AI Deals Sentinel

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

With no annotations, the description carries the full burden and does so well: one read-only GET per API, 10s timeout promoted to a 45s re-probe for SLOW, and no answer within 45s for DOWN. It also warns that the measurement is a cached 'last measured' snapshot via generated_at, which an agent needs to interpret staleness.

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?

Front-loaded with the verb+resource, then dense semantics for SLOW/DOWN and the counts.measured caveat. Every sentence carries information, though the count-denominator note is a slight detour from what the tool itself does.

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 or annotations exist, and the description compensates by defining the status enum-like values and the measured/total count distinction. It does not sketch the overall response shape (e.g. per-API entries, nested counts), which is the only remaining 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 coverage is 100% and the single api parameter is already documented in the schema ('substring match on API name; omit for all'). The description adds nothing about the parameter, so the 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?

States a specific resource and scope: 'the last measured liveness of free, no-key public data APIs: HTTP status and latency.' An agent immediately knows what returns and from where, and the sibling tools (find_free_inference, get_offer) are clearly unrelated operations.

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?

Usage is implied (check current health/liveness before relying on an API) but never stated explicitly, and no when-not-to-use or alternative is named. Since the siblings are unrelated tools there is little to route between, so 3 is defensible rather than 2.

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, and it does disclose useful result behavior: every result carries source_url, verified_at, and a recency delta so the caller can apply a freshness tolerance. It omits pagination, result limits, and any rate/auth behavior, so it is not fully complete, but it adds real behavioral value.

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 core purpose and then the freshness metadata. No filler, no repetition of the schema, and the most decision-relevant fact (results are verifiable and time-stamped) comes second.

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 discovery tool with no annotations and no output schema, the description covers purpose and result shape adequately. It would be fully complete with a note on result volume/pagination or how it relates to get_offer, but the freshness disclosure covers the main risk an agent faces here.

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 provider, capability, and agent_usable_only, including the default. The description adds no parameter-level meaning beyond that, which is the baseline expectation when the schema does the work.

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 and resource ('find free tiers, trial credits and discounts on AI APIs'), so the agent knows exactly what the tool returns. It doesn't differentiate itself from the sibling get_offer, which is the only gap keeping it from 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?

Usage is implied ('an agent can use') but there is no explicit when-to-use, when-not-to-use, or comparison against the siblings check_api_health and get_offer. The agent must infer that this is the discovery tool and get_offer is the detail tool.

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 no-match fallback ('returns nearest candidates rather than an empty result') and the returned field set, but says nothing about auth requirements, rate limits, or explicitly confirming a read-only nature.

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 core purpose and followed by a genuinely useful edge-case note. Little waste, though tightly terse in places.

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 one-parameter lookup with no output schema or annotations, the description covers what is returned and how no-match behaves. Adequate, though usage routing and richer behavioral cues remain absent.

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 single 'provider' parameter is fully documented in the schema. The description adds no extra meaning (e.g., format, ambiguity handling) beyond what the schema provides, so the 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?

States a specific verb+resource: retrieving 'full detail for one provider's offer,' which is clearly a single-record lookup rather than a search. It does not, however, name or differentiate against sibling find_free_inference, which performs a similar discovery function.

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 no explicit when-to-use, when-not, or alternative guidance. Missing an opportunity to route the agent between this and find_free_inference for bulk/exploratory lookups.

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