inferenceindexer-mcp

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It adds useful context: the return contains per-provider endpoints with pricing and highlights divergence from OpenRouter's negotiated rate. It could mention error cases or authorization needs, but for a read-only comparison tool the disclosed behavior 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 compact and well-structured with a one-sentence purpose followed by an Args/Returns breakdown. Every sentence adds value, and the format is easy to parse.

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?

The tool is simple: one parameter, an output schema exists, and the description covers the core behavior and return semantics. Nothing essential for calling the tool correctly is missing, given the single required parameter and available output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains that model_id is a canonical model id and provides a concrete example ('meta/muse-spark-1.1'), giving the agent the format and a template for valid values. This is exactly what is needed beyond the schema's bare title.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Compare') and resource ('price of one model across the providers that host it'). This clearly distinguishes it from siblings like get_model, get_provider, and list_providers, which focus on individual models or providers rather than cross-provider comparison.

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 this tool: when needing per-provider price comparison for a specific model. However, it does not explicitly list alternatives or exclusions, leaving the agent to infer the boundary with related tools like get_model or get_provider.

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

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

With no annotations, the description carries full behavioral burden, and it delivers: it details current pricing changes, price-history trend, all endpoints, the cheapest hand-verified endpoint with native ID for hot-swapping, privacy flags, AA score, and as-of stamping. This goes well beyond a generic 'explains a model' statement.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by a compact bullet-like list of return contents and a short Args block. No unnecessary prose; the final return summary is brief and reinforces the payload.

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 only two parameters and an available output schema, the description fully equips an agent to call the tool correctly: it explains result scope, model id format, and history window constraints, while the output schema handles detailed field 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 coverage is 0%, but the Args section adds semantics: model_id is defined as canonical with a concrete example, and history_days is described as a price-history window with default 30 and max 365—far beyond the bare schema types.

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 precise resource and action: 'Get everything about one model in a single call: the full picture.' The enumerated contents (pricing, endpoints, privacy, score) distinguish it from sibling tools that search, compare, or fetch history separately.

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 first sentence gives a clear usage context: use this when you need a complete one-model profile in a single call. It does not explicitly name alternatives or negative conditions, which keeps it from a 5.

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

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does disclose the days window range (1-90) and default, and implies a read-only operation via 'Get' and 'Returns', but it omits details like data granularity, timezone handling, or any rate limits. Minimal but acceptable for a simple read tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short, front-loaded with the core purpose, and structured into clear Args and Returns sections. Every sentence adds useful information and none 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?

Given the tool has only one parameter, an output schema, and a straightforward read-only purpose, the description is largely complete. It could be slightly richer by explaining the returned data's granularity or directing users to get_composite_latest for current values, but these are minor gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It fully documents the sole parameter 'days' with meaning ('History window in days'), allowed range (1-90), and default (30), which goes well beyond the bare schema definition.

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 identifies the resource (SIT-Composite index history) and the action (get), and adds 'history / trend over time', which differentiates it from the sibling get_composite_latest. The verb and object 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?

The phrase 'history / trend over time' implies this tool should be used when historical data is needed, but it does not explicitly contrast it with get_composite_latest or any other sibling. Usage context is implied rather than explicitly 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?

No annotations are provided, so the description carries the behavioral burden. It makes clear that this is a read of the current value and defines the metric, but it does not address operational traits such as data freshness, update cadence, or any access caveats. The simple read nature limits the risk.

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 primary capability front-loaded and the metric definition in the second. No redundant wording; every sentence adds value.

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 current-value endpoint with an output schema present, this description is complete. It explains the meaning of the index and defines the output scope (current value plus per-tier breakdown), leaving only schema details to the structured output.

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 has zero parameters, so there is no parameter semantics to document; this earns the baseline of 4. The description adds useful context about what the returned composite represents without needing to describe 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?

