DeepInfra MCP by usefulapi

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

readOnlyHint=true already establishes the safety profile, so the description's added value is modest: it discloses the returned fields (identity, email, quotas) and the underlying REST endpoint. It omits anything about auth requirements, rate limits, or how the optional checklist flag changes the response.

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 declarative sentence with the return contents front-loaded, followed by a short REST reference. Efficient and waste-free, though the endpoint line is slightly redundant for an agent that only calls the MCP tool.

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-required-parameter, read-only tool with no output schema, the description covers what the agent needs to know it will get account identity and quota data. The only gap is that it does not say how the optional checklist parameter alters the returned payload.

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 optional 'checklist' parameter is fully documented in the schema, so the baseline is 3. The description adds nothing about that flag, though it does hint at response content (quotas) that partially overlaps.

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') plus resource ('DeepInfra account / user details') and enumerates the payload (identity, email, quotas), which cleanly separates it from siblings like deepinfra_get_usage and deepinfra_get_rate_limit. The REST mapping (GET /v1/me) further pins down the purpose.

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: an agent can infer this is the tool for identity/account metadata, but the description never states when to prefer it over get_usage, get_rate_limit, or get_usage_tokens, nor does it note that no arguments are required. No explicit when/when-not 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?

Annotations already declare readOnlyHint=true, so safety is covered. The description adds that the return includes config plus current status, which is useful, but says nothing about auth requirements, rate limits, or behavior for a missing/stopped deployment.

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 purpose front-loaded and the REST endpoint appended as a compact traceability aid. 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 single-parameter read tool with no output schema, saying it returns config plus current status is close to sufficient. Minor gaps remain around error/not-found behavior, but nothing essential to invoking it correctly is missing.

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 there is only one parameter, so the schema fully documents deploy_id. The description adds only 'by id', which is already implied; 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 (Get) and resource (dedicated deployment) with clear scope (one, by id). It does not name or contrast with siblings like deepinfra_get_deployment_stats or deepinfra_list_deployments, so an agent must infer the distinction.

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: 'one ... by id' suggests fetching a single known deployment rather than listing. There is no explicit when-to-use, when-not, or pointer to an alternative such as list_deployments or get_deployment_stats.

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 that the data is time-series covering throughput/latency/replicas, which is useful, but says nothing about return shape, granularity, or whether an empty range returns an error — reasonable but not rich.

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 front-loaded sentences with zero padding: the metric content and scope come first, the endpoint mapping second. Nothing is redundant or misplaced.

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 partially compensates by naming the three metric families returned. It still leaves time-series granularity and response structure unstated, but for a simple read-only stats tool with fully documented params this is nearly sufficient.

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%: deploy_id, from and to are all documented in the schema with format hints (unix timestamp or relative expression, units s/m/h/d/w). The description adds no parameter detail beyond the REST path naming deploy_id, 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 ('Get') and resource ('time-series stats') and enumerates the metrics returned (throughput / latency / replicas), scoped to a dedicated deployment. It does not explicitly contrast itself with the sibling deepinfra_get_live_metrics, but the 'time-series' qualifier implicitly separates it from a live-snapshot 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 phrase 'for a dedicated deployment' implies the applicable context, and the REST endpoint reference confirms the underlying operation. However, there is no explicit when-to-use statement, no exclusions, and no naming of the alternative (e.g. deepinfra_get_live_metrics) that an agent should pick for real-time data.

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 that this reflects currently provisionable hardware (a point-in-time snapshot implied), but discloses nothing about freshness, caching, or 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 tight sentences with the core purpose front-loaded; the REST endpoint reference is compact and useful for debugging. No filler, though the endpoint detail is marginally redundant for an agent.

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, zero-required-parameter read tool with full schema coverage and no output schema to explain, the description covers what is needed to invoke it. Only the when-to-use context is missing.

