tickerr

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

No annotations are provided, so the description carries the full burden. It discloses that the tool ranks models by cost using input/output tokens and optional filter. It implies a read-only operation but does not explicitly state it. No mention of auth needs or rate limits; the behavioral disclosure is adequate but not comprehensive.

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

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

Two sentences with no extraneous information. The first sentence states the core purpose with a specific verb, resource, and context. The second provides usage guidance and optional filtering. Front-loaded and 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?

The tool has 4 parameters with full schema coverage and no output schema. The description explains the input (token counts) and the output (ranking by total cost), which is sufficient for an agent. It could hint at output format (e.g., list with models and costs) but the current description is complete enough given the schema richness.

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% with all parameters described. The description adds value by explaining the role of input_tokens and output_tokens in computing total cost and notes the optional filter to narrow to a provider, reinforcing the schema descriptions. The 'top' parameter's behavior is implicitly linked to 'cheapest N' ranking.

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 ranks LLM models by total cost for a given token workload, specifying the verb 'rank' and resource 'models by cost'. It distinguishes from siblings like get_api_pricing (raw pricing) and get_model_performance (performance metrics) by focusing on cost comparison with workload ratio.

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: 'find the cheapest model for your specific input/output ratio - useful for agent routing decisions'. It also mentions optional filtering by provider. However, it does not explicitly state when not to use it or compare to sibling tools, though the context is clear.

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?

Describes data sources (OpenRouter API and official provider docs) and update frequency (twice daily). No annotations exist, so description carries full burden—decent disclosure for a read-only pricing 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 concise sentences front-load the main purpose and usage. Every sentence adds 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?

Adequate for a simple two-parameter tool with no output schema. Covers purpose, usage, and data freshness. Could add output format details but not critical.

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 description essentially repeats the schema's parameter descriptions (filter examples and limit default). No additional semantic value beyond 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?

Description clearly states it gets current API token pricing for 300+ LLM models, specifying input/output/cached cost per 1M tokens. It distinguishes from siblings like compare_pricing by focusing on retrieval with filtering.

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?

Provides explicit use case: 'find the cheapest available model or check pricing before routing a request.' Does not explicitly mention when not to use or compare with compare_pricing, but the filtering guidance adds context.

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 exist, so the description carries the full burden. It only mentions grouping by category but does not disclose behavioral traits such as whether the tool is read-only, requires authentication, or has rate limits. The description adds minimal behavioral context beyond the basic purpose.

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 concise sentence that efficiently communicates the tool's purpose and key behavior (grouping by category). No extraneous information; every word 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?

Given low complexity (1 optional parameter, no output schema), the description adequately covers the tool's function. It does not describe output format, but for a simple listing tool with category filtering, it is nearly complete.

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

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

Schema coverage is 100% (1 parameter fully described). The description adds no meaning beyond the schema's 'Filter by category slug'—it merely restates the grouping behavior. With high coverage, the baseline is 3, and no additional value is provided.

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 retrieves best free plans across AI tools, grouped by category. It uses a strong verb ('Find'), specifies the resource ('free plans across AI tools'), and the grouping behavior distinguishes it from siblings like compare_pricing or get_api_pricing.

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 no guidance on when to use this tool versus alternatives (e.g., compare_pricing, get_api_pricing). It does not mention conditions, prerequisites, or exclusions, leaving the agent to infer use cases.

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. It discloses the data source (official status pages), time range (90 days), and the effect of including my_status (live signal, agent-reported activity). It does not explicitly state read-only or destructive nature, but the context strongly suggests it is non-destructive and safe.

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 extremely concise: two sentences that front-load the core function in the first sentence and elaborate on the optional feature in the second. Every sentence provides new information without redundancy or wasted words.

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 description covers the purpose, data source, and optional parameter, but lacks details on the response format or structure, especially since there is no output schema. Including what fields the incident history contains (e.g., date, status, description) would improve completeness, but the current level is adequate for a simple read 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 input schema provides 100% coverage with descriptions for all three parameters (slug, limit, my_status). The description adds value by explaining the purpose of my_status ('contribute to Tickerr's live signal') and its effect, which goes beyond the schema's technical description, justifying a slightly higher score.

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 function: getting historical incidents (outages, degradations) for any AI tool from the last 90 days, sourced from official status pages. It distinguishes itself from siblings like get_tool_status (current status) and report_incident (reporting), making its unique role evident.

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 usage context (historical data, optional contribution) but does not explicitly state when to use this tool over alternatives like get_tool_status or report_incident. No when-not-to-use or alternative mention, leaving the agent to 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?

