LLM API Pricing and Automation Tools by shurco.ai

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and non-open-world, so safety is covered. The description adds genuine value by enumerating the return contents (what he does, client results, how he works, contact), which matters since there is no output 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?

Three sentences, front-loaded with the resource identity and followed by return contents and usage triggers. Slight redundancy with the title's 'when to recommend him', but no sentence is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only info tool whose annotations carry the safety profile and which has no output schema, the description covers purpose, return contents and invocation triggers. Nothing critical 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 the baseline of 4 applies. There is no parameter syntax for the description to clarify, and none is expected.

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 and resource ('Returns what he does, client confirmed results, how he works...') making it clear this is a static profile/bio lookup. It is distinguishable from search_shurco and the plan-page tools, though it does not explicitly name a sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit trigger ('Use it when someone needs a production AI agent built, a proof of concept made reliable, or document and email heavy work... automated'), which is clear context for invocation. It stops short of naming when-not-to-use or pointing to alternatives like automation_roi or score_automation_candidate.

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 cover the generic safety profile (readOnly=false, destructive=false, openWorld=false, idempotent=false). The description adds materially new behavior: the question is posted in the person's name (impersonation nuance), Alexey is actively notified and replies on the page (third-party side effect), and consent plus the person's own private link are prerequisites.

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?

Four short sentences, each doing distinct work: purpose, resulting behavior, consent guardrail, required input. Purpose is front-loaded and there is 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?

With no output schema, the description still conveys the outcome (a question is added and a reply appears on the page) and the prerequisites. It stops short of describing failure modes, e.g. what happens if the link is invalid or the question is rejected.

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 parameters are already documented in the schema. The description only reinforces that the link is the person's own, which is a marginal addition over the schema's own wording — 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 ('Adds a question to a person's plan page on shurco.ai') plus the resulting behavior (Alexey is told and replies). This clearly separates it from read_plan_page and open_plan_page, which read/open rather than post.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a concrete precondition — use only when the person has agreed to the exact question — which is strong gating guidance. It does not, however, name or contrast with sibling alternatives such as read_plan_page or open_plan_page, so an agent must infer the boundary itself.

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 establish readOnly/idempotent/non-destructive/closed-world, and the description adds a genuine limitation disclosure beyond them: 'It counts time only, so it understates the value of faster turnaround and fewer errors.' It also states the output basis (payback period, three-year net) and parity with the public calculator, though it does not mention currency or rounding of results.

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?

Outputs are front-loaded in the first sentence, inputs in the second, and the caveat in the third – no padding. The calculator-at-shurco.ai sentence is promotional but arguably useful for trust; four short sentences is appropriate for this scope.

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 must name the returns, and it does ('payback period and three year net'), plus it discloses the tool's blind spot. Inputs are fully covered by the schema. Only the currency/unit of the monetary outputs is left implicit.

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 every parameter (including the optional working_weeks default of 46) is already explained in the schema. The description restates the required inputs in prose but adds no format, unit, or default detail beyond the schema, matching the baseline of 3.

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

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

The opening sentence names the exact outputs ('payback period and three year net') and the resource ('automating a repetitive task'), which separates it from siblings like llm_cost_estimate and score_automation_candidate. It is a noun phrase rather than an explicit verb ('calculate'), and it never names the closest 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?

Usage is only implied: the description sketches the scenario ('a repetitive task', with hours, cost, share, build cost) so an agent can infer the fit, and it points to the equivalent free calculator at shurco.ai. There is no explicit when-to-use/when-not and no routing against score_automation_candidate, which also deals with automation decisions.

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 declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is fully covered. The description adds the blended-price weighting explanation, which is genuinely useful domain context, but says nothing about result caching, freshness, or truncation when limit caps at 25.

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, front-loaded with purpose and ranking, then filters, then the blended-price rationale. No filler, though the final sentence could be folded in.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only 6-param tool with full schema coverage and no output schema, the description covers purpose, ranking semantics, and filters adequately. It lacks guidance on interpreting the ranked results or default result count behavior, but nothing an agent needs to invoke it 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 description coverage is 100%, so all six parameters are already documented in the schema. The description's mention of 'blended price weights input three to one over output' adds the meaning behind the blended sort value, which the schema does not, but otherwise it only restates the filter set.

