Velarion Company Intelligence

Predict say-on-pay risk

predict_say_on_pay_risk

Read-onlyIdempotent

Predict Say-on-Pay risk for a company using Velarion's deterministic risk compilers.

Returns trend phrase, peer cohort distribution note, governance friction summary, and overall risk band, computed from disclosed vote results and compensation data.

Input Schema

TableJSON Schema

Name	Required	Description
`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
`trend`	No	Trend phrase for the vote history.
`ticker`	No
`risk_band`	No	Overall say-on-pay risk band.
`attribution`	No	Source citation, dataset DOI, and a ready-made cite_as string.
`fiscal_year`	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

A4/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the bar is lower. The description still adds non-obvious behavioral detail: it is deterministic and produces four named output components derived from vote/compensation data. This communicates what the agent will receive beyond what the annotations state.

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?

Two sentences with no filler; the action and outputs are front-loaded. Every clause adds information, including the data sources and the deterministic nature.

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 an output schema present and all parameters documented, the description does not need to restate return structure. It is complete enough to call for a risk prediction, though it could be strengthened by adding selection guidance versus sibling tools.

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 baseline is 3; the description adds no parameter-specific detail beyond the schema. It does not mention ticker, agent_token, or fiscal_year, but the schema already documents their types and defaults.

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 leads with a specific verb and resource ('Predict Say-on-Pay risk for a company') and lists concrete outputs, so an agent can tell it is a risk-scoring operation rather than a raw lookup like get_say_on_pay_history. The reference to 'deterministic risk compilers' further differentiates it from sibling tools. This makes the purpose unmistakable.

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?

No explicit when-to-use or when-not-to-use guidance appears; the description never names alternatives such as get_say_on_pay_history or benchmark_executive_pay. The context implies this tool is for generated risk predictions ('computed from disclosed vote results'), but exclusions and selection criteria are left to the agent.

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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