Live GPU compute & inference token price indices for AI agents — 591 reference indices across H100/A100/B200/B300 spot+on-demand, Claude/GPT/Llama token pricing, and more. Every value is methodology-versioned and citable via the /v1/verify handshake.
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Average 4.3/5 across 5 of 5 tools scored.
Each tool has a distinct primary purpose: discovery (list/search), pricing (get_price), citation (get_index), and verification (verify). There is slight overlap between get_price and verify in 'current value' lookup mode, but descriptions clarify intended use cases.
Most tools follow a consistent verb_noun pattern in snake_case (get_index, get_price, list_indices, search_indices). The tool 'verify' deviates by using a bare verb without a noun, breaking the pattern slightly.
Five tools is well-scoped for a data lookup and verification server. Each tool covers a distinct need without bloat, and the count is within the ideal range for a focused service.
The tool set covers the full lifecycle for interacting with FLOPS indices: discover (list/search), retrieve current price (get_price), retrieve citation metadata (get_index), and validate values (verify). No obvious gaps for the stated purpose, as advanced features like source-level detail are intentionally excluded.
get_indexAInspectResolve a FLOPS index to its source-opaque PUBLIC citation payload. Returns exactly {index_id, value, unit, as_of, data_tier, confidence, methodology_url, verify_url, citation_url, permalink} — no source-level detail. Prefer get_index when you intend to CITE the value.
| Name | Required | Description | Default |
|---|---|---|---|
| index_id | Yes | FLOPS index id / slug, e.g. 'FLOPS-H100-OD', 'FLOPS-A100-SPOT'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the payload is 'source-opaque' and 'PUBLIC', and explicitly lists the exact return fields and states 'no source-level detail.' This provides meaningful behavioral transparency beyond just the operation name, though it does not mention failure modes or authorization 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?
The description is three sentences: purpose, return format, and usage guidance. Every sentence adds value, is front-loaded with the primary action, and there is no redundancy or 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 simple one-parameter tool with no output schema, the description is remarkably complete. It specifies the exact return payload fields, conveys the important limitation (source-opaque), and gives guidance on when to prefer it over siblings. No significant gaps 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 coverage is 100% and the parameter 'index_id' is well-described in the schema with examples. The description adds no additional parameter-specific semantics, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Resolve' with a clear resource ('FLOPS index') and output ('citation payload'), and explicitly distinguishes itself from siblings by stating 'Prefer get_index when you intend to CITE the value.' This makes the tool's role 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?
The description explicitly states when to use the tool ('Prefer get_index when you intend to CITE the value'), providing clear context. It does not explicitly list when-not-to-use or alternative tools, but the context is sufficient for most cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_priceAInspectFetch the current published value for a FLOPS compute price index. Returns {value, unit, as_of, data_tier, confidence, verify_url, citation_url}. Cheapest way to answer 'what does X trade at right now?' Confidence is a label (HIGH or MED), never a numeric weight. Carries no methodology or source attribution by design — the anonymous surface does not enumerate the tiers or families it withholds.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | FLOPS index slug. Accepts FLOPS-<MODEL>-OD, FLOPS-<MODEL>-SPOT, FLOPS-<MODEL>-DEPIN, or a bare gpu_model. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full transparency burden. It discloses the exact return fields, clarifies that confidence is a label not a numeric weight, and explicitly states that it 'carries no methodology or source attribution by design.' These are important behavioral traits that go beyond schema. It doesn't cover error handling or staleness, but the disclosed caveats are significant.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the core action. Each sentence adds value: purpose, return structure, use case, confidence semantics, and attribution limitation. There is no fluff or repetition, making it highly efficient.
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 tool with no output schema, the description is remarkably complete. It covers what the tool does, what it returns, when to use it, and notable caveats. The inclusion of the return fields compensates for the lack of an output schema, and the caveat about missing attribution helps set expectations fully.
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 input schema already fully describes the single parameter 'slug' with its accepted formats. The description adds no additional parameter-level meaning beyond what the schema provides. With 100% schema coverage, a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: 'Fetch the current published value for a FLOPS compute price index.' It also distinguishes from siblings by framing it as the 'Cheapest way to answer what does X trade at right now?' This makes it clear this is a lightweight price lookup compared to tools like get_index or search_indices.
