Seb Labs

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

With no annotations, the description carries the full behavioral burden and does so well: it discloses concurrency of up to four items, per-item success/error retention with input indices, retry behavior, rate limits, funded-token requirements, and per-item/question billing. This is exactly the kind of operational context an agent needs before invoking a metered batch tool.

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 front-loaded with the core operation and remains dense but purposeful throughout. Each sentence adds operational or usage-relevant information without redundant restatement.

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 the complex nested input schema, lack of output schema, and lack of annotations, the description is sufficiently complete. It explains result structure, error handling, concurrency, limits, auth/billing, and billing model so an agent can invoke and interpret the tool 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 description coverage is 100%, so the schema already documents the states and questions parameters in detail. The description reinforces the 1–100 state limit and 1–10 typed questions but adds little parameter-level meaning beyond what the schema already 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 clearly states a specific batch operation: classify, screen, triage, rank, or bulk-label up to 100 independent text states using the same typed questions. It is clear what the tool does, but it does not explicitly name or differentiate itself from sibling tools such as seb_decide or seb_route.

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 usage context, including batch size limits, independent-item semantics, error inspection, retry guidance, rate limits, and funding requirements. However, it does not explicitly say when to use a sibling tool instead of this batch version.

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 well: it discloses that results are typed answers with metered usage and no prose rationale, that a funded account token is required, and that shared state is metered separately per question. These are non-obvious operational facts an agent needs before invoking. It omits failure/error behavior and any rate-limit detail, keeping it at 4.

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 dense sentences, front-loaded with the use case before the mechanics, with no filler. It is packed rather than padded, though the use of the non-obvious term 'noul' alongside compressed clauses makes it slightly harder to parse on first read than an ideal definition.

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 nested-object tool with no output schema and no annotations, the description supplies the essentials: what each question type returns, the metering model, the token requirement, and the no-retrieval constraint. The remaining gaps — error handling, and how this differs from seb_batch_decide — are minor but real.

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, but the description adds genuine conceptual meaning the schema lacks: choice takes 2–20 option keys mapped to descriptions or null, noul returns a probability in [0,1], and score returns a 0-based expected ordinal over 2–5 ordered levels. Those return-shape semantics are especially valuable because there is no output schema. It does not explain the state parameter's role beyond the schema's own text.

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 capability — evidence/claim support checks, binary screening, rubric scoring — and enumerates the three question modes (choice, noul, score) it accepts. That is far more than a restatement of the name. It stops short of 5 because it never distinguishes itself from siblings like seb_batch_decide, leaving the agent to guess which entry point handles multi-state or batched work.

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 trigger conditions ('evidence/claim support checks, binary screening, rubric scoring') and maps each to a question type, so the agent knows which mode to pick for a given judgment. It also warns 'Include all needed facts; no browsing or retrieval,' which is a real usage constraint. No explicit when-not-to-use or sibling alternative is named, so it falls short of 5.

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 and closed-world annotations, the description adds important behavioral context: it returns labeled illustrative probabilities, never live inference, and requires no account token. It could say more about determinism or exact return shape, but for a simple demo this is strong 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?

The description is three short sentences, front-loads the use case, then clarifies output behavior and pricing/auth context. Every sentence adds useful information without repetition.

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 demo with annotations and no output schema, the description covers purpose, usage, auth needs, and the illustrative/non-live nature of results. The only minor gap is that the exact response shape is not described.

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%, so the description must carry parameter meaning. It does so by naming all four enum values and framing them as fixed examples of enterprise, finance, robotics, or handoff decisions, which gives useful semantic context beyond the bare enum.

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 that this is a free demo/connection check returning fixed illustrative probabilities for four named decision domains. It clearly distinguishes itself from live inference and paid tools, though it does not call out specific sibling tools by name.

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 cases: a free connection/interface check or a fixed example across enterprise, finance, robotics, and handoff decisions. It also states when not to use it for real evaluation by directing users to a paid tool for their own input, but it does not name which sibling paid tool to choose.

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 and does so well: entitlement errors, no fallback, paid input-token metering, and return limitations ('not investment advice, a forecast guarantee or an executed trade') are all disclosed. It also states that missing finance configuration produces an error, which is important failure behavior.

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 front-loaded with the entitlement gate and core purpose, followed by useful operational caveats. It is dense but appropriately sized; some of the general-model fallback warning overlaps with the earlier public-endpoint sentence, but the redundancy is minor.

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 complex nested-schema tool with no output schema, the description covers the key context an agent needs: entitlement prerequisites, error behavior, metering, non-advice status, and the general output nature as typed model estimates. It could say more about the returned estimate structure, but the schema already defines the question formats and the description provides the operational guardrails.

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 detailed state and questions schema already documents parameter semantics. The description mentions 'supplied point-in-time market context' and the choice/noul/score schema, but does not add syntax or usage details beyond what the schema provides; baseline 3 is appropriate.

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: judge financial language or supplied point-in-time market context using the choice/noul/score schema on an entitled dedicated Seb-9B-Fin backend. It clearly separates this specialist from the public general-model endpoint, but does not name or distinguish the sibling tools such as seb_decide or seb_route.

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 eligibility conditions ('only for entitled accounts with an enabled dedicated Seb-9B-Fin backend'), states that the public general-model endpoint does not enable the specialist, and warns never to assume general-model fallback. These are concrete when-to-use and when-not-to-use instructions.

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 well: it discloses the return shape (selected label plus per-option probabilities), the accuracy caveat ('Model-estimated probabilities can be wrong'), an auth requirement ('funded account token') and a cost model ('charges input tokens'). Not disclosed are rate limits, determinism, or failure behavior, keeping it short of a 5.

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?

Roughly four dense sentences: purpose and use cases first, then input expectations, then return value, then caveats and cost. Nothing is redundant and no sentence is 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?

Only two required parameters and no output schema, so the description must explain the return value — which it does explicitly. Input constraints, cost, and reliability caveats are all covered; an agent has everything needed to invoke it 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 description coverage is 100%, so both parameters are already fully documented in the schema, including the 2–20 / 80-character bounds that the description repeats. The only added semantic value is the advisory about including an unclear/escalate label. Baseline 3 is appropriate.

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 ('fast single-label classification or routing') and backs it with concrete application examples (ticket triage, intent labels, tool selection). An agent immediately knows what the tool produces. It never explicitly contrasts itself with the sibling seb_decide / seb_batch_decide, so the single-label vs. multi-criteria distinction must be inferred.

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?

Gives rich when-to-use context through enumerated scenarios and practical setup advice ('add an unclear/escalate label if appropriate'). It also scopes what the tool will NOT do ('No explanation or downstream action'). It does not name an alternative tool or state when to prefer seb_decide over this one.

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