company-intelligence

price_product

Price a Velarion product. Handles EVERY catalog SKU in its real state.

product_type may be a sku_id (e.g. "SKU-002") OR a custom_artifact_family (e.g. "peer_disclosure_custom_cohort"). Returns the row's classification and, when APPROVED_SELLABLE, the price a buyer is actually charged:

APPROVED_SELLABLE → proposed_price_cents + currency + fulfillment_type + latency + caveats. No price floor touches it (deleted 2026-07-14); the catalog/pricer anchor IS the billed price.
NEEDS_OWNER_APPROVAL → {status: "needs_owner_approval"} structurally — price is known but the row is pending Andy's approval flip.
UNSELLABLE / KILLED → structured error with the reason.

custom_band families price via the profit-aware pricer (respecting MERCHANT_MIN_MARGIN); one_off_fixed SKUs price at the canonical catalog price_cents. No fabricated price is ever returned (price_verified gate enforced upstream in classify_sku).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ticker`	Yes
`buyer_type`	No
`agent_token`	No
`product_type`	Yes
`scope_params`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A3.7/5.0

Behavior5/5

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

With no annotations, the description fully discloses behaviors: price floor deletion, profit-aware pricer, no fabricated returns, and detailed status outcomes. This is comprehensive.

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

Conciseness3/5

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

The description is verbose with detailed status explanations. While front-loaded with purpose, it could be more concise by deferring details to the output schema or separate sections.

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

Completeness3/5

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

Has output schema and explains returns for each status, but fails to document all parameters. Given 5 parameters, the description is incomplete without explaining ticker, buyer_type, etc.

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 coverage is 0%, so description must compensate. It explains product_type (SKU ID or family) but does not describe ticker, buyer_type, agent_token, or scope_params, leaving gaps.

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 prices Velarion products, handles all SKUs, and explains outputs for different statuses. It is distinct from siblings like list_skus (listing) and fulfill_paid_order (fulfillment).

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. The description does not compare to sibling tools or specify context where this tool is preferred.

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

Each tool targets a distinct purpose: pay benchmarking, multi-company comparison, governance card generation, risk prediction, compensation lookup, product pricing, ordering, fulfillment, and catalog listing. No two tools overlap significantly; descriptions clearly differentiate them.

Naming Consistency5/5

Tool names consistently follow a verb_noun pattern (e.g., benchmark_executive_pay, compare_companies, place_order). The pattern is uniform across all 9 tools, with no mixed conventions or abbreviations that break consistency.

Tool Count5/5

With 9 tools, the server covers its domain—executive compensation, governance, risk, and commerce—without being too sparse or overwhelming. Each tool has a clear role, and the count fits the scope well.

Completeness4/5

The tool surface covers core needs: pay benchmarking, multi-company comparison, governance card, risk prediction, compensation data, and product commerce (pricing, ordering, fulfillment). Minor gaps exist, such as lacking a tool for historical data retrieval or quote updates, but the set is largely complete for its stated purpose.

Resources

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