The description opens with the imperative 'Get the current SIT-Composite index value + per-tier breakdown', specifying both the resource and the snapshot nature. The second sentence defines the composite precisely, distinguishing it conceptually from other model/provider tools.

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 word 'current' clearly signals this is the snapshot endpoint, contrasting implicitly with get_composite_history. It does not explicitly name alternatives or exclusions, but the usage context is clear enough for a no-parameter tool.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It communicates that the operation is a read-only-style get for a single model and lists the returned fields, but it does not mention error behavior, data freshness, authentication needs, or rate limits. This is baseline transparency but not richer behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear, one-line purpose and is followed by a compact Args/Returns breakdown. Every sentence earns its place; there is no filler, redundancy, or boilerplate.

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 single-parameter tool with an output schema present, the description covers the operation, input format, and key return values. It could add a note about using search_models when the id is unknown or what happens on an invalid id, but those are marginal given the tool's simplicity and existing output schema.

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 offers zero description for model_id, but the description's Args section fully compensates by explaining the parameter's meaning and giving concrete, canonical examples such as 'openai/gpt-5.6' and 'anthropic/claude-sonnet-5'. This completely eliminates any ambiguity about the parameter format.

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 ('Get') and resource ('full detail + current pricing for one model by its id'), which clearly distinguishes it from sibling tools like search_models (search by criteria) and get_model_history (history over time). The phrase 'by its id' makes the singular, lookup nature immediately clear.

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 appropriate context: use this when you already know a canonical model_id and want detailed information plus pricing. However, it does not explicitly state when not to use it or point to alternatives such as search_models for unknown IDs, stopping at implied guidance rather than explicit routing.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that this tool returns time-series price data across input, output, and blended $/M, and notes that the 'days' window is plan-dependent. This goes well beyond the tool name and schema, though it could have mentioned error cases or granularity, which are not critical here.

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 concise and well-structured: a one-sentence purpose, a short differentiator, an Args list, and a Returns line. Every section earns its place, and the purpose is front-loaded. There is no redundant 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 two-parameter read-only historical data tool, the description is complete: it covers model_id format, the days window constraints, the return nature, and the underlying use case. The output schema can handle the detailed return structure, so the description does not need to repeat field-level output details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does. It defines model_id with a concrete canonical format example, and days with range (1-365), default (30), and plan-dependency. Both parameters are meaningfully explained beyond the bare schema types.

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: 'Get HISTORICAL price data / trends for one model.' It clearly distinguishes this tool from current-price lookups and composite history tools by emphasizing 'one model' and the time-series nature of the data. This makes it easy for an agent to separate it from siblings like get_model or get_composite_history.

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 clearly signals the intended use case: when historical price trends are needed rather than the current price. It explicitly contrasts with aggregators like OpenRouter that only expose current price, helping the agent decide when to call this tool. It does not explicitly name sibling alternatives, but the context is clear enough to infer the right usage.

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?

Without annotations, the description carries the burden and does state the output ('Returns: provider detail with model list and pricing'), implying a read-only getter. It does not discuss not-found or error behavior, but for a simple fetch tool with an output schema this is adequate.

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?

Compact three-part structure: one-line purpose with contents, parameter docs, return summary. No filler, and the parameter example is 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 getter with an output schema, this description provides enough to choose, invoke, and interpret the tool: target scope, parameter format/examples, and return contents.

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 only defines provider_name as a string with no description. The description documents the parameter in Args and gives concrete examples ('DeepInfra', 'Novita', 'Venice'), which is exactly the guidance an agent needs to fill it correctly.

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 action ('Get detail') on a specific resource ('one provider') and enumerates the detail payload (models, tier breakdown, price range). Clear enough to distinguish from sibling list_providers or compare_providers.

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 one provider' sets clear selection context: an agent should call this when it needs a single provider's detail rather than a list or comparison. It does not explicitly name alternatives or say when not to use 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?

