Hiring Change Intelligence

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

With no annotations, the description carries the full burden and delivers genuinely useful behavioral facts: it is paid at $0.03 USDC per company via x402, is capped at 50 companies, and returns per-company counts. It does not disclose auth requirements, per-call timing, or how missing companies are handled, but pricing and batching limits are the highest-value disclosures here.

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 cost/payment terms front-loaded, immediately followed by what it returns and who it is for. No filler, though the trailing list of industries is longer than strictly 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?

No output schema and no annotations, so the description must cover everything; it does describe the return shape (per-company new-opening and closure counts) and cost model. However it omits the observation window for 'new openings and closures' and the acceptable company identifier format, leaving the agent able to call the tool but not fully confident in the 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?

The single parameter (companies) has 0% schema description coverage, so the description must compensate. It adds real meaning – the array holds companies, is billed per company, and is capped at 50 – but never specifies the expected identifier format (domain vs. name vs. ID), which is the one thing an agent most needs.

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 specific verb ('Track hiring') and resource ('per-company counts of new openings and closures') and signals batch scope ('across a whole portfolio or target-account list in one call'), which implicitly separates it from the single-target siblings. It stops short of naming any sibling explicitly, so routing still requires inference.

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 contextual scenarios – 'recruitment agency monitoring, VC portfolio talent tracking, sales prospecting signals, sector hiring trends' – plus an implicit batch cue ('whole portfolio... in one call'). There are no exclusions or explicit when-to-use-this-instead-of-hiring_landscape directions, so it is context-rich but not fully routing 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 does disclose the notable behavioral fact that the tool is paid ($0.05 USDC on Base via x402), which is real value. But it omits permission/auth needs, the detection window or comparison baseline, rate limits, and return shape—all material for a change-detection call.

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 dense sentences, no filler, with purpose and change types presented up front. Leading with the payment note is slightly awkward ordering but not wasted text.

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 one-parameter, no-output-schema tool this covers purpose, cost, and use cases adequately, but it leaves the agent without any guidance on the comparison period, output structure, or payment mechanics, which are the practical unknowns for actually invoking it.

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 description never explains the single `company` parameter—no format, identifier convention, or examples. The only faint signal is the generic 'X' in the use-case phrasing, which does not compensate for the undocumented input.

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+resource: detects hiring changes against history, with three concrete change types (new roles, removed roles, team/location shifts). The 'vs history' framing implicitly separates it from the point-in-time siblings like hiring_snapshot, but no sibling is named explicitly, 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?

Offers clear trigger scenarios ('did X just start/stop hiring', new postings, recruiting trends, downsizing alerts), which is strong context. However, it never says when NOT to use it or which sibling to pick for adjacent needs (e.g., snapshot vs landscape), so alternatives are left 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 behavioral burden, and it does disclose a key non-obvious trait: this is a paid call ($0.50 USDC on Base via x402). However, it says nothing about latency, error/refund behavior, or whether the same company can be re-queried cheaply, leaving meaningful gaps for a paid mutation-adjacent service.

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, front-loaded with the price and the value proposition, then the contents, then use cases. Every clause adds information; no filler or restatement of the name.

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 annotations, so the description must stand alone, and it does list report contents and the payment requirement. It omits return format, granularity (per-company vs per-team), and freshness of the data, which an agent would need to set expectations 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% for the single required 'company' parameter, so the description is the only source of parameter meaning, and it only implies the input is a company identifier. It doesn't specify whether a name, domain, or ticker is expected, which is a real ambiguity for a single-param tool.

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 specific deliverable (workforce-intelligence report) and enumerates its contents: hiring growth rate, scaling teams, geographic footprint, headcount estimate, executive takeaways. It doesn't explicitly differentiate from siblings like hiring_snapshot or hiring_landscape, so an agent must infer position in the family.

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 concrete use cases — gauging expansion, account research, competitive talent analysis, sourcing priorities — which tells an agent when this report is the right call. It stops short of naming exclusions or stating when a sibling (e.g., hiring_snapshot) would be preferred.

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 at all, the description carries the full behavioral burden and it does add non-obvious context: a $5 USDC charge on Base via x402 and a cap of up to 10 companies. Cost and quota limits are exactly the kind of trait an agent cannot get from the schema, though error behavior and what happens with fewer/more than 10 companies is unstated.

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 pricing/limit caveat is front-loaded in parentheses where an agent will see it first, and the remainder is a single dense but readable clause. No filler sentences, though the output enumeration is slightly packed.

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 2-parameter tool with no annotations and no output schema, the description covers purpose, usage, cost and quotas well, but it leaves the return payload's structure and the anchor/companies semantics only loosely implied. An agent can select it confidently but has to guess at invocation details.

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% on 2 parameters, so the description must compensate. It implicitly defines 'anchor' as the company ranked against peers and constrains 'companies' to up to 10 entries, which is useful, but it gives no format guidance (names vs. IDs vs. domains) for either parameter.

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 chain: it ranks an anchor company against peers by hiring volume/growth, surfaces the fastest-scaling competitor, and reports team/geography shifts. It is clearly not a raw dump like hiring_snapshot or a delta feed like hiring_changes, but it never names those siblings, so differentiation is left to inference.

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 explicit use contexts: 'competitive intelligence, market mapping, employer benchmarking.' That is clear guidance on when to reach for it, but there is no statement of when NOT to use it and no routing to the sibling tools, which is the main gap.

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 must carry the full behavioral burden. It discloses that the tool is FREE, names data sources (Greenhouse/Lever/Ashby), and lists return fields, but does not explicitly state read-only status, rate limits, pagination, or authentication requirements.

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 compact paragraph that is front-loaded with cost and purpose, then usage, sources, and input format. Every sentence adds value with no 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 one-parameter, no-output-schema read tool, it covers purpose, usage examples, data sources, returned fields, and input formatting. It is slightly incomplete on behavioral details like read-only guarantees and rate limits, but overall 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?

Schema coverage is 0%, so the description must compensate. It does so by explaining the exact accepted formats for the company parameter: 'gh:<h>, lever:<h>, ashby:<h> or bare company handle.' That is essential meaning not present in 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?

States a specific resource ('current job openings / who is hiring at one company right now') and enumerates returned fields (teams, locations, remote, employment type). Clear purpose, but it never differentiates itself from sibling tools like hiring_batch_scan or hiring_changes.

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?

Provides explicit usage contexts via example queries ('what jobs is X hiring for', open roles, talent demand, headcount signals). No when-not conditions or direct comparison to alternatives, so it falls short of a 5.

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