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 both filters (source, base_model) are documented in the schema. The description adds no syntax, allowed values, or interaction detail beyond the schema, 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 availability') and clarifies the meaning of availability with '(which hardware can currently be provisioned)', which an agent can act on. It does not, however, distinguish itself from the sibling deepinfra_get_hardware, so the agent must infer the boundary.

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 says what the tool returns but never when to call it versus alternatives such as deepinfra_get_hardware or before deepinfra_start_deployment. No prerequisites, no exclusions, no 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?

The readOnlyHint annotation already covers the safety profile, so the bar is lower. The description adds the underlying REST mapping (GET /v2/hardware), which is useful context, but says nothing about error behavior (e.g., unknown model), what the returned configuration includes, or any constraints.

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, front-loading the resource and scope before the REST endpoint detail. 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 simple read-only single-parameter tool, this is close to sufficient, and the absence of an output schema means return values need not be explained. However, leaving the overlap with deepinfra_get_gpu_availability unresolved is a real selection risk.

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?

There is a single required parameter fully documented in the schema (100% coverage), so the schema does the heavy lifting. The description's 'for a given model' merely restates the parameter's meaning without adding format, naming, or source details.

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: retrieving the hardware options/GPU configuration for a given model. The purpose is clear on its own, but it does not differentiate itself from the sibling deepinfra_get_gpu_availability, which sounds like the same concept, so an agent cannot confidently choose between them from the text alone.

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 tool versus alternatives is given. The most relevant alternative, deepinfra_get_gpu_availability, is never mentioned, even though the two overlap significantly and the agent has no way to know which one answers 'what GPUs are available/free'.

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 is a safe read, so the bar is lower. The description adds that the metrics are account-wide, real-time, and backed by GET /v1/metrics/live, which is modestly useful context. It does not disclose freshness/refresh cadence, aggregation window, or rate limits.

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 filler, and the core purpose is front-loaded before the REST endpoint detail. The endpoint line is marginal for an agent but not wasteful.

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 the burden of explaining what is returned, and it only gestures at 'throughput / activity' without naming fields or shape. For a zero-parameter read tool this is adequate but leaves a real gap in what the agent can expect back.

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; the baseline of 4 applies. No parameter-related claims are made or 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 with scope qualifiers: 'global live inference metrics across the account (real-time throughput / activity)'. The words 'global' and 'live' implicitly separate it from per-deployment siblings like deepinfra_get_deployment_stats and historical ones like deepinfra_get_usage, though no sibling is named 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 when-to-use or when-not-to-use guidance is given. An agent must infer from 'live' and 'global' that this is for real-time monitoring rather than historical usage, but the description never names the alternatives (deepinfra_get_usage, deepinfra_get_deployment_stats) or the conditions that select between 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 safety is covered, and the description adds useful context beyond them: the underlying REST endpoint (GET /models/{model_name}) and the fact that the return is a catalog entry containing pricing, context length, and capabilities. That effectively compensates for the absent output schema, though error behavior and version-resolution semantics are not discussed.

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 filler; the purpose and returned fields are front-loaded, and the REST endpoint is a useful secondary detail rather than 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?

For a simple two-parameter read tool with no output schema, the description covers what is fetched (pricing, context length, capabilities) and how, which is enough for correct invocation. Only minor gaps remain, such as behavior when the model or version does not exist.

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 (model_name with slash handling, version defaulting to latest) are fully documented in the schema itself. The description adds no parameter-level detail beyond implying a single model lookup, which is the expected baseline 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?

The description gives a specific verb and resource ('Get one model's catalog entry') and enumerates the content returned (pricing, context length, capabilities), so the agent knows exactly what it fetches. The contrast with the sibling deepinfra_list_models is implied by 'one model' but never named, so it falls short of explicit 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?

Usage is only implied: the singular 'one model' plus the required model_name parameter signal that this is the lookup path when a specific model is already known, versus list_models. There is no explicit statement of when to prefer this over alternatives and no exclusions or prerequisites mentioned.

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 the safety profile, so the description only needs to add context. It does add the granularity of returned data (per-model/per-endpoint, request vs token limits) and the underlying REST endpoint, but says nothing about auth requirements, throttling of the call itself, or freshness of the values.

