kcalbin-data-products

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

With no annotations, the description carries the burden and does well: it discloses the headless-browser mechanism, that JavaScript executes, and the tier-dependent return behavior (preview = title + excerpt, paid = full text + links). It omits auth requirements, rate limits, and failure/timeout behavior, which keeps it from a 5.

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?

Three compact sentences, front-loaded with the core action and mechanism, then the differentiating capability, then the output/tier behavior. 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?

No output schema, so the description usefully explains return values and the preview/paid distinction. Reasonably complete for a single-parameter fetch tool, though error modes and limits are unaddressed.

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?

Only one parameter (url) at 0% schema coverage; the description adds the 'public web page' constraint, which is meaningful, but does not clarify URL format, redirect handling, or JS-render wait semantics. Modest value beyond the self-evident parameter name.

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 verb and resource (render a web page) plus a precise scope (public pages, headless browser) and names what it returns. The sibling tools are entirely unrelated domains, so no differentiation is needed.

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?

Explicitly gives the selecting condition: 'JavaScript is executed, so it works on pages a plain HTTP fetch cannot read.' It also distinguishes the free-preview vs paid tiers. It stops short of naming an alternative tool or stating when not to use it, but the context is 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?

With no annotations, the description carries the full behavioral burden. It usefully discloses that output is tier-gated (a free subset vs a paid full result), which is genuine behavioral context, but it omits auth/subscription mechanics, how the paid tier is unlocked, and any rate-limit or error behavior.

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 compact sentences with the core framing front-loaded and no filler. The branding prefix and pricing detail are slightly marketing-flavored but still carry information and do not bloat the definition.

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 tool with two undocumented parameters, no annotations, and no output schema, the description is incomplete: it neither explains the inputs nor clarifies the shape of returned data beyond a tier listing. An agent cannot confidently call it or interpret results from this text alone.

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 two parameters (limit, state), so the description must compensate and does not. It mentions 'county name' (an output field, not a parameter) but never explains state or limit, leaving both inputs undocumented.

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 names a resource (county demand-intelligence for local service businesses) but never states a verb or the actual operation the tool performs. It reads more like a product/pricing blurb than a definition of what the call does, leaving the agent to infer that it retrieves county-level 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?

There is no when-to-use, when-not-to-use, or alternative-tool guidance. The free/paid tier split describes output content, not when to select this tool over siblings like chandler_warn or chandler_economic_context, so selection is left entirely to inference.

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?

With no annotations, the description carries the full burden. It discloses that the tool is PAID, which is genuine behavioral context absent from structured fields, but says nothing about rate limits, auth requirements, response format, or whether signal_type filters or defaults.

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 terse fragments, front-loaded with the resource and category scope, with the critical cost flag at the end. Every token carries information, though the telegraphic style leaves meaning implicit.

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?

No output schema and no annotations, so the description must explain return shape, cost model, and parameter behavior — and it does not. Only the data domain and the PAID flag are conveyed, which is insufficient for an agent to invoke it 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 0% and signal_type is entirely undocumented in the schema. The description's enumeration of signal categories (bankruptcy, WARN, NLRB) hints at the values signal_type accepts, providing partial compensation, but it does not specify the accepted tokens or behavior when omitted.

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 (court filings: bankruptcy, WARN lawsuits, NLRB) and frames them as displacement events, which tells the agent what data it returns. However, it does not distinguish itself from the sibling chandler_warn, which likely covers overlapping WARN-related data, leaving ambiguity about which to pick.

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?

There is no explicit when-to-use guidance or named alternative. The 'PAID.' token is a useful cost warning that may influence tool selection, but it does not tell the agent what conditions call for this tool versus chandler_warn or chandler_signals.

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?

With no annotations, the description carries the full burden. It discloses data provenance, geographic scope, and an enterprise access requirement, but omits whether the operation is read-only, pagination behavior, rate limits, and output format.

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, front-loaded with the key data sources and scope. The trailing 'Included with Chandler Enterprise' slightly repeats the leading 'ENTERPRISE' label, but otherwise it is tight and waste-free.

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 data-retrieval tool with no output schema and no annotations, the description covers what indicators are returned but leaves critical gaps: parameter usage, read-only behavior, and output structure are unexplained. An agent cannot confidently invoke it without guessing how to filter or limit results.

