Velarion Company Intelligence

by ai.velarion

Overview Discussions Admin

Benchmark executive pay vs peers

benchmark_executive_pay

Read-onlyIdempotent

Benchmark executive pay vs. disclosed peers using Velarion's canonical percentile data.

Returns pay percentile, performance (TSR) percentile, pay-for-performance gap, and an alignment label.

role is CEO-only.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`role`	No	Optional executive role to benchmark (default CEO-only).	CEO
`ticker`	Yes	The company's ticker symbol (e.g. "AAPL").
`agent_token`	No	Optional Velarion agent token; falls back to the X-Velarion-Agent-Token header.
`fiscal_year`	No	Optional fiscal year to read (e.g. 2025). Defaults to the latest on file.

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No
`ticker`	No
`attribution`	No	Source citation, dataset DOI, and a ready-made cite_as string.
`pay_percentile`	No
`tsr_percentile`	No
`alignment_label`	No	Pay-for-performance alignment label.
`peer_rank_caveat`	No
`p4p_gap_percentile_points`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 19, 2026

addedOutput schema / anyOf

Added value: +[
+  {
+    "required": [
+      "ticker"
+    ]
+  },
+  {
+    "required": [
+      "error"
+    ]
+  }
+]

removedOutput schema / required
```
Removed value: -[
-  "ticker"
-]
```

First observedSep 6, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare read-only and idempotent behavior, so the description doesn't need to repeat that. It adds useful behavioral context: the tool is based on canonical percentile data and returns pay and TSR percentiles plus an alignment label. The line 'role is CEO-only' is a meaningful scope constraint, though it could be phrased more clearly.

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 compact and front-loaded: purpose first, then return values, then the role constraint. Every sentence earns its place and there is no redundant filler.

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?

With a rich output schema, full parameter schema coverage, and strong annotations, the description is nearly complete for invoking the tool. The main missing piece is explicit guidance about how this relates to sibling tools such as get_disclosed_peer_group or lookup_company_compensation, so the agent has to infer the intended routing boundaries.

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%, so the input schema already documents ticker, role, agent_token, and fiscal_year. The description adds only the terse 'role is CEO-only' clarification, which is helpful but doesn't substantially extend what the schema already states about parameters.

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 names a specific verb ('Benchmark'), a specific resource ('executive pay vs. disclosed peers'), and the data source ('Velarion's canonical percentile data'). It lists concrete outputs, which makes the tool's function unmistakable and distinguishes it from siblings like get_disclosed_peer_group or get_company_compensation.

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

Usage Guidelines4/5

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

The description clearly implies the usage context: call this when the agent needs a peer-relative pay benchmark with percentile outputs. It does not explicitly name alternative tools or state when not to use it, but the purpose is specific enough that an agent can route to it correctly.

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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