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AdvaitR7

Firecrawl MCP Multiple Keys

by AdvaitR7

firecrawl_search_feedback

Improve search quality and earn a credit refund by submitting structured feedback on results, including rating, valuable sources, and missing content.

Instructions

Send structured feedback on a previous firecrawl_search result. Call this immediately after a search where you used the results so we can improve search quality and refund 1 credit (search costs 2).

Pass the searchId returned by firecrawl_search (the id field on the response) and tell us:

  • rating — overall result quality: good, partial, or bad.

  • valuableSources — which result URLs were actually useful, and a short reason why.

  • missingContentthe most important field. An ARRAY of specific pieces of content you expected to find but didn't. One entry per missing piece, each with a short topic and an optional longer description. Examples: {"topic":"enterprise pricing","description":"no pricing tier table for the Enterprise plan was returned"}, {"topic":"API rate limits"}, {"topic":"comparison vs competitors"}. Be specific — these aggregate across teams and tell us what to index next. Do not pack multiple topics into one entry.

  • querySuggestions — how the query or response shape could be improved (e.g. "would have liked official docs first", "should boost github.com").

Substantive-feedback requirement (zero-effort feedback is rejected with HTTP 400):

  • good — must include at least one valuableSources entry

  • partial — must include valuableSources or at least one missingContent entry

  • bad — must include at least one missingContent entry or querySuggestions

Time window: Feedback must be submitted within ~2 minutes of the search. Beyond that, the call returns HTTP 409 with feedbackErrorCode: "FEEDBACK_WINDOW_EXPIRED" — do not retry, just move on. Same goes for any 4xx response: do not retry-loop.

Behaviors:

  • Idempotent per searchId. Re-submitting for the same id returns alreadySubmitted: true with creditsRefunded: 0.

  • Refund only applies to billable searches; preview teams are blocked.

  • Failed searches cannot receive feedback (the search itself already returned an error you can act on).

  • Daily refund cap (per team, per UTC day, default 100 credits). Once a team's creditsRefundedToday reaches dailyRefundCap, the response returns dailyCapReached: true with creditsRefunded: 0. The feedback is still recorded for search-quality improvement — only the credit refund is gated. Stop calling this tool for the rest of the UTC day when you see dailyCapReached: true.

When to call: Right after processing a search result. If the result didn't help, send rating bad with a clear missingContent — that is just as valuable as a good rating.

Usage Example (good rating with valuable sources + missing content):

{
  "name": "firecrawl_search_feedback",
  "arguments": {
    "searchId": "0193f6c5-1234-7890-abcd-1234567890ab",
    "rating": "good",
    "valuableSources": [
      { "url": "https://docs.firecrawl.dev/features/search", "reason": "Most up-to-date description of /search." }
    ],
    "missingContent": [
      { "topic": "Pricing for the search endpoint", "description": "No pricing tier table for /search specifically." },
      { "topic": "Rate limits", "description": "Per-team RPS for /search not documented." }
    ],
    "querySuggestions": "Boost docs.firecrawl.dev for queries that mention 'firecrawl'"
  }
}

Usage Example (bad rating, what was missing):

{
  "name": "firecrawl_search_feedback",
  "arguments": {
    "searchId": "0193f6c5-1234-7890-abcd-1234567890ab",
    "rating": "bad",
    "missingContent": [
      { "topic": "Recent benchmarks", "description": "All results were >12 months old." },
      { "topic": "Comparison vs Algolia" }
    ]
  }
}

Returns: { success, feedbackId, creditsRefunded, creditsRefundedToday, dailyRefundCap, dailyCapReached?, alreadySubmitted?, warning? } JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ratingYes
searchIdYes
missingContentNo
valuableSourcesNo
querySuggestionsNo
Behavior5/5

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

Extensively covers behavioral details: idempotency per searchId, refund conditions, daily cap, time window with specific HTTP errors, and restrictions. These go far beyond the annotations (readOnlyHint false, destructiveHint false, openWorldHint true) to fully inform the agent.

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 description is well-structured with sections, headings, and examples, making it easy to parse. It could be slightly more concise by trimming some redundant explanations, but overall it's clear and front-loaded.

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 complex input schema and no output schema, the description provides complete context: return value format, error handling (time window, cap, idempotency), and edge cases. No gaps remain for an agent to operate correctly.

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

Parameters5/5

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

With schema coverage at 0%, the description fully compensates by explaining each parameter in detail, including constraints (e.g., maxItems, length limits) and inter-field dependencies (e.g., rating requirements for valuableSources and missingContent). Examples further clarify usage.

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 sends structured feedback on a previous firecrawl_search result, specifying the action, resource, and context. It distinguishes from other sibling tools like firecrawl_feedback by focusing on search results.

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?

Provides explicit usage guidance: call immediately after a search, within a 2-minute window, and details per-rating requirements. Also explains when not to call (e.g., after cap reached, failed searches) and non-retry behavior for 4xx errors.

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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