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RentSeek Evidence

Get Company Evidence Dossier

get_company_dossier
Read-only

Returns a sourced company dossier for a ticker, including executives, compensation history, pay ratio, pay-versus-performance, claims, and release provenance, with optional full or compact format and field projection. Requires OAuth or X-API-KEY and costs 3 credits. The result is filing evidence, not investment advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNo
formatNofull
tickerYes

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true. Description adds that it costs 3 credits, requires OAuth or X-API-KEY, and that results are filing evidence not investment advice. No contradictions.

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, front-loaded with purpose, no fluff. Every sentence adds essential information.

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?

No output schema, but description lists key contents and qualifies the result as filing evidence. Could elaborate on return structure or pagination, but it's reasonably complete for a dossier retrieval tool.

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 0%, so description must compensate. It mentions 'ticker' (implied), 'optional full or compact format', and 'field projection'. It lists some fields (e.g., executives, compensation_history) but does not fully enumerate all enum values from the schema (e.g., company, sources, limitations are missing). Adds partial meaning.

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?

Clearly states the tool returns a sourced company dossier for a ticker, listing specific contents like executives, compensation, pay ratio, etc. Distinguishes from sibling tools like get_executive_compensation (narrower) or get_claim_evidence (specific to claims).

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?

Specifies authentication requirements and credit cost, but does not explicitly state when to use this tool vs siblings or when not to use it. Lacks explicit alternatives or exclusions.

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

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct aspect of executive compensation data: claim evidence, full company dossier, compensation rows, ticker listing, and material changes. Descriptions clearly differentiate them, preventing confusion.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern using snake_case: get_* for retrieval and list_* for enumeration. No mixing of styles or irregular verbs.

Tool Count5/5

Five tools is well-scoped for the domain of accessing executive compensation filing data. Each tool provides essential functionality without unnecessary bloat or gaps.

Completeness4/5

The tool set covers core operations: retrieving evidence, dossiers, compensation details, available tickers, and material changes. A minor gap is the lack of a direct tool for bulk compensation data across multiple companies, but the set is largely complete.

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