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% and both parameters (limit, state) are undocumented in the schema. The description mentions 'county' and 'STATE level' but never explains how the state or limit parameters affect the query, leaving the agent with no semantic guidance.

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 names the specific data assets (Census building permits, BLS QCEW, NY Fed delinquency) and their geographic granularity (county, state), making the resource clear. It lacks an explicit action verb (e.g., 'retrieves') and does not differentiate itself from sibling tools like chandler_signals, so it falls short of a 5.

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?

No guidance is given on when to use this tool versus alternatives, nor are there any exclusions or prerequisites beyond the enterprise access note. The enterprise note is an access condition, not usage 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?

With no annotations, the description carries the full burden, and it does disclose one genuinely useful trait: the free preview masks county names. It does not state whether this is a read-only operation, what auth or rate limits apply, or how many rows/pages are returned, so the behavioral picture is only partly filled in.

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 tight sentences with the resource and payload fields front-loaded, and the masking caveat placed immediately after. Appropriately sized, though the parenthetical field list reads more like schema content than added guidance.

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?

There is no output schema and no parameter documentation, so the description should explain what a call returns and how limit/state shape the result. It names three fields but omits filtering behavior, geography scoping, and pagination, leaving the definition short of what a data-retrieval tool needs.

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 (limit, state) have 0% schema description coverage, and the description never mentions them, so the agent gets no meaning for either. This is the case where description must compensate for a schema gap, and it does not.

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 (county-level workforce displacement signals) plus the fields returned (severity, score, forecast), which distinguishes it from siblings like chandler_commerce_signals and chandler_court_signals by domain. It is clear what the tool retrieves, though it never explicitly contrasts itself with those siblings.

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?

No guidance on when to prefer this over chandler_economic_context, chandler_warn, or the other chandler_* signal tools, and no stated prerequisites. The only usage-adjacent detail is the free-preview masking note, which hints at tiering but does not tell the agent when to call this.

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?

With no annotations, the description carries the full burden, and it does disclose one genuinely useful behavioral trait: the free preview masks company names, which sets expectations about output completeness. However, it says nothing about read-only vs. mutating behavior, rate limits, or why names are masked (paywall), leaving significant behavioral gaps.

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 tight sentences with zero filler, and the resource description is front-loaded ahead of the preview caveat. It is efficient, though extremely terse.

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, output-schema-less lookup tool, describing the returned fields and the preview limitation covers a fair amount. But the lone input parameter is unexplained and there is no guidance on scope, so an agent cannot fully invoke it 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?

Schema coverage is 0% and the sole parameter (state) is undocumented in both schema and description. The listed fields (company, workers affected, dates) describe output, not the input filter, so the agent gets no guidance on how to scope the query geographically.

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?

Names a specific dataset (WARN Act layoff notices) and enumerates the returned fields (company, workers affected, dates), which is far more informative than its bare name. It does not explicitly differentiate itself from siblings like chandler_court_signals or chandler_signals, so it falls short of a 5.

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?

There is no when-to-use guidance and no mention of alternatives among the chandler_* family. The only contextual clue is a note about the free preview, which is a data caveat rather than usage direction.

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?

With no annotations and no output schema, the description carries the full burden. It discloses what data comes back (series plus baseline) but says nothing about the 'ENTERPRISE' gating, permissions, request size, cost, or rate limits — all of which are material for a bulk extraction tool.

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?

A single front-loaded sentence that leads with the tier tag and scope. No filler, though the terse phrasing packs several concepts (counties, year range, baseline) into one clause chain.

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 large enterprise data-dump tool with zero annotations, no output schema, and an undocumented parameter, the description omits volume/format expectations and access prerequisites. An agent can guess the gist but lacks what it needs to invoke this 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 0% for the single 'state' parameter, so the description is the only source of meaning. It says the extract is 'for a state' but gives no format guidance (abbreviation vs full name, FIPS code) to disambiguate the accepted value.

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 concrete verb (bulk extract) and resource (full projection series, all counties x 2025-2040, for a state, plus current-year baseline), which clearly distinguishes it from the narrower deathcare_projection sibling. It never names a sibling explicitly, so it stops short of full differentiation.

