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get_quote

Feed in your requirements — free-form concerns, questions, whole areas of interest — and get back an enumerated quote: your requirements intelligently grouped and disambiguated into well-formed watched topics, then checked BY CODE, topic by topic, against what we actually read: a topic is priced ($20/topic/month at the opening rate) ONLY when a desk, a Watch we keep or a standard Watch reads it from sources with a recent item. How recent is not a fixed number: the live bar is derived from the topic's own cadence and served with each quote in coverage.basis.bars.liveDays, beside coverage.rule, which states it in words. Every topic carries coverage — its state (covered, gap, or cannot-check with the reason), the sources, how many and how fresh. Gaps come back under coverageRequests at $0, never billed, each with the free propose_topic call that asks us to start keeping it; coverage.summary says it in one line. A model proposes the topics; it never decides what is sold. The count is disclosed in BOTH directions: overlaps are merged, broad requirements are split into the separate daily reports they actually need, and billing.expansion states the requirement-to-topic ratio outright so a split is as visible as a merge. Large baskets are resolved in batches, so a requirement the desk cannot place does not sink the rest: you get a quote for everything it could place, partial: true, and unresolvedRequirements naming what it could not and why — never a topic invented to cover a gap. A quote is not a charge, needs no contact details, and holds for 30 days. Real model work: ~10 quotes/day per network address (shared egress shares the allowance).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requirementsYesWhat you want watched, in your own words — one blob of text or bullet points; the desk does the grouping

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior5/5

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

With only weak annotations (readOnlyHint=false, idempotentHint=false), the description carries real weight and delivers: $20/topic/month only when actually read, gaps priced at $0 and never billed, 30-day quote validity, no contact details required, partial results with unresolvedRequirements rather than invented topics, and a concrete rate limit of ~10 quotes/day per network address. That is exactly the kind of behavior annotations cannot express.

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

Conciseness2/5

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

The content is valuable but the packaging is poor: one enormous opening sentence plus a dense run of parenthetical clauses and backtick field names makes it hard to scan, and key facts (price, rate limit, 30-day hold) are scattered rather than front-loaded. Much of it earns its place, but the structure does not.

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

Completeness5/5

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 explain the return shape, and it does thoroughly: coverage state per topic, coverage.basis.bars.liveDays, coverage.rule, coverageRequests at $0, coverage.summary, billing.expansion, partial, and unresolvedRequirements. For a complex, non-idempotent quoting tool, an agent has everything needed to call and interpret it.

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

Parameters3/5

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

Schema description coverage is 100% for the single `requirements` parameter, so the schema already explains that it is free-form text and the desk does the grouping. The description restates this ('free-form concerns, questions, whole areas of interest') but adds no syntax, format, or limit details beyond the schema.

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

Purpose4/5

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

The description states a specific verb and resource: feed in requirements, get back an enumerated quote of grouped, code-checked watched topics with prices. It is clearly distinguishable from siblings like ask, get_answer, or get_topic_record. It loses a point only because the core statement is buried inside a very long first sentence rather than stated crisply upfront.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: you call it when you want to know what watching your requirements would cost, and it names propose_topic as the follow-up for gaps. However, there is no explicit when-not-to-use, no statement of prerequisites, and no direct routing against alternatives such as ask or create_order.

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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