Typesense Cloud

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

Annotations cover the safety profile (destructiveHint true, readOnlyHint false, idempotent false), but the description adds substantial context beyond them: lifecycle deletion 72 hours after creation unless claimed, rate limits (2/hour, 5/day), api_key scope (read/write/search but no delete or key management) and 24-hour validity, and hand-off rules for the claim_url. This is exactly the extra operational detail annotations cannot convey.

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 core action and the cluster specs, then flows into lifecycle and credential rules. It is dense but each clause carries needed operational meaning; a bulleted breakdown of lifecycle/limits would read slightly better, but there is no padding.

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 carries the return-value burden and does so fully: claim_url, access_token for polling, hostname and api_key when ready, otherwise status=provisioning and a next field. Combined with lifecycle and rate-limit rules, an agent has everything needed to call and follow up 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?

Only one optional parameter with 100% schema description coverage, so the schema already documents region and its default. The description's 'unless one is given' largely restates the schema default; baseline 3 applies since the schema does the heavy lifting.

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 with concrete specs (0.5 GB memory, 2 vCPU burst, one node, no account/credential), which cleanly separates it from the polling sibling get_claimable_cluster. An agent knows exactly what this tool produces and how it differs from the read-side 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?

The description gives operating context: use claim_url for the human, never open it yourself, and poll get_claimable_cluster every ~60 seconds until status is ready. It names the sibling and the polling cadence, though it does not state explicit preconditions for when creation is inappropriate beyond the rate limits.

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?

Beyond the readOnly/idempotent annotations, it discloses the auth mechanism (bearer token), a hard rate limit (120 calls/hour/cluster), the polling cadence (~60s), and the non-obvious behavior that hostname and api_key are re-returned on every call until the cluster is claimed. This is exactly the kind of context annotations cannot carry.

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 tight sentences lead with how to call it, then the return states, then auth, then limit. Nothing is padded and the most actionable detail (input provenance) is front-loaded.

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 fully covers return semantics by listing the four possible status values and what ready includes. Auth, limits, and polling behavior are all present, so an agent has everything needed to invoke and interpret 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 100%, so the baseline is 3, and the description adds provenance by stating that id comes from the create answer and that access_token is the bearer token from create_claimable_cluster. This is useful but largely restates what the schema descriptions already convey.

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 (get/status of a claimable cluster) and enumerates the exact states returned (provisioning, ready, claimed, expired). It also implicitly separates this polling tool from the write-side sibling create_claimable_cluster.

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 tells the agent the full workflow: pass the cluster_id from the create response as id, poll again in about 60 seconds while provisioning, and authenticate with the access_token from create_claimable_cluster. Both the when-to-call and the source of each parameter are explicit.

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