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Request a fixed-price quote from a human

request_quote

Send a physical-world task to a human in the Czech Republic for screening and a fixed-price quote. Not an order and not binding: nothing is charged and nothing is made until you accept the quote. A human answers by e-mail within 24 hours. Returns quote_id, access_token, response_due_at and nda_url. Store the access_token — it is the only way to read the quote back. No authentication.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYes
agentNo
clientYes
allow_anonymised_exampleNoConsent to publish this task as an anonymised example (no names, addresses or personal data), under the confidentiality section of the terms of service. Default false; can be withdrawn.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

The description adds substantial behavior beyond annotations: non-binding quote request, human response within 24 hours, returned fields, the access_token requirement, and 'No authentication.' This meaningfully clarifies the mutation semantics without contradicting readOnlyHint=false or destructiveHint=false.

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

Conciseness5/5

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

The description is dense but efficient: it front-loads the core function and then covers binding status, response time, return values, access token handling, and authentication in four sentences. Every sentence contributes useful information with no filler.

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?

Given the nested schema and absence of an output schema, the description reasonably covers the full quote workflow: what happens, when the quote arrives, what fields are returned, and how to access the quote later. Minor gaps like the exact email destination for file attachments remain, but the overall context is sufficiently complete.

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

Parameters2/5

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

Schema description coverage is only 25%, so the description must compensate for the four top-level parameters. It does not: task, client, agent, and allow_anonymised_example are not explained at the top level. The mention of 'physical-world task' hints at task, but no meaningful semantic guidance is added for the actual parameters.

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?

The description states a clear verb and resource: 'Send a physical-world task to a human... for screening and a fixed-price quote.' It distinguishes itself from siblings by emphasizing the human-screening/quote-request nature, which is clearly different from get_status and list_services.

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

Usage Guidelines4/5

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

It gives clear context for when to call this tool: when a non-binding fixed-price quote is needed before any work or payment. The phrase 'Not an order and not binding... nothing is charged and nothing is made until you accept the quote' functions as a useful when-not/exclusion. It does not explicitly name alternative tools for ordering, but the context is clear enough.

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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