deepinfra

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

readOnlyHint=true already tells the agent this is a safe read, so the burden is lighter. The description adds the REST mapping (GET /v1/me) and the response contents, but says nothing about authentication requirements or response shape beyond the field list.

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?

One compact sentence with the REST endpoint appended; the resource and returned fields are front-loaded and nothing is wasted.

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 trivial read-only, zero-required-param tool with no output schema, the description covers identity, email, and quotas plus the endpoint, which is adequate. Minor gaps remain on auth requirements and whether quota values are per-account or per-key.

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% for the single 'checklist' boolean, so the schema already explains it. The description does not mention the parameter at all, meaning it adds no meaning beyond structured data — the baseline of 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 and resource ('Get the current DeepInfra account / user details') and enumerates the payload (identity, email, quotas), so the agent knows what it returns. It does not explicitly distinguish itself from lookalike siblings such as deepinfra_get_usage or deepinfra_get_rate_limit, whose data could overlap with 'quotas'.

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 when-to-use statement, no prerequisites, and no pointer to an alternative sibling for related data like usage or rate limits. Usage is only implied by the name and content list.

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, so the safety profile is covered. The description adds real value beyond that by naming the returned content scope ('config + current status') and the underlying REST call, which tells the agent what to expect from the read.

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 with the core action front-loaded; the REST mapping in the second sentence is useful context, though slightly redundant for an agent that already has the schema.

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 single-parameter read tool with annotations covering safety and no output schema, the description supplies what the call returns (config + status), so an agent has enough to invoke it correctly. Minor gaps remain around error/not-found behavior.

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 param is fully documented, so the baseline is 3. The description only restates the id in the REST path and adds nothing about format or constraints beyond the schema.

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 ('Get one dedicated deployment by id') and the 'one' framing implicitly distinguishes it from list_deployments. It does not explicitly name sibling tools like get_deployment_stats, so it stops 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?

Usage is only implied: 'by id' suggests fetching a single known deployment, in contrast to the list sibling. There is no explicit when-to-use vs when-to-use-alternative (e.g., stats vs config) guidance.

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?

readOnlyHint=true already tells the agent this is a safe read, so the bar is lower. The description adds the REST endpoint and confirms the resource is a dedicated deployment, but says nothing about response shape, sampling granularity, or retention beyond what annotations cover.

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 with the resource and metric families front-loaded; nothing is wasted. The REST endpoint hint is short and arguably helpful for cross-referencing the API docs.

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 stats tool with a fully documented schema and no output schema, the description covers what the tool returns at a high level (throughput/latency/replicas). The main uncovered item is how results differ from the live-metrics sibling.

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 deploy_id, from, and to (including the relative-expression syntax). The description adds no parameter semantics beyond the schema, which puts it 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 (Get) and resource (deployment stats) and enumerates the metric families returned (throughput / latency / replicas), which is more than a tautology. It does not, however, explicitly differentiate itself from the closely related sibling deepinfra_get_live_metrics, which an agent could easily confuse with this 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 no when-to-use instruction and names no alternatives. The 'time-series' phrasing weakly implies historical/aggregate queries, but with deepinfra_get_live_metrics in the sibling set the lack of explicit routing guidance is a real gap.

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, so the safety profile is covered for this pure read. The description adds useful context by mapping the tool to the underlying REST endpoint (GET /deploy/llm/gpu_availability) and noting that results reflect current provisionability. It does not disclose auth requirements, how dynamic/volatile the answer is, or the response shape.

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 clauses with zero waste: the purpose and the mechanism are front-loaded, and the endpoint reference is a single trailing clause. Nothing needs trimming.

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 should hint at what comes back (e.g. a list of GPU types with availability), but only says 'which hardware can currently be provisioned'. Combined with the absent usage guidance against deepinfra_get_hardware, the definition is serviceable but leaves gaps for a read tool with two undocumented-augmenting filters.