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. The trailing 'DeepInfra REST: GET /v1/me/rate_limit' mapping is mildly redundant for an agent but small and potentially useful for debugging.

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 with no output schema, the description covers what the tool does and roughly what it returns. It stops short of describing the response shape or the scope of 'account', but nothing critical for invocation is missing.

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 document and the baseline is 4. Schema description coverage is 100% and no additional parameter guidance 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?

Specific verb ('Get') plus resource ('account's current rate limits') with a clarifying parenthetical that the limits are per-model/per-endpoint request and token limits. This clearly distinguishes it from siblings like deepinfra_get_usage, deepinfra_get_usage_tokens, and deepinfra_get_account.

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 the use case (fetch current limits) but gives no explicit when-to-use guidance, no prerequisites, and names no alternatives among siblings such as the usage-related tools. Adequate but leaves routing to inference.

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 safe-read profile is covered. The description adds the underlying REST endpoint (GET /payment/usage), which is minor debugging value, but says nothing about auth scope, aggregation granularity, or whether the result is a total or a line-item breakdown. With annotations carrying safety, this is adequate but adds little.

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 before the REST mapping. Nothing is wasted, though the REST endpoint line is marginal value for an agent that only needs to call the tool.

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 more burden for conveying what comes back; 'spend / usage' is thin and doesn't say whether the response is a single aggregate or a per-model/per-period breakdown. Combined with the unaddressed sibling overlap, the definition is minimum-viable rather than 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?

Schema description coverage is 100%, so the schema already documents both 'from' (formats: YYYY.MM, 'current', 'current(-N)', unix timestamp) and 'to' in detail. The description adds no parameter semantics beyond implying a billing period. Baseline 3 applies since structured data 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 spend / usage for a billing period') with a scope qualifier, which is clearer than a bare 'get usage'. However, it does not differentiate itself from the close siblings deepinfra_get_usage_rent and deepinfra_get_usage_tokens, so an agent cannot tell from the text alone whether this is the aggregate billing view or a subset.

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 context, no prerequisites (e.g. billing/account scope), and no routing advice toward the rent/tokens usage siblings. The agent must infer usage from the 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?

Annotations already declare readOnlyHint=true, so safety is covered. The description adds the REST mapping (GET /payment/usage/rent), confirming a non-mutating billed-usage read, but says nothing about aggregation granularity, response contents, or pagination/limits.

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 zero filler; the purpose leads and the endpoint mapping follows. Nothing redundant or 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?

For a simple two-parameter read tool this is close to sufficient, but with no output schema the description does not indicate what the usage figures look like (per-GPU, per-instance, totals). The endpoint string partially compensates but leaves the return shape undocumented.

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 both parameters (from/to unix seconds, to defaulting to now) are documented there. The description adds no format or semantic detail 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 ('Get GPU rental (dedicated hardware) usage') and scopes it to a time range. The '(dedicated hardware)' parenthetical usefully distinguishes rent billing from the token-usage siblings like deepinfra_get_usage and deepinfra_get_usage_tokens, though it doesn't name those siblings 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?

The 'for a time range' phrasing and the rent vs token distinction imply when this applies, but there is no explicit statement of when to prefer it over deepinfra_get_usage or any prerequisite/exclusion guidance. Usage is inferable rather than stated.

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 this is a safe read, so the description's job is lighter. It adds the REST mapping (GET /payment/usage/tokens), which is mildly useful context, but says nothing about return grouping, aggregation granularity, or auth requirements. Adequate but not rich.

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 core purpose leads. The REST endpoint clause is marginally non-essential but not wasteful.

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 fully documented params and no output schema, the definition covers what an agent needs to invoke it correctly. Missing only the sibling disambiguation that would make it fully self-sufficient.

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 both parameters have thorough descriptions ('YYYY.MM', 'current', 'current(-N)', unix timestamp), so the schema carries the semantics. The description adds nothing beyond the period framing, making the baseline 3 correct.