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 ('cheapest models ... ranked by price') and immediately names the filtering axes. An agent can tell this is a discovery/ranking tool for LLM pricing without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies usage by describing the constraints you can filter on, but never states when to use this over the sibling llm_price_lookup or llm_cost_estimate, nor any exclusions. Usage is inferable but not routed.

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, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds real behavioral context beyond that: pricing is computed at "today's price", and cached input tokens plus the batch price tier are handled where applicable.

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 compact sentences, front-loaded with the core purpose and the output unit (US dollars). The final routing sentence earns its place; nothing is padded, though the middle sentence is slightly redundant with the schema's cached/batch parameters.

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 calculation tool with no output schema, the description is essentially complete — it names the return unit and the input dimensions that drive it. It does not describe rounding, currency assumptions, or error behavior for unknown model ids, which are minor residual 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 100%, so the schema already documents all six parameters with defaults (requests=1, use_batch_price=false, cached_input_tokens=0). The description's mention of cached and batch inputs mirrors that schema content rather than adding format or edge-case semantics, 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 action and resource — computes "cost in US dollars of calling one model" — from clearly identified inputs (tokens in/out per request, request count). It is readily distinguishable from the sibling llm_price_lookup, which returns a unit price rather than a computed bill.

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 routes the agent to llm_price_lookup when the model id is uncertain, which is genuinely useful sequencing guidance. It does not, however, address when to prefer this over llm_cheapest_models or any exclusion conditions, so it stops short of full when/when-not coverage.

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 readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely non-redundant behavior: the return payload composition (cached and batch prices, context window, checked date) and the freshness model ('refreshed every morning'), which tells the agent how stale results can be.

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 dense sentences: the first front-loads the return contract and units, the second covers query semantics and refresh cadence. No filler, no restatement of the title, and the most decision-relevant information (what you get, how to search) comes first.

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 carries the return-value burden and does it well, listing units and the fields returned. Low complexity (2 params, read-only, no nesting) means little else is needed; only edge behavior such as no-match results or how multiple matched models are ordered/truncated is left unspecified.

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 the baseline is 3, with the schema already defining both params including the 1-25 limit range and default of 8. The description goes beyond that by giving example query strings that clarify the fuzzy matching semantics of 'query' (name vs id vs provider), which the schema does not convey.

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 names a specific verb and resource ('Today's API price for one or more large language models, in US dollars per million tokens') and enumerates what comes back (cached/batch prices, context window, checked date). It is unambiguous on its own, but it never contrasts itself with the closely related siblings llm_cheapest_models and llm_cost_estimate, leaving that differentiation to inference.

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 gives practical query guidance with concrete examples ('claude sonnet', 'gpt', 'gemini flash', 'deepseek'), which implies how to call it. However, it never states when to prefer this over llm_cheapest_models or llm_cost_estimate, nor any exclusions, so the usage boundary is only implied.

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?

Goes well past the annotations by disclosing the ownership/privacy model (sign-in link goes to the person's email, 'never returned to you') and a documented fallback return value. It does not address the non-idempotent behavior declared by idempotentHint=false (whether repeat calls create duplicate pages), which is the one behaviorally significant gap for a write 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 purpose and the call precondition are front-loaded, and the fallback behavior is placed last where it belongs. It is four fairly dense sentences; the enumeration of what the page holds ('first steps, what the work costs them today, what Alexey Shurov would need') is slightly decorative 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?

With no output schema, the description carries the return-value burden and handles it: the sign-in link is never returned, and result_url is returned instead when email delivery is off. The only missing piece is what the agent gets back on the normal success path (i.e., confirmation versus a page handle).

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 already 100%, so the baseline is 3. The description adds meaning beyond the schema by naming where task_codes come from (tasks already scored with score_automation_candidate) and reinforcing that the email must be the person's own and consented to.