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% (both filters documented: 'Filter by source' and 'Filter by base model'), so the schema carries the parameter burden. The description adds nothing about what 'source' means or what values base_model accepts, which is the baseline 3 case when 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 ('Get GPU availability for LLM deployments') and clarifies the meaning of availability with '(which hardware can currently be provisioned)'. It does not explicitly differentiate from the sibling deepinfra_get_hardware, so the agent must infer the distinction between a static hardware catalog and live provisioning availability.

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 when-to-use guidance, no prerequisites, and no alternative named. The closest sibling, deepinfra_get_hardware, is not mentioned, and the agent is not told that this is the pre-flight check before deepinfra_start_deployment. The parenthetical implies the purpose but never states the condition for calling it.

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?

The readOnlyHint=true annotation already establishes this as a safe read. The description adds only the REST endpoint (GET /v2/hardware), with no disclosure of return shape, whether hardware lists are static or availability-dependent, or error behavior. This is modest added value on top of 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?

Two compact sentences with zero waste; the purpose is front-loaded and the REST mapping is a useful secondary detail.

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 single-parameter read-only tool with full schema coverage and annotations, the description is nearly sufficient. The remaining gap is the lack of differentiation from deepinfra_get_gpu_availability.

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% and there is only one parameter ('model'), which the schema fully documents. The description adds no format or naming conventions beyond 'model name', so the baseline of 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 and resource ('Get the hardware options / GPU configuration available for a given model'), which is clear. However, it does not differentiate from the sibling deepinfra_get_gpu_availability, which an agent could easily confuse with this 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?

No when-to-use guidance, no prerequisites, and no mention of alternatives such as deepinfra_get_gpu_availability or deepinfra_get_model. The agent must infer the context entirely.

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?

readOnlyHint=true already tells the agent this is a safe, non-mutating read, so the description's scope note ('global', 'real-time') is modest added context plus the REST path. It does not cover refresh cadence, staleness, or what the metrics object contains, leaving behavioral gaps relative to the annotation baseline.

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: scope first, endpoint second. Every clause earns its place and nothing is padded.

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 parameterless, read-only, no-output-schema tool the description covers the essentials: what is returned conceptually and its real-time nature. Only the shape/cadence of the returned metrics is unaddressed, a minor gap given the tool's simplicity.

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 takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 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 and resource ('Get global live inference metrics across the account') and clarifies the payload as real-time throughput/activity. It is distinguishable from account-scoped siblings like get_usage or get_deployment_stats, though it never names an alternative to sharpen the contrast.

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?

No indication of when to reach for this tool versus deepinfra_get_usage, deepinfra_get_deployment_stats, or get_rate_limit, and no preconditions or exclusions. The agent must infer usage from the word 'live' alone.

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, so the safety profile is covered by structured data. The description adds that the return is a catalog entry (pricing, context length, capabilities), which usefully discloses return content, but does not cover error behavior (e.g., unknown model) or whether the version parameter affects the response. A 3 is appropriate given annotations carry the safety signal.

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 with no waste; the purpose is front-loaded and the REST endpoint detail is appended compactly. Slightly more than strictly necessary, but efficiently structured.

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 a simple read-only tool with two fully documented parameters and an annotation-set safety profile, the description is nearly complete. The main missing element is what a not-found or invalid-model response looks like, and there is no output schema to fall back on.

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 fully documented in the schema, including the slash-handling note for model_name. The description adds no syntax or format details beyond what the schema provides. Baseline 3 is correct when 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 ('Get') and resource ('one model's catalog entry') and enumerates what the entry contains (pricing, context length, capabilities). This clearly distinguishes it from the sibling deepinfra_list_models, which would return many models rather than one.

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 phrase 'one model's catalog entry' implies the singular-fetch use case and contrasts implicitly with the list sibling. However, it does not explicitly say 'use deepinfra_list_models to enumerate all models' or state when not to use this tool, so it falls short of the top-tier routing guidance.

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?

readOnlyHint=true already tells the agent this is a safe read, so the bar is lower. The description adds the concrete REST mapping (GET /v1/me/rate_limit), which is useful provenance, but says nothing about response shape, refresh cadence, or whether limits are user- vs account-level.

