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

With no annotations, the description carries the full burden and aptly declares 'Read-only', telling the agent this is a safe, non-mutating operation. It also notes the data is a 'local' ledger. It doesn't specify edge cases (e.g., empty history) or return format, but for a simple read this is solid disclosure.

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, front-loaded with the core purpose, then usage guidance and a behavioral flag. Every sentence earns its place with zero 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?

Complete for a low-complexity tool (one optional param, no output schema). It covers purpose, use case, and read-only behavior. The only gap is that the return contents aren't described, but for this simple ledger-read it's mostly 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 description coverage is 100%, so the single optional parameter is already fully documented ('Filter to one employer; omit for the full history'). The description adds no parameter-level detail beyond this, which lands at the baseline 3.

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 ('Read the local application ledger') that clearly distinguishes it from siblings like search_roles (searching postings) and check_sponsor (visa status). The purpose is immediately actionable and 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?

Explicitly says when to use it ('Use it before applying to avoid duplicate submissions to the same employer, and to see what has already been tried'). It gives clear context, though it doesn't name sibling tools as explicit when-not-to-use alternatives.

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 provided, the description carries the full burden. It discloses that results include a confidence grade, that generic-word matches are unreliable, and that a 'none' result does not rule out sponsorship and should prompt checking the registered legal entity. This is substantial behavioral context beyond a basic lookup, though it does not cover aspects like rate limits or authentication.

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 sentences and immediately states the action, then adds caveats about confidence and interpretation. Every sentence adds value, and the most important information (the lookup) comes first. No 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?

For a tool with no output schema and no annotations, the description covers the essential aspects: what it checks, what result to expect (confidence grade), and how to interpret negative results. It does not specify the exact response format, but the description gives enough for an agent to decide whether to call the tool and how to interpret its output. Some detail on the confidence grade scale might be missing, but overall it is quite 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%, so both parameters (company and skilledWorkerOnly) are already documented in the schema. The description adds no additional parameter-level detail; it only implies that company names may collide but does not clarify formatting or validation beyond what the schema states.

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 action: 'Look up whether a company holds a UK Skilled Worker sponsor licence.' It names the resource (company) and the licence type, and the wording clearly distinguishes it from the unrelated siblings like screen_posting or application_history.

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 does not explicitly specify when to use this tool versus alternatives, nor does it state exclusions. The purpose implies the use case (checking sponsorship), but there is no direct guidance on when not to use it or what to use instead. However, the description does provide interpretive guidance for results, which indirectly helps the agent decide next steps.

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 provided, the description carries the full burden of behavioral disclosure. It does disclose what the tool evaluates (the explicit criteria list), which is genuinely useful context. But it never states whether the call is read-only, what it returns, or any side effects — for an analysis tool with zero annotation coverage these gaps are felt.

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 tight two sentences with no filler and front-loads the core purpose and criteria. The usage directive is placed at the end, which is acceptable given the value of the criteria list, though it could arguably lead with the 'run before applying' guidance for maximum salience.

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 two-parameter analysis tool with no output schema, the inputs are fully covered and the screening categories are spelled out. However, the description does not indicate what the result conveys — whether it returns a verdict, a risk score, or flagged bars — leaving an agent unsure what to expect from the call.

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%, so the schema already documents both title and description fully; this earns the baseline 3. The description adds marginal semantic value by signaling why the parameters matter (the analysis centers on the posting text), but does not add syntax or format detail beyond what the schema provides.

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 ('Screen') and resource ('a posting') with enumerated screening dimensions: hard eligibility bars (citizenship, residency, clearance, refusal to sponsor), hidden seniority, salary, and stack. This precise scope clearly differentiates it from siblings like check_sponsor, which covers only the sponsorship angle.

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?

'Run this before investing effort in an application' gives explicit timing and tells the agent to call it early in the workflow. However, it does not name alternatives or state exclusions — e.g., it never routes sponsorship-only cases to check_sponsor — so the guidance stops short of full alternative differentiation.

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 disclose behavioral traits. It does: it states the auth requirement (REED_API_KEY), explains the funnel output (showing what each filter removed so a small result set is understandable), and notes the exclusion logic. These go beyond the schema and help the agent anticipate how the tool behaves. It lacks details on error handling or rate limits, but core behaviors are covered.

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. The first sentence states the core purpose and exclusions; the second explains the funnel and the auth requirement. No wasted words. The information is front-loaded: an agent gets the gist immediately, then the important behavioral details. This is exemplary conciseness.

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 there is no output schema, the description explains the return format (a funnel) and the API key requirement. The parameters are fully documented elsewhere. The only minor gap is the precise structure of the funnel output, but that is not essential for calling the tool correctly. The description covers the key operational aspects an agent 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?

All five parameters are fully described in the input schema (coverage 100%), so the baseline is 3. The description does not add parameter-specific guidance beyond what the schema provides; it mentions the funnel generally but doesn't tie specific parameters to outcomes. Since the schema already handles semantics, there is no deficit requiring compensation.

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 opens with a specific verb-resource pair ('Search live UK job listings via Reed') and adds a key scoping detail: exclusions of recruitment agencies and job boards, with the reason they cannot sponsor. This distinguishes it from sibling tools (check_sponsor, screen_posting, application_history) whose purposes are plainly different. An agent can tell exactly what this tool does and for whom.

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 context: it searches live UK roles on Reed and excludes entities that can't sponsor, implying it is used to find roles that can sponsor visas. It also explains the funnel output, which helps an agent interpret results. However, it doesn't explicitly state when to prefer this over siblings or when not to use it, though the tool names make alternatives obvious. The use case is well implied, if not formally stated.

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