RentSeek Evidence

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

Annotations already provide readOnlyHint: true, so the description adds value by disclosing authentication requirements, credit cost, and the disclaimer. It also lists the returned fields, giving behavioral context beyond the annotations.

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

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

Two sentences with all essential information: what the tool returns, required authentication, cost, and a disclaimer. No filler, every sentence is necessary.

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

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

For a simple tool with one parameter and no output schema, the description is fairly complete: it lists the returned elements and provides usage requirements. Slightly more detail on status values could help, but it's adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% for the single parameter claim_id. The description compensates by specifying the format 'RentSeek `clm_...` claim ID', adding meaningful context beyond the schema's generic string type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Returns the value, filing source, release identity, methodology version, and current or superseded status for a RentSeek `clm_...` claim ID', specifying the verb (Returns) and the resource (claim evidence for a specific claim ID). It distinguishes from siblings like get_company_dossier which are unrelated.

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

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

The description includes requirements ('Requires OAuth or X-API-KEY and costs 1 credit') and a caution ('not investment advice'). While it doesn't explicitly state when to use vs alternatives, the context of RentSeek claims makes it clear this tool is for claim evidence, differentiating from siblings covering different domains.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already indicate readOnlyHint=true (safe read) and openWorldHint=false. The description adds credit cost and a disclaimer about the result being evidence, not advice. This is modest added value beyond what annotations convey.

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

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

Two sentences that immediately state purpose and contents, with no wasted words. Every sentence adds value: core function, then auth/cost/disclaimer.

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

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

Given moderate complexity (3 params, no output schema) and sibling tools that are more specific, the description covers purpose, contents, auth, cost, and disclaimer. It does not explain the return format beyond 'compact/full' but is otherwise complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains the format parameter (full/compact) and the fields parameter (projection), but does not describe the ticker parameter beyond its requirement or provide detailed syntax. Partial compensation, not fully detailed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies a clear verb ('returns') and resource ('sourced company dossier for a ticker') and lists specific contents (executives, compensation, pay ratio, etc.), distinguishing it from sibling tools like get_claim_evidence or get_executive_compensation which are more narrowly scoped.

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

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

The description mentions authentication requirements and credit cost but offers no guidance on when to use this tool versus its siblings or when not to use it. No explicit context for selection is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already indicate readOnlyHint=true, but the description adds valuable behavioral context: credit costs (0 vs. 1), authentication needs (OAuth/X-API-KEY for historical requests), and the disclaimer about filing data vs. 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.

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

The description is two concise sentences. The first sentence packs all key functional info; the second adds cost/auth/exclusions. No unnecessary words, front-loaded structure.

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

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

Despite no output schema, the description covers what is returned (pay components, totals, URLs), identifier options, fiscal-year filters, and cost/auth. It lacks detailed return format but is sufficient for a data retrieval tool. The exclusions and disclaimer add important completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 57%, so the schema already documents most parameters. The description adds no parameter-level details beyond what the schema provides, but it does summarize the overall filtering capability. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns annual named-executive compensation rows for a company by ticker or CIK, with specific filters and fields. It explicitly differentiates from sibling tools like get_company_dossier by stating what is excluded (pay ratio, pay-versus-performance, dossiers).

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

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

It provides explicit credit costs and authentication requirements for latest vs. historical/multi-FY requests. It also indicates when not to use (for excluded data), though it doesn't name alternative tools by name. This is clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already indicate read-only and non-open-world. Description adds important context: no authentication, 0 credits, returns availability only, and disclaims investment advice. No contradiction with annotations.

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

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

Two sentences efficiently convey purpose, operational details, and caveats. No superfluous information.

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

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

Covers core functionality and important caveats. Could mention response format (e.g., array of strings) since no output schema, but not essential for a simple list tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, and schema coverage is 100%. Description adds meaning about the tool's purpose without needing to explain parameters. Baseline of 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it returns ticker symbols for companies with published RentSeek executive-compensation data. Differentiates from siblings like get_executive_compensation by noting it provides availability only, not compensation values.

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

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

Implies use as a discovery tool before fetching compensation data, but does not explicitly mention when to use it over siblings or provide alternatives. Mentions free cost and no authentication as encouragement.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations declare readOnlyHint=true and openWorldHint=false. The description adds behavioral context: authentication method, credit cost, and the disclaimer that output is filing evidence. No contradictions. It provides value beyond annotations by explaining usage constraints and data limitations.

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

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

The description is two sentences: one covering the primary function, constraints, and features; the second covering exclusions and a disclaimer. It is front-loaded with the key action and efficiently uses every sentence without redundancy.

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

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

Given the tool has 7 parameters (2 required), no output schema, and a non-trivial domain (executive compensation), the description covers main points: output type, ticker limit, date scope, pagination, thresholds, authentication, and cost. It could be more detailed about pagination behavior, but it is largely sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage (all 7 parameters have descriptions). The description mentions 'configurable thresholds' and 'cursor pagination,' but these are already implicit in the schema-based parameter details. No additional parameter meaning is added, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns 'classified executive-compensation disclosure changes' for 1-50 tickers since a date/timestamp, with configurable thresholds and cursor pagination. It distinguishes from sibling tools like get_executive_compensation (which likely provides full data) and list_available_tickers (different purpose).

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

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

The description notes authentication requirements and cost ('Requires OAuth or X-API-KEY and costs 2 credits per page'), and clarifies that the result is 'filing evidence, not investment advice.' It also states what is not included (current snapshots, full historical rows). However, it does not explicitly compare to sibling tools or state when to prefer this over alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.