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, zero waste: the purpose and scope lead, and the REST reference is appended as supporting detail. Nothing could be cut without losing 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 no-parameter, read-only lookup with no output schema, the description supplies enough to invoke it correctly and understand roughly what comes back. Only the absence of any when-to-use context keeps it from being fully 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?

The tool takes zero parameters and the schema is 100% covered, so there are no parameter semantics to explain; baseline 4 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?

Names a specific verb and resource ('Get the account's current rate limits') and even enumerates the scope (per-model / per-endpoint request and token limits). It is clearly distinguishable from siblings like get_usage or get_account by resource alone, though it does not explicitly name or contrast any alternative.

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 states only what the tool does; it never says when to reach for it (e.g. before issuing requests, or when a 429 is hit) and names no alternative tool. Usage can only be inferred from the resource name.

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?

readOnlyHint=true already tells the agent this is a safe read, so the bar is lower. The description adds only the REST endpoint mapping (GET /payment/usage); it does not describe return shape, aggregation granularity, or auth requirements beyond the annotation.

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, zero filler, with the core purpose front-loaded ahead of the REST endpoint note. Nothing needs trimming.

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?

Functional for a read-only billing lookup, but with no output schema the description could say what the usage figures cover (aggregated spend vs. breakdown) and how it differs from usage_rent/usage_tokens. Those gaps leave the agent to infer.

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 `from` (with its rich format options) and `to` are fully documented in the schema. The description adds no parameter detail beyond that, 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 clear verb+resource+scope: get spend/usage for a billing period. However, it does not distinguish itself from the closely-named siblings deepinfra_get_usage_rent and deepinfra_get_usage_tokens, leaving ambiguity about which usage view this returns.

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?

No when-to-use guidance and no exclusions. With three usage-related siblings (usage, usage_rent, usage_tokens) the description gives the agent no basis for choosing among them.

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, so the safety profile is covered. The description adds the REST endpoint mapping (GET /payment/usage/rent), which is mild context, but says nothing about what the usage data contains, aggregation, or pagination.

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 with the purpose front-loaded and no filler. The REST endpoint reference is slightly redundant metadata but does not bloat the 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 simple two-parameter read tool with covered annotations and a fully documented schema, this is adequate. Without an output schema, however, it omits any indication of what the returned usage data represents, which would help an agent interpret results.

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 'from' and 'to' are fully documented with unix-timestamp semantics and the default-to-now behavior. The description adds no parameter detail, so the baseline of 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 and resource ('Get GPU rental (dedicated hardware) usage'), and the parenthetical clarifies the resource beyond the ambiguous name. It is distinguishable from siblings like deepinfra_get_usage_tokens, though it never explicitly contrasts with the plain deepinfra_get_usage.

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 guidance on when to choose this tool over deepinfra_get_usage or deepinfra_get_usage_tokens, nor any prerequisites. Only the implied 'for a time range' hints at context, leaving the agent to infer selection from the name.

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?

The readOnlyHint annotation already establishes this as a safe read, so the description's bar is lower. It adds the REST endpoint mapping (GET /payment/usage/tokens), which is useful provenance, but says nothing about rate limits, auth scope, or the shape of the returned usage data.

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 the purpose front-loaded and the endpoint reference appended. Nothing is wasted, though the REST endpoint line is developer trivia rather than agent-facing guidance.

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 two-parameter read tool with full schema documentation and a readOnlyHint, this is close to adequate. However, there is no output schema, and the description does not describe what 'per-model token usage' returns, leaving the caller to guess at the response structure.

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 'from' and 'to' are fully documented with format details ('YYYY.MM', 'current', 'current(-N)', unix seconds) and the default for 'to'. The description adds nothing beyond 'for a period', so the baseline of 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 and resource ('Get per-model token usage') plus the scope ('for a period'), which reads as distinct from generic siblings like deepinfra_get_usage and deepinfra_get_usage_rent. It stops short of explicitly naming those siblings, so differentiation is by inference rather than declaration.

