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Estimate a keyword or a change

keyword_estimate
Read-only

Price a new keyword (no id, the arguments of keyword_create) or a change to one (an id, the arguments of keyword_update) without writing anything. valid=false carries the refusal the write would give: the status, the code and the sentence naming the fix. valid=true carries monthlyCredits in total, per Source and per search, firstScanCredits when backfill is true (the most the first scan can cost, see backfill), the plan allowance and the credits left, and for a change the monthly difference and resetSearches, the searches it would start over. A new keyword also carries volume when it names a free Source (Reddit, Bluesky, Hacker News, GitHub, Stack Overflow, Mastodon, Lemmy, RSS): the estimated mentions a month on the Sources in measuredOn, and a bucket from none to over_10k, free and never charged. Show the person monthlyCredits before any write, then pass estimateToken to that write with the same arguments. What each Source needs in a search is in the description of searches[].overrides. A topic keyword (subjectRole topic) sent without globalCriteria.match is created with match word, the phrase as written; send match contains to widen it, or platform for the platform's own matching. For a topic, also send sentimentEnabled true and an aiStep with a boolean problem_fit field (true only when the author has the problem themselves): the opportunity filter of list_mentions reads it. Costs 0 credits and writes nothing. Returns what the write would cost a month, computed by the functions that charge it, and the estimateToken the write requires.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoAbsent: price a new keyword, with the arguments of keyword_create. A keyword id: price a change, with the arguments of keyword_update.
nameNo
colorNoThe keyword colour in the dashboard. Left out on create, an unused one is picked.
rulesNoReplaces the list. Copy the shape from get_keyword on an existing keyword.
aiStepNoYour own question asked of every mention, with the fields to fill: {"instruction": one or two sentences, 800 characters at most, "schema": 1 to 12 flat fields, each {"type": "string" | "number" | "boolean" | "enum", "description", "values" for an enum, "maxLength" for a string}}, no nesting and no arrays. The answer arrives on each event as analysis. Costs 1 credit per 25 items, on top of the check rate. Delivery waits up to 120s for it; on timeout the event is still delivered, with the answer null and a status saying why. null removes it.
sourcesNoThe Sources this keyword polls, one entry each; a single entry is a keyword that polls one Source. Each enabled search is one check at its rate. What each Source needs is in the description of searches[].overrides.
aiPresetNo
aiPromptNo
backfillNotrue runs a first scan right away, so the keyword is not empty on day one. What it finds fires no webhook, since a first scan is context and not news, and comes back marked seeded. A first scan is one search per enabled Source at that Source's check rate, except where a Source is billed per result: on X it is 5 credits plus 3 per result returned, at most 65 credits for its page of 20. So a first scan that includes such a Source is quoted at its ceiling: say "up to". Needs the nephia:spend permission. AI answers never backfills.
channelsNoReplaces the delivery channels attached to the keyword.
scheduleNoAn activation window with ISO instants. null keeps the keyword always on.
aiEnabledNoSort mentions into buckets.
muteRulesNoReplaces the list. A plain string mutes that word.
spikeAlertNoThe alert a new keyword starts with. null starts with none. Free.
vipAuthorsNoReplaces the list. A plain string is a handle.
webhookUrlNoA publicly reachable HTTPS endpoint the person controls: new mentions are POSTed there. Never invent this URL, ask the person for it. Left out, the keyword records what it catches without pushing it, and you read it with list_mentions, get_keyword_results or get_keyword_events. null removes the webhook.
subjectRoleNoWhat the keyword is about: your own brand, a competitor's, or a topic. A new topic without globalCriteria.match is created with match word.
webhookModeNoall delivers every new mention; rules delivers only what a rule routes.
globalCriteriaNoThe criteria every search inherits: market (required, e.g. "us") and query or terms. AI answers reads no search text, so a keyword of prompts only still names a market.
coBrandsEnabledNoAI answers only: also read each answer for the other brands it names, charged per answer on top of the run.
relevanceContextNoOne sentence the AI reading uses to judge relevance, such as "Acme is a cloud storage company, not the cartoon." null clears it.
sentimentEnabledNoRead every mention for sentiment and intent.
refreshIntervalSecondsNoDefault seconds between checks, for every Source without its own interval. The plan and each Source set a floor; the estimate names the band when a value is outside it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / subjectRole / description
      Previous value: -"What the keyword is about: your own brand, a competitor's, or a topic."New value: +"What the keyword is about: your own brand, a competitor's, or a topic. A new topic without globalCriteria.match is created with match word."
  2. Changed2 schema fields changed
    • addedInput schema / properties / color
      Added value: +{
      +  "description": "The keyword colour in the dashboard. Left out on create, an unused one is picked.",
      +  "enum": [
      +    "keyword-1",
      +    "keyword-2",
      +    "keyword-3",
      +    "keyword-4",
      +    "keyword-5",
      +    "keyword-6",
      +    "keyword-7",
      +    "keyword-8"
      +  ],
      +  "type": "string"
      +}
    • addedInput schema / properties / relevanceContext
      Added value: +{
      +  "anyOf": [
      +    {
      +      "maxLength": 400,
      +      "type": "string"
      +    },
      +    {
      +      "type": "null"
      +    }
      +  ],
      +  "description": "One sentence the AI reading uses to judge relevance, such as \"Acme is a cloud storage company, not the cartoon.\" null clears it."
      +}
  3. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations include readOnlyHint=true and openWorldHint=true, and the description reinforces this by stating 'Costs 0 credits and writes nothing.' It goes beyond the annotations by detailing the output structure: valid=true carries monthlyCredits, per-Source and per-search breakdowns, firstScanCredits, plan allowance, credits left, and for changes monthly difference and resetSearches. It also explains valid=false carries the refusal details. No contradictions.

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 well-organized, front-loading the core purpose and then layering specifics. Every sentence adds information—costs, output fields, free sources, topic handling, and the token requirement. Despite its length, it is efficient and well-structured for the complexity involved.

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?

Given the 23 parameters, nested objects, and no output schema, the description provides comprehensive context: it details the output fields, the conditions for free sources, the meaning of valid flags, and the backfill cost ceiling. It also references searches[].overrides for source-specific requirements, which is necessary for correct invocation. No critical information appears missing.

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 description coverage is 87%, so the schema already documents most parameters. The description adds meaningful context: the id parameter's absence/presence determines new vs change, the topic keyword behavior (match, sentimentEnabled, aiStep with problem_fit), and the backfill cost implications. This adds value beyond the schema without redundantly repeating it.

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 clearly states the tool's purpose: to price a new keyword (using keyword_create arguments) or a change (using keyword_update arguments) without writing. It distinguishes the two modes via the presence of an id and explicitly mentions that it writes nothing, which differentiates it from the write tools.

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?

The description provides a clear workflow: show the person monthlyCredits before any write, then pass estimateToken to that write with the same arguments. It also notes that valid=false carries the refusal the write would give, so the tool can be used to check validity without committing. It does not explicitly name alternative tools, but the usage context is unambiguous.

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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