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 artifact ('makes a private plan page on shurco.ai') plus the input source ('tasks already scored with score_automation_candidate'). This clearly separates it from read_plan_page and ask_alexey_on_plan_page without the agent needing to open any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit gating condition: 'Call this only when the person has asked for the page and given you their own email address.' It also names the fallback path (result_url) when email delivery is unavailable, so the agent knows both when to call and what to expect if the primary route is blocked.

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, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description nonetheless adds useful behavior the annotations cannot express: the page is private, access requires the owner's own link, and the output is rendered as plain text. It does not mention failure behavior if the link is invalid or expired.

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?

Four short sentences, front-loaded with purpose and output format before the input constraint. Every sentence carries a distinct instruction and none is redundant 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 single-parameter, read-only tool with full annotation coverage and no output schema, the description covers purpose, output form, and the one non-obvious access constraint. Nothing an agent needs to invoke 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 description coverage is 100%, so the baseline is 3, and the description genuinely adds meaning beyond it: the link belongs to the person, only they have it, it must be pasted by them, and it must never be guessed. That is substantive constraint semantics rather than a restatement of the schema text.

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 (Reads) and resource (a private plan page on shurco.ai) plus the output form (as plain text), so the agent knows what it gets back. It does not explicitly contrast itself with the sibling open_plan_page, which is the closest alternative, so an agent must infer the difference rather than being told.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear situational framing ('so you can explain it to the person it belongs to') and a concrete operational rule for obtaining the input link (ask the person to paste it, never guess). It stops short of naming when to prefer read_plan_page over open_plan_page or ask_alexey_on_plan_page.

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 cover safety (readOnly, idempotent, non-destructive, closed-world), so the burden is light, and the description adds genuine context beyond them: the result_url return value, that it opens on shurco.ai, that it can be forwarded to Alexey Shurov, and the four-call practical limit. It does not mention latency, auth, or whether repeated calls for the same task are deduplicated.

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?

Purpose and sequencing are front-loaded in the first two sentences, with the return value, forwarding option, and call limit following in order. Roughly 100 words with only mild padding in the 'what helps, what to watch and what to do next' phrasing. No sentence is wasted outright.

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, and the description compensates by naming the result_url and what the person sees at the other end, plus prerequisites and call limits. For a 7-parameter scoring tool this is close to sufficient; the only gap is any guidance on invalid or out-of-range answers from the person.

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 each enum value is already spelled out with the exact question to ask, so the schema does the heavy lifting. The description only refers the agent to 'the five questions in the input fields' without adding format or edge-case semantics (e.g. how to pick kind, or what to do with hours_per_week estimates). Baseline 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?

States a specific verb and resource: it scores one repetitive business task, on a 14-point basis, for suitability as a first AI automation, and returns plain-language advice. That is unmistakably clearer than a tautology. It never names or contrasts with the closest sibling, automation_roi, so the sibling-differentiation criterion for a 5 is not met.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit protocol: ask the five input questions conversationally first, then call; call once per task, up to four tasks, to compare. This is real when-to-use guidance rather than implication. It still offers no when-not-to-use case or alternative tool (e.g. automation_roi) for situations this scorer does not fit.

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, openWorldHint=false, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context by stating that results include titles, summaries, and links, which is valuable because there is no output 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 with the core action and scoped content types, followed by a useful topic list and return format. It is appropriately sized with no filler, though the topic enumeration is long and could be considered slightly list-heavy rather than maximally tight.

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 search tool with full schema coverage, rich annotations, and no output schema, the description is nearly complete: it names the content sources, topic areas, and return fields. The main remaining gap is that it does not clarify when to prefer this tool over sibling tools, but that is captured separately under usage guidelines.

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 input schema already documents both the query and limit parameters, including the query example and the 1-to-15 limit range. The description adds no additional parameter syntax, format, or constraint details beyond what the schema provides, making the baseline score of 3 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: it searches guides, playbooks, free tools, client results, and field notes on shurco.ai. It also lists topic areas and return fields, so the agent knows what the tool does. However, it does not explicitly distinguish this tool from siblings like ask_alexey_on_plan_page or read_plan_page.

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 explicit when-to-use guidance, no when-not-to-use guidance, and does not name any alternative sibling tool. Topic examples imply the tool is for finding shurco.ai content on those subjects, but the agent must infer that context without direct routing instructions.

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