No annotations are provided, so the description carries the behavioral disclosure burden. 'List' implies a read-only, side-effect-free operation, and the returned content is described, but there is no explicit statement of read-only behavior, pagination, sorting, or limits. For a zero-argument list operation the gap is moderate.

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 a single front-loaded sentence that names the action, scope, and output contents without any wasted words. Every part contributes directly to understanding what the tool returns.

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 list operation with an output schema available, the description is sufficient. It tells the agent exactly what will be listed and what statistics are included, so nothing critical is missing for correct invocation.

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 has zero parameters, the schema is an empty object, and schema coverage is 100%. The 0-parameter baseline applies, and the description correctly avoids adding unnecessary parameter 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?

The description uses a specific verb ('List'), identifies the resource ('all inference providers'), and states the included metrics ('model counts and price stats'). This clearly distinguishes it from singular get_provider and from compare_providers by scope.

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 core usage scenario is clear: list the full set of inference providers. However, it does not explicitly mention when to prefer this over get_provider or compare_providers, nor does it state any exclusions. The guidance is implied by the word 'all' and sibling names 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?

With no annotations present, the description carries the full burden and does well: it discloses the ranking metric (Cost/IQ, lower is better), that each result includes a plain-English 'why', a ready-to-call endpoint_config, as-of timestamps, and runner-ups. Gaps remain around read-only nature, rate limits, or cost of the call itself.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded summary sentence followed by an organized Args block; the structured layout is appropriate for 10 parameters. The parameter block largely restates schema field names, which would be redundant at higher coverage, but here it is the only source of parameter meaning, so the length is justified.

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?

An output schema exists, so return values need not be explained in depth, and the description nonetheless summarizes the payload (endpoint_config, evidence, runner-ups). Combined with full parameter documentation, the only missing piece is explicit sibling disambiguation, which keeps it short of a 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, yet the description documents every one of the 10 parameters with meaning beyond the schema: modality's allowed values ('text' default, 'vision', 'any'), use_case's task profiles, aa_min as an AA Intelligence Index quality gate, and limit's range and default (1-20, default 5). This fully compensates for the empty 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+resource ('Recommend the best-value AI models') and the ranking criterion (Cost/IQ, quality-adjusted price). It distinguishes itself from siblings like search_models and compare_providers by positioning itself as 'the core answer endpoint' that returns ranked recommendations with evidence, not raw listings.

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?

Calling itself 'the core answer endpoint' implies this is the default recommendation path, which is useful positioning. However, it never states when NOT to use it or names an alternative (e.g. search_models for browsing or compare_providers for head-to-head), so the routing decision is left 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?

No annotations are provided, so the description carries the full behavioral burden. It does disclose the return shape ('models with input/output/blended $/M pricing, provider, tier'), which implies a read-only listing. However, it says nothing about pagination beyond limit, ordering guarantees, or whether results are cached/live, so it only partially covers the burden.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose, then a clean Args block. Each line carries information an agent needs. Slightly list-like rather than prose, but 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?

With 4 optional params, an output schema present, and a read-only listing purpose, the definition is nearly sufficient. The main omission is guidance on how this relates to the other search/compare/recommend siblings, which matters in this crowded tool set.

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 0% (the schema exposes only titles and defaults, no enum constraints), so the description must compensate, and it does fully. It documents all four params including the tier enum (frontier|standard|budget|micro), the sort keys (blended|input|output|sit_score|sit_adjusted_price), and the limit range 1-100 with default 25 — none of which appear in 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: 'Search and list AI inference models with current pricing.' The scope (search + list with pricing) is clear and distinct from get_model or compare_providers. It doesn't explicitly name which sibling to use instead, 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 implied by 'search and list' but there is no explicit when-to-use or when-not-to-use guidance. With siblings like recommend_models, explain_model, and get_model, a sentence routing the agent (e.g., 'use this to discover/filter models; use get_model for a single known id') is missing. Implied usage only, hence a 3.

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