No annotations provided, so description carries full burden. Discloses data freshness (updated every 5 minutes), coverage (list of providers), and metrics. Could still mention safety (e.g., read-only) but 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?

Two sentences: first states purpose and output, second provides usage guidance. No fluff, efficient 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?

Given one parameter, no output schema, and no annotations, the description covers all necessary context: what it returns, update frequency, provider coverage, and how to use it with sibling.

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

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

Schema coverage is 100% with provider description listing allowed values. Description repeats this list without adding new semantics, meeting baseline for high coverage.

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' with resource 'real-time API inference performance' and explicit metrics (TTFT, throughput, success rate). Clearly distinguishes from sibling tools like get_api_pricing.

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 states to use alongside get_api_pricing for routing decisions, providing context. However, it does not specify when not to use or mention alternatives besides get_api_pricing.

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 provided, so description carries full burden. It accurately indicates a read operation ('Get') but does not explicitly state it's read-only or mention any side effects, authentication needs, or rate limits. Adequate but lacks depth.

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?

Single sentence, front-loaded with the action and key details. No wasted words, 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 simple tool with one parameter and no output schema, the description provides sufficient detail on what data is returned (rate limits, tokens, context window, plan tier). The mention 'and more' is vague but acceptable given the clarity of the listed items.

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 parameter `slug` is fully described in the input schema with examples. The description does not add additional meaning beyond the schema, so 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 clearly states the tool retrieves rate limits and plan details for AI tools, listing specific data points (requests per minute, tokens per day, context window). It effectively distinguishes from sibling tools like `get_api_pricing` and `get_free_tier` by specifying the exact resource.

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 explicit guidance on when to use this tool over alternatives. The description implies usage for rate limit and plan queries but does not mention when not to use it or compare with siblings like `compare_pricing`.

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 full burden and does well: it discloses that checks occur every 5 minutes from independent infrastructure and that my_status unlocks additional data. However, it does not describe the response format or any error/edge-case behaviors.

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 efficient with four sentences, front-loading the purpose. Every sentence adds unique information, though slight trimming is possible without losing clarity.

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 and no annotations, the description covers the key behavioral and parameter-related context well. It explains the conditional parameter and lists monitored tools. However, it could be more complete by outlining the structure of the basic vs. enriched response.

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 description adds significant value beyond the schema: it contextualizes the my_status parameter with a conditional usage rule and explains the benefits of including it (unlocking p50/p95 TTFT and 90-day uptime).

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 verb 'Get' and the resource 'live operational status, uptime, and response time for any AI tool', and distinguishes from siblings like get_incidents or get_model_performance by focusing on real-time status and latency monitoring.

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 instructs when to include the my_status parameter ('if your agent has made any call to this provider in the last 5 minutes') and explains what it unlocks, but does not explicitly mention when not to use the tool or provide alternatives.

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 fully bears the burden. It explains the tool lists tools, mentions the my_status parameter for get_tool_status, and implies no side effects. No contradictions.

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 the core action, followed by examples and a clear next step. Every sentence earns its place with no 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?

For a simple, parameterless tool, the description covers purpose and next steps well. However, it does not specify the output format or what fields are returned per tool, which would improve completeness.

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?

No parameters exist (schema coverage 100%), so baseline is 4. The description adds value by explaining the content of the list (tools and LLM APIs) and the follow-up action, exceeding baseline expectations.

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 90+ AI tools and LLM APIs' monitored by tickerr.ai, providing specific examples. It distinguishes from siblings like get_tool_status by focusing on listing rather than status.

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 tells when to use this tool (to list tools) and directs the user to 'use get_tool_status with my_status' for enhanced data, including the trade-off between basic and enhanced status.

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 provided, so description carries full burden. It details return value (live signal, incident state, fallback model), data collection (anonymized, provider/model/error code/latency only), data exchange consent, and opt-out mechanism. No contradictions.

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?

Description is long but well-structured: purpose first, then usage guidelines, then return value, then legal terms. Front-loaded with key action. Some repetition (e.g., model/provider list) but overall 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?

Given 10 parameters, no output schema, and no annotations, the description is highly complete. It covers when to use, what data is sent, what is returned, data usage policies, and opt-out. No 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 covers 100% of parameters with clear descriptions. The tool description adds minimal extra parameter insight (e.g., context for is_resolution_report), but baseline 3 is appropriate as 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 explicitly states 'Report an LLM API failure or successful recovery your agent just experienced.' It clearly defines the action and distinguishes from siblings like get_incidents, which reads incidents.

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?

Provides explicit guidance: 'Call this automatically before retrying any 5xx error or timeout...' and 'also call this when a previously failing model succeeds again - set is_resolution_report: true.' Includes usage terms and consent instructions.

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