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 provides clear usage context: it is the 'cheapest way to answer what does X trade at right now,' which implies a quick, minimal-data need. However, it does not explicitly name alternative tools or say when not to use it, so it earns a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_indicesAInspectList all public FLOPS compute-price indices. Returns {count, indices[]} where each row is {index_id, family, value, unit, as_of, confidence, change_24h, delayed}. Use this to discover available indices before calling get_price or verify. No auth required.
| Name | Required | Description | Default |
|---|---|---|---|
| family_filter | No | Optional family prefix to filter by (e.g. 'FLOPS-H100' or 'FLOPS-B300'). Case-sensitive substring match. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the return format ({count, indices[]} with field names), the public nature of the data, and that no auth is required. It does not mention pagination or rate limits, but for this simple list-all tool, the disclosure is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose and return structure. Every sentence provides value without redundancy.
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?
Despite lacking an output schema, the description explicitly lists the return fields. It also provides usage context relative to sibling tools and notes authentication requirements. The tool is simple (one optional parameter), and the description fully covers its operation.
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 schema fully documents the single parameter (family_filter) with clear semantics including case-sensitivity and substring match. The description adds no extra parameter details, but high schema coverage makes this acceptable per the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all public FLOPS compute-price indices, includes the return structure, and differentiates from siblings by mentioning usage before get_price or verify. The verb 'List' and resource are specific and 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?
It explicitly says 'Use this to discover available indices before calling get_price or verify', providing a clear when-to-use directive. This also implies the alternatives (get_price, verify) for subsequent steps, meeting the explicit guidance criterion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_indicesAInspectResolve a free-text query to canonical FLOPS index slugs. Use when you don't know the exact slug — e.g. 'H100 spot' returns the matching SPOT family entries. Best paired with get_price to look up + quote a value in one turn.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Free-text query (e.g. 'h100 spot' or 'b200 on-demand'). | |
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It explains the mapping behavior via example and mentions 'canonical' slugs, but lacks disclosure on edge cases (empty results, limit behavior, fuzzy matching). Core behavior is clear but not deeply transparent.
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 sentences, front-loaded with purpose, then usage and pairing recommendation. No redundant or filler content; 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 simple 2-parameter search tool with no output schema, the description covers purpose, when to use, and paired tools. Missing details like empty-result behavior or limit semantics, but these are minor gaps 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?
Schema coverage is 50%: q has a description with the same example, while limit has no semantic description. The tool description does not add new meaning for limit and repeats q's example, adding minimal value 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?
The description clearly states the tool's function with a specific verb ('Resolve') and resource ('free-text query to canonical FLOPS index slugs'). The example 'H100 spot' returns matching SPOT family entries, distinguishing it from siblings like list_indices or get_index.
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?
Explicitly says 'Use when you don't know the exact slug', providing a clear condition. Also recommends pairing with get_price for lookup+quote. It doesn't explicitly name alternatives (e.g., get_index when slug is known), but the context implies them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verifyAInspectCheck a FLOPS index value against the published index. Pass value with a number you are about to cite (or already hold) and the response tells you whether it is right: verified true/false, expected (what you submitted), actual_value (what FLOPS publishes) and delta_pct. Omit value to simply look up the current published value — then verified is null, which means 'not checked', NOT 'correct'. Without an API key you are checked against the public value, which is rounded to 2 decimals and delayed onto a 6-hour grid, so a full-precision real-time number will not match anonymously. No source list or source-count is exposed.
| Name | Required | Description | Default |
|---|---|---|---|
| value | No | Optional. The number you want checked, e.g. 2.14. Omit it to do a plain lookup. | |
| index_id | Yes | FLOPS index identifier (e.g. 'FLOPS-H100-OD', 'FLOPS-B300-SPOT'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the safety and behavior burden. It explicitly discloses response fields (verified, expected, actual_value, delta_pct), explains the null semantics for verified, and details the anonymous rounding/delay limitation. This is thorough and adds significant context beyond 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?
The description is front-loaded and every sentence adds necessary information—purpose, usage, response details, null semantics, and anonymous limitations. It is slightly longer than the minimal two-sentence example but remains dense and avoids redundancy, earning a high score with a minor deduction for length.
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 no output schema, the description must explain return values, and it does so comprehensively. It covers the tool's purpose, both usage modes, edge cases (null verified), and anonymous limitations. For a tool with this complexity, the description is complete enough for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% parameter descriptions. The description adds extra meaning by explaining the dual behavior of the optional value parameter (check vs. plain lookup) and providing concrete examples for both params. This goes beyond the schema, justifying a score above the baseline.
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 opens with a specific verb and resource: 'Check a FLOPS index value against the published index.' It clearly differentiates from sibling tools (get_index, get_price, list_indices, search_indices) by focusing on verification rather than simple retrieval or listing.
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 clear usage scenarios: pass a value to verify it, omit value to look up the current published value. It also warns about anonymous limitations. It does not explicitly name sibling tools as alternatives, but the mode distinction is clear, so this is slightly below the highest bar.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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