signal-bureau

get_quote

Feed in your requirements — free-form concerns, questions, whole areas of interest — and get back your requirements grouped and disambiguated into well-formed watched topics, then checked BY CODE, topic by topic, against what we actually read: a topic is offered as a Watch 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 answer 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, 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 offered. 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 requirements are resolved in batches, so a requirement the desk cannot place does not sink the rest: partial: true and unresolvedRequirements name what it could not place and why — never a topic invented to cover a gap. Nothing here is a price: Watches are free. Configure, save and keep a Watch at no charge: no card, no end date. Up to 5 saved Watches per verified email or key. Need more, or something built around your own question? Ask us. Pass the covered topics to save_watch (domain + topics) to keep them. Needs no contact details. Real model work: ~10 per day per network address (shared egress shares the allowance).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "requirements": "<requirements>"
    },
    "name": "get_quote"
  }
}

Input Schema

Name	Required	Description	Default
`requirements`	Yes	What 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.

First observedAug 17, 2026

TDQS

A3.9/5.0

Behavior5/5

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

Rich disclosure beyond the annotations: it explains the coverage object's state values (covered, gap, cannot-check with reason), that the recency bar is derived per-topic and returned in coverage.basis.bars.liveDays, and that partial results surface under partial/unresolvedRequirements rather than inventing a topic. It also discloses quota ('~10 per day per network address, shared egress shares the allowance'), the 5-saved-Watch cap per email/key, and that the operation is free — none of which the annotations convey.

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?

Roughly 250 words for a one-parameter tool, mixing behavioral facts, output field names, pricing reassurance ('Nothing here is a price: Watches are free'), and rate limits into a single dense block. Key operational routing (save_watch, propose_topic) is buried mid-paragraph and the ALL-CAPS emphasis ('BY CODE', 'ONLY', 'BOTH') adds noise rather than scannability.

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?

With no output schema, the description carries the return-shape burden itself and does so thoroughly: coverage fields, coverageRequests, coverage.summary, billing.expansion, partial/unresolvedRequirements. Combined with quota and follow-up-call guidance, an agent has enough to call this correctly and interpret the response.

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 100% and the single parameter is already documented as 'one blob of text or bullet points; the desk does the grouping'. The description reinforces that free-form input (concerns, questions, whole areas of interest) is acceptable and adds that large requirements are resolved in batches, which tells the agent it need not pre-split input. Marginal but real added meaning.

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 concrete transformation: free-form requirements in, grouped and disambiguated watched topics out, code-checked for coverage. It distinguishes itself from siblings by naming save_watch and propose_topic as the downstream/alternative calls. The core purpose is clear, though it is buried inside a long paragraph of prose rather than stated up front.

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?

There is implicit routing ('Pass the covered topics to save_watch (domain + topics) to keep them' and gaps carry 'the free propose_topic call'), which tells an agent what to do next. However, there is no explicit when-to-use-this-versus-sibling guidance (e.g. vs get_calibration, start_here, or get_topic_record), and the prerequisite/position in the workflow is only inferable. Adequate but with a clear gap.

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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