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 is clear enough for an agent to act on. It doesn't differentiate from close siblings like deepinfra_get_usage or deepinfra_get_usage_rent, 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?

No guidance on when to use this versus deepinfra_get_usage, deepinfra_get_usage_rent, or deepinfra_query_logs. The only hint of usage is the implicit period framing, which an agent must infer.

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 by structured data. The description adds genuinely useful context beyond that: the output is metadata only and excludes secret values, which matters for a credentials-related resource, plus the REST endpoint mapping for traceability.

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 zero filler, and the most decision-relevant fact (metadata only, not secrets) is placed immediately after the purpose rather than 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 trivial read-only list with no parameters and no annotations requiring elaboration, the description covers purpose, returned content, and endpoint mapping. A brief note on ordering or whether all tokens are returned in one call would close the 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate. Baseline 4 applies; no parameter detail is needed or missing.

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') with a precise qualifier that resolves the main ambiguity about what is returned. No sibling in the list covers token management, so there is no overlap risk; an agent knows exactly what this does.

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 the resource name – an agent can infer it should call this when it needs to enumerate tokens. There is no explicit when-to-use, prerequisite, or alternative stated, but with no sibling covering tokens there is little routing ambiguity to resolve.

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 tells the agent this is a safe read, and the description adds essentially nothing behavioral beyond restating that it lists deployments and echoing the REST route. No mention of pagination, result ordering, or whether accounts without deployments return empty.

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 purpose front-loaded and no filler. The REST endpoint reference ('GET /deploy/list') earns marginal value for traceability but is not actionable for an agent, keeping it just below a 5.

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, annotation-covered read-only list tool with a fully documented single parameter, this is minimally adequate. However, with no output schema, the description still leaves the return contents and any pagination behavior unaddressed.

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 'status' parameter is fully documented in the schema, so the baseline is 3. The description does not mention the status filter at all, adding no meaning beyond structured data.

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 dedicated deployments'), and the plural vs. the sibling 'deepinfra_get_deployment' (singular) implicitly separates listing from fetching one. It does not explicitly name or contrast with any sibling, 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?

No guidance on when to use this versus alternatives such as deepinfra_get_deployment (single, by id) or deepinfra_get_deployment_stats. Usage is only implied by the name; no conditions, prerequisites, or exclusions are 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?

readOnlyHint=true already declares the safe-read profile, so the bar is lower. The description adds the REST mapping (GET /payment/invoices), confirming a non-mutating read, but says nothing about pagination semantics, auth requirements, or result shape beyond what the schema and annotation imply.

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, nothing wasted. The REST endpoint line is arguably marginal but costs almost nothing and is not distracting.

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, fully-documented, zero-required-param read tool with an annotation covering safety and no output schema to explain, the description is sufficient. Only minor gaps (pagination behavior, result format) remain.

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%, with limit, invoice_type, and starting_after all documented inline, including the pagination cursor. The description adds no parameter detail, so the baseline 3 is correct — the schema carries the full load.

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+scope: 'List billing invoices for the account', which is unambiguous and maps to a resource none of the siblings cover. It stops short of explicitly differentiating from billing-adjacent siblings like get_usage or get_usage_rent, so it lands at a clean 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?

Usage is implied by the listing verb and account scope, but there is no explicit when-to-use guidance, no exclusions, and no routing against the several other billing-shaped siblings (get_usage, get_usage_rent, get_rate_limit). Minimum-viable, with the agent left to infer the choice.

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 declares this is a safe, non-mutating read, so the description carries a reduced burden. It adds the underlying REST endpoint (GET /models/list), which is mildly useful provenance but discloses nothing about pagination, ordering, or result size.

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, front-loaded with the purpose. The trailing REST endpoint reference is the least essential element but is short enough not to undermine the structure.

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, parameterless, read-only catalog listing with annotations covering the safety profile, the description is sufficient to invoke correctly. With no output schema, it could say more about what the list contains, but the endpoint reference partially compensates.

