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LLM API Pricing and Automation Tools by shurco.ai

Open a private plan page for a person

open_plan_page

Makes a private plan page on shurco.ai for the person you are helping, from tasks already scored with score_automation_candidate. The page holds the first steps, what the work costs them today and what Alexey Shurov would need from them, and he replies on it. Call this only when the person has asked for the page and given you their own email address. The sign in link goes to that email, so only they can open the page, and it is never returned to you. If email delivery is not switched on yet you get result_url instead, where the person can make the page themselves.

Input Schema

NameRequiredDescriptionDefault
emailYesThe person's own work email. Ask for it and confirm they want the page.
task_codesYesOne code per task, the part after t= in result_url, for example orders.20.2.3.2.1.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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.

Conciseness4/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines5/5

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.

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