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 guidance about when to choose this over deepinfra_get_usage or deepinfra_get_usage_rent, both of which live in the same account/billing space. Usage is only implied by the name and the 'per-model token' phrasing.

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, so safety is covered, but the description adds genuinely useful behavioral context beyond them: the response is metadata only and explicitly excludes secret values, and it names the underlying REST call (GET /v1/api-tokens). It stops short of describing pagination or the shape of the metadata.

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-loaded with the action and the key caveat about secret values, followed by the endpoint. 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?

There is no output schema, so the description carries some burden for return values; 'metadata only, not secret values' handles the most important agent concern. It could go slightly further by naming the metadata fields returned, but for a zero-param list tool this is nearly 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?

The tool takes zero parameters, which is the baseline-4 case; there is no parameter semantics to clarify. The description correctly implies no filtering or input is needed.

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 ('List the account's API tokens') and immediately scopes the result ('metadata only, not secret values'). It is trivially distinguishable from siblings like deepinfra_list_models, deepinfra_list_deployments, and deepinfra_list_invoices.

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 by the name and the zero-parameter shape: there is nothing to configure, so calling it to enumerate tokens is self-evident. However, the description offers no explicit when-to-use framing or reference to a sibling alternative (e.g. how it relates to deepinfra_get_account).

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?

readOnlyHint=true already tells the agent this is a safe read, so the bar is lowered. The description adds the REST endpoint mapping and implies the listing is scoped to the whole account, but says nothing about pagination, result size, or auth requirements.

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, purpose front-loaded, no filler. The REST endpoint note is arguably redundant but is compact and aids mapping to the underlying API.

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 list with full schema coverage and annotations, this is minimally adequate. With no output schema, the description could say what a deployment record contains or whether the list is paginated, and it does not.

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 optional 'status' filter is already documented in the schema with a concrete example. The description adds no syntax or default behavior beyond the schema, 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?

Clearly states a specific verb and resource: 'List the account's dedicated deployments.' The scoping word 'account's' plus 'dedicated deployments' distinguishes it from deepinfra_list_models and deepinfra_get_deployment without naming either sibling explicitly.

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?

No guidance on when to use this versus deepinfra_get_deployment (single) or the other list tools. The only usage hint, filtering by status, lives in the schema, not the description.

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, so the safety profile is covered. The description adds only the REST endpoint, which is cosmetic; it says nothing about pagination semantics, ordering of results, or volume — the one behavior (cursor pagination via starting_after) an agent would care about is left entirely to the schema.

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-loaded with the purpose and zero filler. Nothing is redundant or buried.

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, zero-required-parameter list tool with full schema coverage and annotations carrying the safety hint, the description is minimally sufficient. However, without an output schema it could have said something about return shape or pagination behavior, leaving a small 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 description coverage is 100%, so all three parameters (limit, invoice_type, starting_after) are already documented in the schema. The description adds no extra meaning such as valid invoice_type values or default/max limits, 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?

States a specific verb and resource ('List billing invoices for the account') and even maps it to the REST endpoint GET /payment/invoices. It is clearly distinguishable from sibling readers like get_usage or list_api_tokens, though it never names or contrasts a specific sibling.

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 versus alternatives such as deepinfra_get_usage or deepinfra_get_usage_rent, and no mention of prerequisites or context. The agent must infer usage from the resource name alone.

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?

readOnlyHint=true already declares the safe read nature, so the bar is lower, but the description only adds the REST endpoint mapping (GET /models/list). It says nothing about catalog size, pagination, caching, or response shape beyond 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?

Two short, front-loaded sentences with no filler: the purpose first, the endpoint mapping second. Nothing superfluous.

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 list tool this is nearly sufficient. Since no output schema exists, a brief note on what the response contains (e.g. model IDs and metadata) would fully close the 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?

The tool takes zero parameters, so there is nothing to disambiguate and the baseline of 4 applies. The description correctly implies an unfiltered full-catalog listing with no filtering inputs.

