pay window).Free
place_orderOpen a paid order against a quote.Metered
fulfill_paid_orderRetrieve the artifact once settlement clears.Metered
list_skusEnumerate the sellable catalog — fixed SKUs and custom artifact families — with pricing shape and latency.Free

Paid artifacts are withheld until payment clears, and every artifact is checked against the underlying data before it ships — the server will refuse to deliver a narrative it can't ground. See docs/custom-requests.md for the paid-artifact flow.


Quick start

1. Self-issue a token (one call, instant)
curl -X POST https://api.velarion.ai/agent/v1/token/self-serve \
  -H 'Content-Type: application/json' \
  -d '{"agent_label": "my-agent", "contact_email": "[email protected]"}'

Returns your token once — store it:

{
  "token": "vlragt_...",
  "scopes": ["mcp:read", "mcp:buy"],
  "balance_cents": 0,
  "mcp_endpoint": "https://api.velarion.ai/mcp",
  "header": "X-Velarion-Agent-Token",
  "free_tier": {
    "governance_alpha_card": "3 free cards per day",
    "read_tools": "lookup_company_compensation, benchmark_executive_pay, predict_say_on_pay_risk, compare_companies"
  }
}
2. Register the server with your client

Claude Desktop / Claude Code (claude_desktop_config.json or via claude mcp add):

{
  "mcpServers": {
    "velarion": {
      "type": "streamable-http",
      "url": "https://api.velarion.ai/mcp",
      "headers": {
        "X-Velarion-Agent-Token": "vlragt_your_token_here"
      }
    }
  }
}
3. Ask

Once connected, ask your agent things like:

"What did Microsoft's CEO earn last year, and how does it compare to disclosed peers?" "Is JPMorgan at risk of a say-on-pay revolt?" "Compare CEO pay-for-performance alignment across the three largest banks in coverage."


Connecting

Full connection flow for any MCP client (Claude Desktop, Claude Code, a custom agent, or an agent procured via a marketplace): docs/connecting.md.

Sample outputs

These are real responses from the live server, lightly trimmed.

lookup_company_compensation("MSFT")

{
  "ticker": "MSFT",
  "company_name": "Microsoft Corp",
  "fiscal_year": 2025,
  "executives": [
    {
      "last_name": "Nadella", "first_name": "Satya",
      "position": "CEO", "title": "Chairman and Chief Executive Officer",
      "sct_salary": 2500000, "sct_stock_awards": 84245496,
      "sct_non_equity_incentive": 9555000, "sct_all_other": 196294,
      "total_comp": 96496790, "is_departed": false
    },
    {
      "last_name": "Hood", "first_name": "Amy",
      "position": "CFO", "title": "Executive Vice President and Chief Financial Officer",
      "sct_salary": 1000000, "sct_stock_awards": 25037360,
      "sct_non_equity_incentive": 3421000, "total_comp": 29481551
    }
  ]
}

predict_say_on_pay_risk("JPM")

{
  "ticker": "JPM",
  "risk_band": "low",
  "latest_sop_approval_pct": 92.74,
  "sop_yoy_change_bps": 91,
  "trend_phrase": "Say-on-Pay approval trend: FY2024 91.8% → FY2025 92.7%.",
  "governance_friction_summary": "Governance friction signals: none identified from available data.",
  "limitations": "Deterministic signal only. Does not incorporate narrative CD&A context or ISS/Glass Lewis advisory outputs."
}

Who this is for

Comp consultants, institutional investors, corporate compensation committees, governance analysts — and the agents that work on their behalf. Velarion structures what public companies disclose about how they pay their leadership, so an agent can answer a benchmarking or governance question in one call instead of parsing a filing.

Trust & limitations

  • Sourced from annual proxy filings, processed nightly. A response is current as of the most recently processed filing for that company.
  • Deterministic where it matters. Say-on-pay and benchmarking signals are computed from disclosed figures, not model guesses. Narrative outputs are grounded against the underlying numbers before delivery.
  • Coverage is bounded and honest. Outside the ~3,000-company universe, tools return not_in_coverage.
  • Not investment advice. Not a substitute for a company's own filings.

Support

Questions, coverage requests, or integration help: [email protected]

Comments

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