Fundz

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, and the description adds 'No API key required,' which covers auth expectations. It also adds data provenance and recency constraints (≤24 months, Form 5500, Schedule A) that annotations cannot convey. No contradiction exists.

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 tightly written sentences with no filler. The data criteria lead, followed by intended audience and the auth note, so every sentence earns its place.

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 read-only tool with two optional, schema-documented parameters and no output schema, the description provides enough context: data source, recency window, key result fields, intended users, and auth. Minor omissions like pagination or ordering are acceptable for this simple use case.

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 already describes both optional parameters fully (company name search and state filter). The description's matching criteria relate to the dataset itself rather than parameter formats, so it adds no additional parameter semantics beyond the schema.

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 exactly what the tool surfaces: recently funded companies with DOL Form 5500 filings, including renewal timing, participant-count vs headcount comparison, and carrier from Schedule A. It names the target audience and data source, making it distinct from the sibling list tools. While it lacks an explicit 'returns' verb, the content is unambiguous.

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 target personas are explicitly listed—benefits brokers, retirement-plan advisors, PEOs, and HR-tech sellers—so an agent can infer when this list is relevant. It does not name sibling tools or give exclusion criteria, so it earns 4 rather than 5.

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 mark it read-only, idempotent, and non-destructive. The description adds meaningful context beyond that: data source (DOL Form 5500), scale (58,000+ brokers), ranking criteria (filings carried), and the fact that no API key is required. This enriches the agent's understanding without contradicting 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?

The description is a single well-structured sentence that front-loads the core purpose, then adds target audience and a practical note (no API key). It is concise while including essential context, though it could trim a few words without loss.

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 read-only directory tool with three optional parameters and no output schema, the description covers the data content, source, ranking, and audience. It doesn't need to explain return values since there's no output schema, and pagination is already in the schema via the page parameter. It is sufficiently 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 100% – each parameter (q, page, state) is already documented. The description adds no param-specific guidance beyond the schema, so a baseline of 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?

States a specific resource (benefits brokers from DOL Form 5500 Schedule A Part 1) with clear detail on ranking and metrics. The sibling tools focus on other data domains (benefit plans, lenders, money movement, etc.), so this tool is easily distinguished without needing to open its schema.

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 gives explicit use cases (carriers, broker M&A, benefits-tech sellers, brokers scouting competitors) that make the intended context clear. It does not name specific sibling tools or state when not to use it, but the purpose is so domain-specific that the usage is effectively implied.

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 cover read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable behavioral context: data freshness ('refreshed daily'), result ordering ('score-ranked'), and authentication requirements ('No API key required'). 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?

The description is a single, information-dense sentence. It front-loads the core selection criteria, then adds ordering, freshness, and auth details without any filler. Every clause contributes to correct tool usage.

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 list tool with two optional parameters and no output schema, the description explains what results look like conceptually, how they are ranked, how fresh they are, and that no API key is needed. It does not describe the exact return structure, but the absence of an output schema and the simplicity of the tool make this an acceptable minor gap.

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 100%, with q and state both documented in the input schema. The description does not add material meaning about how parameters behave beyond the schema, so the baseline score of 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 states a specific verb-resource relationship: it returns companies matching verified funding in the last 12 months AND live hiring evidence. The criteria are concrete and distinguish this tool from sibling tools like get_benefit_plans_in_play or get_lender_directory, which target different business signals.

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 gives clear context for when to use it: when targeting funded companies with active hiring signals, described as 'the strongest buying window.' It does not explicitly name alternatives or when-not-to-use conditions, but the specificity of the criteria makes intended usage apparent.

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 declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. Beyond that, the description adds meaningful context: 'No API key required' and 'CO refreshed daily' — disclosing accessibility and data freshness, which are not captured in annotations. No contradictions found.

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 tightly written sentences with no fluff. The opening hook ('Who finances whom') is immediately followed by specific, high-value details (volume ranking, lien counts, exposure, coverage, refresh frequency, API key). Every word earns its place, and key information is front-loaded.

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 2-parameter tool with no output schema and safety carried by annotations, the description is complete. It tells the agent what data the tool returns (active-lien counts, lapsing-soon exposure), pagination defaults are in the schema, and it adds operational context (daily refresh, no API key). Nothing essential is missing.

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 100% for both parameters ('q' and 'page'), so the baseline is 3. The description does not add extra meaning beyond the schema; it restates the context (lender directory) but does not elaborate on formatting or ordering beyond what the schema already provides. It is sufficient but not additive.

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 a specific resource ('directory of 11,800+ UCC secured parties (lenders)') and its unique value proposition: ranked by filing volume with active-lien counts and lapsing-soon exposure. It also specifies geographic coverage (CA + CO), making it unambiguous what the tool provides and differentiating it from broker or other financial directories.