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 'bulk' and 'all counties x 2025-2040' framing implies this is the use-everything option versus per-geography projection tools, but no when-to-use condition or alternative tool is stated. Usage must be inferred from the scope wording.

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?

With no annotations, the description carries the full burden. It discloses the ranking method, national percentile scope, and a paid access requirement, which is useful behavioral context. However, it does not state whether the operation is read-only (implied but not explicit), what the output looks like, or any limits on result size or pagination.

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 three short, front-loaded fragments with zero waste. The core purpose appears first, followed by scoring scope and access tier. 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 simple ranking tool with no annotations and no output schema, the description covers purpose, scope, and access tier. But it omits any explanation of the three filter parameters and does not describe what a result contains (e.g., county name, growth metric, percentile), leaving notable gaps for correct invocation.

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% for three parameters (limit, state, min_deaths), and the description does not mention or explain any of them. It provides no meaning for how these filters constrain the ranking, so it fails to compensate for the schema's lack of documentation.

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 and resource: 'Rank counties by projected growth in deaths 2025-2040.' It clarifies the analytical question ('expansion/market-selection') and the scoring scope ('national percentile across all 3,144 counties'). However, it does not explicitly distinguish this tool from siblings like deathcare_projection or deathcare_market, so it stops short of a 5.

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 gives clear context for when to use it: 'the expansion/market-selection question.' It also signals access tier ('PAID (Professional and above)'). But it names no alternative tools and provides no explicit when-not guidance, so it is clear context without exclusions.

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?

With no annotations, the description carries the full disclosure burden. It does add one genuinely useful behavioral trait — free-tier output masks county names — but says nothing about authentication/tier requirements needed to unmask, rate limits, or pagination, leaving most operational behavior unexplained.

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 tight sentences with the purpose front-loaded and the caveat trailing; nothing is redundant. It is slightly terse to the point of omitting necessary parameter detail, but the prose itself is efficient.

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?

No output schema, no annotations, and zero parameter documentation mean the description must compensate for a lot, and it doesn't. An agent cannot learn the FIPS/state format, units of the death count, or what limit does without guessing.

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% for all three parameters. The phrase 'for a county or state' loosely maps to fips and state, but no format, identifier scheme, or default is given, and 'limit' is entirely unexplained in both schema and description.

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 concrete resource and scope — current-year expected deaths for a county or state — plus the audience (death-care operators and investors), so an agent knows exactly what data it gets back. It doesn't explicitly contrast itself with deathcare_projection or deathcare_growth_ranking, though 'current-year' implicitly separates it from the projection tool.

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?

There is no when-to-use guidance and no mention of alternatives among the many siblings (deathcare_projection, deathcare_growth_ranking, deathcare_bulk_extract). The only usage-flavored remark is that the free preview masks county names, which is a limitation rather than selection 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?

With no annotations, the description carries the full behavioral burden. It usefully discloses the paid-tier requirement, which matters for auth/access, but says nothing about output shape, whether county/state/fips are alternatives or combinable, or how the fixed 2025-2040 window interacts with from_year/to_year.

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?

Three short, front-loaded fragments with no filler: purpose, trend context, access tier. Slightly telegraphic and the 'baby-boomer mortality trajectory' clause is more color than specification, but nothing is wasted.

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 5-parameter tool with zero required fields, no annotations, no output schema, and no parameter documentation anywhere, the description leaves too much unexplained — particularly geographic identifier selection, which is the core input decision.

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?

Five parameters at 0% schema description coverage, so the description must compensate and largely does not. It implies a year range via '2025-2040' but never explains fips vs. state vs. county selection or whether they are mutually exclusive.

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?

Specific verb+resource: 'Year-by-year expected deaths for a county, 2025-2040' tells an agent exactly what is returned and the time horizon. It does not, however, differentiate itself from siblings like deathcare_market or deathcare_growth_ranking, which occupy adjacent territory.

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?

No indication of when to choose this over deathcare_market, deathcare_growth_ranking, or deathcare_bulk_extract. The only guidance is the access tier ('PAID (Professional and above)'), which is an eligibility constraint rather than a usage condition.

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