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 (List) and resource (DeepInfra model catalog) with scope made explicit as 'all available models'. This clearly separates it from the sibling deepinfra_get_model, which retrieves a single model.

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 only implied by 'all available models' — an agent can infer this is the enumeration tool and get_model is the single-fetch tool, but the description never states when to use this versus alternatives or what it cannot do.

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?

The annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds the REST endpoint and the time-window scoping context, but does not disclose auth requirements, rate limits, pagination behavior, or return format. With annotations covering the safety profile, this is an adequate but not rich behavioral 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 sentences with zero waste; the core purpose and scope are front-loaded before the REST endpoint reference. Every sentence earns its place.

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 log query tool with full schema descriptions and no output schema, the description covers the essential purpose and scope. It omits details about return format or pagination, but given the schema's completeness and the absence of an output schema, these gaps are minor.

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 all four parameters (to, from, limit, deploy_id) are fully documented in the schema itself. The description adds no parameter-level meaning beyond what the schema already provides, so the baseline score of 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 ('Query'), resource ('inference request logs'), and scope ('for a dedicated deployment over a time window'). This clearly differentiates it from sibling tools like get_live_metrics or get_deployment_stats, which retrieve different data types.

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 when to use the tool (to query logs for a deployment within a time window) but provides no explicit when-not conditions or alternative tools. There are many sibling tools, and no routing guidance is given.

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 flag destructiveHint=true; the description goes beyond that by disclosing that the operation resumes an existing deployment and may accrue GPU charges while running, which is real cost/side-effect context. It does not cover auth requirements, reversibility, or what the call returns, leaving a few behavioral gaps.

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 short clauses: action, warning, and raw REST mapping. The action is front-loaded, the warning is prominent, and nothing is padded or redundant.

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 single-parameter mutation with no output schema, the definition covers purpose, cost implications, and the underlying endpoint, which is sufficient to call it correctly. The only real omission is any indication of the response shape or how to confirm success, which is minor given the tool's simplicity.

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 parameter, and schema description coverage is 100%, so the schema already fully documents deploy_id. The description adds nothing beyond implicitly showing the id in the REST path; the baseline of 3 for a schema-saturated single parameter 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 plus resource ('Start (resume) a dedicated deployment') and the parenthetical disambiguates start from a fresh creation. It does not name the contrasting sibling (deepinfra_stop_deployment) explicitly, so differentiation relies on the agent inferring from the name, but the purpose itself is 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?

Gives a clear context for use — resuming a deployment that is currently stopped — and adds a cost warning that is directly relevant to deciding whether to call it. It stops short of naming when NOT to use it or pointing at deepinfra_stop_deployment as the inverse, so no explicit alternatives are offered.

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 declare destructiveHint=true; the description adds real behavioral context beyond that: inference halts, GPU billing stops accruing, and the parenthetical "(pause)" signals the operation is a reversible suspension rather than a deletion. It still doesn't say whether the call is idempotent on an already-stopped deployment or what happens to queued work.

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 short, front-loaded sentences: purpose first, consequence second, transport detail last. Nothing is padding and the most decision-relevant information (billing stops) appears immediately.

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 single-parameter, non-returning mutation with destructiveHint already set, the description covers purpose, effect, and endpoint. No output schema exists, so return values need not be explained; only idempotency/state-after-stop is a 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 description coverage is 100% and the single deploy_id parameter is already documented as required with a meaning-carrying description. The description adds no syntax, format, or sourcing guidance beyond the schema, 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 and resource ("Stop (pause) a running dedicated deployment") and immediately scopes what that means. It is clearly distinguishable from sibling tools like deepinfra_start_deployment and deepinfra_get_deployment without opening any schema.

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 by the effect (halting inference and stopping GPU charges), which tells the agent why one would call it, but it never names the alternative (deepinfra_start_deployment) or states preconditions/exclusions. Adequate but the agent must infer the lifecycle pairing itself.

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