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; the baseline for a parameterless tool is 4. No misleading parameter language is 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?

States a specific verb ('List') and resource ('DeepInfra model catalog'), with a parenthetical clarifying scope to all available models. It implicitly distinguishes itself from the singular sibling deepinfra_get_model, though it never names that 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?

The phrase 'all available models' implies this is the enumeration entry point versus a single-model lookup, but there is no explicit when-to-use guidance or named alternative such as deepinfra_get_model. Usage must be inferred from the scope wording.

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, but the description adds no behavioral context beyond that: no retention/availability window for logs, no statement about result volume or how limit interacts with the window, no permissions notes. The REST endpoint reference is a mapping detail, not 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?

Two short sentences, purpose front-loaded with no filler. The trailing REST endpoint line adds little for an agent choosing a tool, but it costs almost nothing.

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 at least hint at what a returned log line contains and whether results are paginated or truncated at the limit; it says neither. Parameter documentation is complete via the schema, so the tool is callable, but the return side is a real 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 deploy_id, from, to, and limit are already fully documented (inclusive/exclusive boundaries, default 100, range 1..1000). The description's 'over a time window' only restates from/to; baseline 3 applies since 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?

States a specific verb (Query), resource (inference request logs), and scope (a dedicated deployment, over a time window), which is enough to distinguish it from metrics/stats siblings. It never names an alternative tool, so differentiation is inferred from the resource noun rather than stated.

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 exclusions, and no pointer to alternatives such as deepinfra_get_live_metrics or deepinfra_get_deployment_stats for real-time or aggregate views. The agent must guess whether this is the right tool for a given observability question.

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 supply destructiveHint=true, and the description adds genuinely new behavioral context beyond that: the operation incurs ongoing GPU charges while the deployment runs, and 'resume' implies the deployment must already exist. It still doesn't say what happens on an already-running or nonexistent deployment id.

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 sentences, each earning its place: the action, the cost warning, and the underlying REST endpoint. Front-loaded with the action and the warning immediately follows, with zero 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 one-parameter mutation with annotations covering the destructive flag and no output schema, the description covers the essentials: action semantics, cost warning, and endpoint. Minor gaps remain around error/idempotency behavior (already running, invalid id), but nothing critical for correct invocation is 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?

With a single parameter at 100% schema description coverage, the schema already documents deploy_id fully. The description adds no format, sourcing, or lookup guidance beyond what the schema provides, 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+resource ('Start (resume) a dedicated deployment') and disambiguates the verb by equating start with resume, which is the key semantic distinction. It does not explicitly name the sibling stop_deployment as the inverse operation, 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?

Usage context is implied by '(resume)' — the tool applies to an already-existing, presumably stopped deployment — but there is no explicit when-to-use statement, no prerequisites, and no reference to the alternative (stop_deployment) or to the case where the deployment is already running.

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 provide destructiveHint=true; the description adds real behavioral context by disclosing the operational effect (inference halts, GPU billing stops) and framing it as a pause rather than a deletion. It stops short of saying whether the deployment config/state is retained or whether it can be restarted, which matters for a destructive-hinted 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?

Three tight sentences with the primary purpose front-loaded and no filler. The REST endpoint sentence is marginally redundant for invocation purposes but useful for cross-referencing with 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 one-parameter tool with no output schema, the description covers purpose, side effects, and cost implications adequately. Gaps are minor: no statement about reversibility or the resulting deployment state after stopping.

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 deploy_id parameter, so the description adds no parameter-level meaning beyond what the schema already documents. 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 (stop/pause) and resource (a running dedicated deployment), and the parenthetical 'pause' clarifies the semantic nuance versus a permanent delete. It does not explicitly name deepinfra_start_deployment as its counterpart, so sibling differentiation is implied rather than stated.

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 stated consequences ('halts inference and stops accruing GPU charges'), which tells the agent why one would call it, but there is no explicit when/when-not guidance or routing to start_deployment for the reverse operation.

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