Velarion — Executive Compensation Intelligence
VerifiedFeaturedRemoteAbout 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
- 01Look up executive compensation
- 02Benchmark CEO pay against disclosed peers
- 03Assess say-on-pay risk and trends
- 04Compare companies' compensation and governance
- 05Generate governance snapshot cards
- 06Quote and retrieve paid research artifacts
Use cases
- 01Benchmark a company's CEO pay against peers
- 02Review say-on-pay approval trends
- 03Compare governance across covered companies
- 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.
https://api.velarion.ai/mcpclaude mcp add --transport http velarion-scraper-production-01a332 https://api.velarion.ai/mcp{"mcpServers":{"velarion-scraper-production-01a332":{"type":"http","url":"https://api.velarion.ai/mcp"}}}Open in Cursor{"servers":{"velarion-scraper-production-01a332":{"type":"http","url":"https://api.velarion.ai/mcp"}}}Open in VS Code{"mcpServers":{"velarion-scraper-production-01a332":{"type":"http","url":"https://api.velarion.ai/mcp"}}}{"mcpServers":{"velarion-scraper-production-01a332":{"serverUrl":"https://api.velarion.ai/mcp"}}}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.
https://api.velarion.ai/mcpAuth requiredTools
9| Parameter | Type | Required | Description |
|---|---|---|---|
| ticker | string | Yes | |
| fiscal_year | any | No | |
| agent_token | string | No |
Details
From the project READMEVelarion 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.
- Endpoint:
https://api.velarion.ai/mcp(streamable-HTTP) - Registry:
ai.velarion/company-intelligenceon the official MCP Registry - Website: https://intel.velarion.ai
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
| Tool | What it returns | Tier |
|---|---|---|
lookup_company_compensation | Full Summary Comp Table for every named executive: salary, bonus, stock/option awards, non-equity incentive, total. | Free (read) |
benchmark_executive_pay | CEO pay percentile vs. the company's disclosed peer set, TSR percentile, pay-for-performance gap, alignment label. | Free (read) |
predict_say_on_pay_risk | Say-on-pay approval trend, year-over-year change, risk band, governance-friction signals. | Free (read) |
compare_companies | Side-by-side comp and governance across two or more covered companies. | Free (read) |
generate_governance_alpha_card | A one-page governance snapshot for a company. | 3 free/day |
price_product | Quote a custom artifact (peer-cohort audit, extended governance pull, say-on-pay window). | Free |
place_order | Open a paid order against a quote. | Metered |
fulfill_paid_order | Retrieve the artifact once settlement clears. | Metered |
list_skus | Enumerate 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
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.