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 implies usage by framing 'Who finances whom' and listing coverage and no-API-key requirement, but it does not explicitly state when to use this tool over siblings or include exclusions. It lacks a direct 'use this when you need lender data' or mention of alternatives, leaving the agent to infer the scope.

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?

Beyond the readOnly/openWorld/idempotent annotations, the description adds concrete behavioral details: data is 'Score-ranked, refreshed daily,' has a 180-day/24-month lookback window, and requires 'No API key.' This gives an agent useful expectations about recency, ranking, and access without contradicting 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?

The description is efficiently packed with the core criteria first, followed by the use-case explanation and audience. The 'money in motion' explanation is slightly colorful but earns its place by clarifying why this data matters. No filler or 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?

The description conveys what is returned (companies meeting two conditions), the ranking mechanism, refresh cadence, intended audience, and authentication requirement. There is no output schema, so return-field detail is limited to 'score-ranked,' but that is enough to select and invoke the tool correctly.

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 already documents both optional parameters with 100% coverage: q is 'Company name search' and state is a '2-letter US state filter.' The description adds no additional parameter-level detail, so the baseline of 3 applies for a fully schema-covered parameter set.

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 states a precise resource and selection criteria: companies with an executive-move announcement in the last 180 days AND a verified funding round in the last 24 months. This clearly differentiates the tool from all siblings, which target benefits, brokers, lenders, refinancing, and hiring. The verb 'get' and the noun phrase 'Companies with...' make the operation obvious.

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 names a clear target audience—RIAs, wealth managers, retirement-plan advisors, executive-benefits sellers—and explains the 'money in motion' scenario that triggers use. It does not explicitly contrast with sibling tools like get_funded_and_hiring, but the audience and use case provide strong contextual guidance.

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 declare this read-only, idempotent, and non-destructive. The description adds meaningful behavioral context: UCC liens expire after 5 years, the incumbent lender must re-file or lose priority, and results represent windows with dates. It also discloses coverage limitations and that no API key is required, giving agents a fuller picture of what to expect.

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 moderate in length but every sentence contributes: core output, the legal/domain rationale, target audience, coverage, and access requirements. The key concept is front-loaded, and the supporting detail is tightly written. It earns a 4 rather than 5 because the legal explanation could be slightly trimmed without losing meaning.

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 read-only, no-output-schema data lookup, the description is largely complete: it explains what is returned, the geographic coverage, refresh cadence, and authentication requirement. Minor details such as whether results are sorted by expiration date or the exact output fields are absent, but these are not critical for an agent to invoke the tool correctly.

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?

Both parameters are fully documented in the schema, so the description is not required to carry the burden. It does add contextual value by implying the 'state' filter is relevant to the California/Colorado coverage and framing 'q' as a company name search, but it provides no additional syntax or parameter-specific guidance beyond schema. 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 lists companies whose active UCC-1 liens lapse within 12 months, framing each as a refinancing window. This is a specific, well-defined resource that is obviously distinct from the sibling tools (which cover benefits, brokers, lenders, etc.). The 'Renewal Radar' title is explained rather than left vague.

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 identifies the target users (lenders, MCA/ISO, equipment finance) and notes the practical value (lien expiration creates refinancing opportunities). It also gives coverage scope (California + Colorado) and update cadence (CO refreshed daily). It doesn't explicitly state when not to use it or name an alternative, but the strong domain context makes the intended use clear.

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 establish read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond this: data scope (California + Colorado), refresh cadence (CO refreshed daily), and the auth requirement (no API key required), all of which help an agent set expectations.

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 compact and front-loaded: it states the defining criteria first, then the use case, then coverage/access details. Every sentence earns its place with no vague filler.

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 read-only lookup with two optional, fully described parameters and no output schema, the description covers the essential facts: what the companies are, why they matter, where they are located, how fresh the data is, and whether authentication is needed. An agent can select and invoke this tool confidently.

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 100%, so the baseline is 3. The description adds value by constraining the state parameter to California and Colorado, which is more specific than the schema's generic '2-letter US state filter' and prevents invalid state usage.

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 defines the resource precisely: companies with active UCC-1 secured debt from 2+ distinct lenders and a filing in the last 24 months. This clear criteria-based definition distinguishes it from sibling tools like get_refinancing_windows and get_money_in_motion without ambiguity.

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 gives strong usage context: it positions the tool for finding layered-financing appetite and second-position/refi/consolidation targets. It also notes geographic coverage and that no API key is required, but it does not explicitly name sibling alternatives or state when not to use it.

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