Spam I Am: carrier spam label check

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

Annotations only declare readOnlyHint=true and openWorldHint=false, while the description adds substantial behavior: the four status values, that a keyless request returns api_key only once, and that wait_seconds can wait up to 25 seconds for a decision. It does not describe error handling beyond the status enum, but it adds meaningful context beyond 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 compact and front-loaded with the core status behavior, followed by operationally important notes about api_key and wait_seconds. Every sentence adds useful information 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 simple read-only status check with no output schema, the description covers the statuses, waiting behavior, and one-time key retrieval. It leaves approval_id/token acquisition and error cases implicit, but annotations and schema cover enough 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 50%: token and api_key are documented in the schema, while approval_id and wait_seconds have no schema descriptions. The description clarifies wait_seconds and the api_key handoff, but it leaves the required approval_id and token usage largely to the schema/name, so compensation is partial.

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?

Describes the exact resource (an approval request) and scope (its status), and enumerates the possible statuses, so an agent can tell this is a status-check tool. It does not explicitly contrast itself with the sibling request_approval tool, 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?

The description implies usage after an approval request through the phrase 'Once approved' and explains how to use the returned api_key and wait_seconds. However, it does not explicitly state when to use this tool versus request_approval or when polling/waiting is inappropriate.

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 and openWorldHint=false, so safety is covered structurally. The description adds useful context by enumerating the returned components (balance, holds, spending limit) and the auth prerequisite, but says nothing about failure behavior when no key is available.

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 short sentences, front-loading the prerequisite before the payload description, with no filler. The terseness of 'Needs a key' costs a little clarity but not length.

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?

With no output schema, the description does the important work of naming the three returned components, which is genuinely helpful. However, for a read tool with an optional-auth path it omits what happens if no key resolves and gives no sense of response shape or units.

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% and the single parameter already documents the sia_... format and the get_approval fallback. The description's 'this key's spending limit' adds a small amount of meaning by tying the key to the scoped limit, but otherwise defers to the schema, so baseline 3 applies.

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 phrase 'The account balance, holds, and this key's spending limit' clearly states what the tool returns, and 'Balance' in the name/title matches. It does not differentiate from siblings like get_check or get_coverage, so it stays at 4 rather than 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?

'Needs a key' is the only usage cue and is ambiguous against the schema, which marks api_key as optional with a fallback to the connection's own key. There is no guidance on when to call this versus get_check, get_coverage, or get_approval.

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 and openWorldHint=false, so safety is covered; the description adds genuinely new behavior — that results can come back as waiting/complete/incomplete and that wait_seconds (max 25) blocks for results. It still doesn't say whether results are stable, cacheable, or how costly polling is.

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 no filler. It is front-loaded with return contents and closes with the actionable wait_seconds hint, though leading with the tool's action rather than its output would be marginally stronger.

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?

With no output schema and 4 parameters, the description carries the return-value burden well by naming each result field and its possible states, plus the wait behavior. The only notable gaps are check_id semantics and what to do on an 'incomplete'/'waiting' result.

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 50%: token and api_key are documented in-schema, the description covers wait_seconds including its 25-second cap and its blocking purpose. check_id, however, is undocumented in both places, so the description only partially compensates for the coverage gap.

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 enumerates exactly what a call returns (status, label, caller name, flagged, screenshot link), which makes the retrieval behavior clear even though the opening phrase is a noun phrase rather than an explicit verb+resource. It does not name a sibling tool to differentiate from, e.g. get_coverage, but the returned fields are distinctive enough for selection.

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 implies the step after start_check by saying the token comes 'from start_check,' and it tells the agent to set wait_seconds to wait for results. However, it never states when this tool should be preferred over get_coverage or what to do if the status is 'waiting' (retry? call again?), leaving timing guidance partially inferred.

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 and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context by saying carriers are 'online now' and prices are in cents, but it does not mention freshness guarantees, rate limits, or response structure.

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 short fragment with no wasted words. Each listed clause—carrier availability, price unit, dialing window—adds relevant 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?

For a zero-parameter read-only tool with no output schema, the description adequately identifies the main returned fields. It could be more complete by clarifying scope or return format, but it covers the essentials for this complexity level.

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 tool has zero parameters, so the parameter semantics baseline is 4. The description does not need to explain any inputs, and none are present.

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 lists the data points returned (online carriers, prices in cents, dialing window) but is a sentence fragment without an explicit verb or scope statement. The tool name and title help, but the description alone leaves the exact purpose slightly 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?

There is no guidance on when to use this tool, what prerequisites exist, or how it relates to siblings like get_check or start_check. The description only implies usage through its content, without any explicit context.

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 only say it is not read-only, is open-world and not destructive; the description adds the important behavior that this is a human-in-the-loop handoff producing an approve_url that must be delivered out-of-band, plus the follow-up polling call. It does not disclose link expiry, denial handling, or what happens if the human never opens it, so some operational behavior remains implicit.

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 sentences, no filler, with the produced artifact (approve_url) and the follow-up call up front. The first sentence is a long colon-chained clause, and the usage triggers sit at the end rather than being front-loaded, which costs a bit of scannability.

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 four-parameter, zero-required, no-output-schema tool, the description covers the outcome artifact, the required human interaction, the sequencing to get_approval, and the precise error conditions that should route here. Nothing essential for correct invocation is missing, and return values are out of scope since no output schema exists.

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 schema already explains number, reason, api_key and amount_cents, including formats and bounds. The description adds only marginal meaning — that the human sets the actual spending limit while amount_cents is the ask — and repeats the api_key-from-get_approval hint already in the schema. Baseline 3 applies.

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 concrete action and artifact: obtain an approve_url link that a human opens to sign in, fund, set a spending limit and approve. That is clearly distinct from siblings like get_approval (which it explicitly hands off to) and start_check. It stops short of naming the sibling set directly, so it is strong but not fully disambiguating on its own.

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?

Explicit trigger conditions are given: 'Use this when a check is refused with free_check_used, insufficient_funds or key_limit.' The description also prescribes the sequence — show the link to the human, then call get_approval — so both when-to-use and what-to-do-next are unambiguous.

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 disclose non-read-only, open-world, non-idempotent behavior; the description adds crucial operational detail: carrier test lines must be dialed within a window, a token must be retained for keyless checks, and billing changes after the first free check. This is substantive context beyond structured fields and does not contradict 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?

Four short sentences: purpose first, then returns, required follow-up, and billing. No filler; every sentence carries operational 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?

With no output schema, the description usefully explains the immediate return values (dial lines, check id, token) and external workflow. It still omits how to retrieve the finished result (e.g., via get_check) and the length of the call window, leaving a modest completeness 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?

All four parameters are fully documented in the schema, so the baseline is 3. The description adds little parameter-specific detail beyond workflow context (the number must call test lines; token must be kept), and does not explain carrier selection or webhook behavior 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?

The description states a specific verb ('Start') and resource ('check') and scopes it to the three carriers, making clear it initiates a new check rather than retrieving one. It does not explicitly name sibling alternatives like get_check, so it falls short of full sibling 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?

It explains the required external follow-up (the checked number must call each test line within the window) and the payment prerequisite (first check free; later checks need a key with funds). It does not explicitly contrast with get_check/get_coverage 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.