Firecrawl MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with minimal overlap. The descriptions explicitly differentiate use cases (e.g., scrape vs. batch_scrape vs. extract, map vs. crawl vs. search), and the 'Best for'/'Not recommended for' sections prevent confusion. Tools like generate_llmstxt and deep_research serve unique specialized functions.
Naming Consistency5/5
All tools follow a consistent firecrawl_verb_noun naming pattern (e.g., firecrawl_scrape, firecrawl_extract, firecrawl_generate_llmstxt). The verb-noun structure is uniform throughout, with no mixing of conventions or styles, making the set highly predictable and readable.
Tool Count5/5
With 8 tools, the count is well-scoped for a web content extraction and research server. Each tool addresses a specific aspect of the domain (mapping, crawling, scraping, extracting, searching, researching, status checking, and file generation), with no obvious redundancy or missing core functionality.
Completeness5/5
The toolset provides comprehensive coverage for web content operations, including discovery (map, search), extraction (scrape, extract, crawl), advanced processing (deep_research, generate_llmstxt), and job management (check_crawl_status). There are no apparent gaps; agents can handle full workflows from URL discovery to structured data extraction.
Average 4.1/5 across 8 of 8 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions that the tool returns 'Status and progress of the crawl job, including results if available,' which gives some behavioral insight into output content. However, it lacks details on error handling, rate limits, authentication needs, or whether this is a read-only operation, which are critical for a status-checking tool in a crawl system.
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 front-loaded with the core purpose in the first sentence, followed by a usage example and return details. It's relatively concise at three sentences, with no redundant information. However, the example could be integrated more seamlessly, and some structural elements like markdown formatting are present but not excessive.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return values but lacks context on integration with sibling tools, error scenarios, or operational constraints. This leaves gaps that could hinder an agent's ability to use the tool effectively in a workflow.
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?
The input schema has 100% description coverage, with the 'id' parameter documented as 'Crawl job ID to check.' The description doesn't add any meaningful semantic details beyond this, such as format examples or validation rules. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to heavily.
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 clearly states the tool's purpose as 'Check the status of a crawl job,' which is a specific verb+resource combination. However, it doesn't explicitly differentiate this status-checking tool from its siblings like firecrawl_crawl or firecrawl_scrape, which likely perform different operations on crawl jobs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It includes a usage example but doesn't specify prerequisites (e.g., after initiating a crawl with firecrawl_crawl) or contrast it with other tools that might handle crawl-related tasks, leaving the agent to infer context from sibling tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It describes what the tool does (generates files) and what it returns, but lacks details on behavioral traits like rate limits, authentication needs, error conditions, or whether it performs web crawling/analysis. The description doesn't contradict annotations (none exist), but provides only basic operational context.
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 clear sections (Best for, Not recommended for, Arguments, Prompt Example, Usage Example, Returns). It's appropriately sized for a 3-parameter tool, though the usage example could be more concise. Most sentences earn their place by providing distinct information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides adequate context about what the tool does and when to use it, but lacks details about behavioral characteristics, error handling, or what the return format looks like beyond 'LLMs.txt file contents.' For a tool that presumably crawls websites and generates files, more operational context would be helpful.
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%, so the baseline is 3. The description adds minimal value beyond the schema: it repeats parameter names and provides a usage example, but doesn't explain parameter interactions or provide additional semantic context. The 'Arguments:' section essentially restates what's in the schema.
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: 'Generate a standardized llms.txt (and optionally llms-full.txt) file for a given domain.' It specifies both the verb ('generate') and resource ('llms.txt file'), and distinguishes from siblings by noting this is for 'machine-readable permission guidelines for AI models' rather than general extraction or research.
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 explicit guidance with 'Best for:' and 'Not recommended for:' sections, clearly stating when to use this tool versus alternatives. It distinguishes this from 'General content extraction or research,' which helps differentiate from sibling tools like firecrawl_extract or firecrawl_deep_research.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explains the core behavior (LLM-based extraction) and mentions support for cloud/self-hosted LLMs, but lacks details on rate limits, authentication requirements, error handling, or what happens with invalid URLs/schemas. The description doesn't contradict annotations (none exist), but could provide more operational context.
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 clear sections (Best for, Not recommended, Arguments, Examples). It's appropriately sized for a 7-parameter tool. However, the prompt and usage examples are quite detailed, making it slightly verbose. Every section serves a purpose, but some redundancy exists between the argument list and schema descriptions.
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?
For a complex extraction tool with 7 parameters (including nested schema) and no output schema, the description does well. It explains the tool's purpose, when to use it, provides parameter guidance, and shows complete examples. The main gap is lack of output format details beyond 'Returns: Extracted structured data as defined by your schema' - more specificity about the return structure would help.
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%, so the schema already documents all parameters. The description lists parameters but adds minimal semantic value beyond what's in the schema descriptions. It provides a usage example showing how parameters work together, which adds some context, but doesn't explain trade-offs or relationships between parameters like 'allowExternalLinks' and 'enableWebSearch'.
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: 'Extract structured information from web pages using LLM capabilities.' It specifies the verb (extract), resource (structured information from web pages), and method (LLM capabilities). It also distinguishes from sibling tools by explicitly stating when not to use it (vs. 'scrape' for full content).
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 explicit guidance with 'Best for:' and 'Not recommended for:' sections, naming specific alternatives ('use scrape'). It clearly defines the appropriate context (extracting specific structured data) and when to avoid it (when needing full content or not looking for structured data).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool can 'optionally extract content from search results' and shows scrapeOptions in the usage example, which adds useful context about content extraction capabilities. However, it doesn't disclose important behavioral aspects like rate limits, authentication requirements, error handling, or pagination behavior, leaving significant gaps for a tool with 8 parameters.
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 clear sections (Best for, Not recommended for, Common mistakes, Prompt Example, Usage Example, Returns) that make it easy to scan. While comprehensive, some sections like the detailed JSON example could be considered verbose. Overall, most content earns its place by providing practical guidance, though it could be slightly more concise.
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 tool's complexity (8 parameters, nested objects, no output schema, no annotations), the description does a good job covering usage context and differentiation from siblings. It explains what the tool returns ('Array of search results with optional scraped content'), which partially compensates for the lack of output schema. However, for a web search tool with extraction capabilities, more behavioral context (like rate limits or error conditions) would improve completeness.
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%, so the schema already documents all parameters thoroughly. The description adds minimal parameter semantics beyond what's in the schema - it mentions 'optional extraction' which relates to scrapeOptions, and the usage example shows some parameters in context. This meets the baseline expectation when schema coverage is high, but doesn't provide significant additional value.
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 as 'Search the web and optionally extract content from search results,' which is a specific verb+resource combination. It explicitly distinguishes this tool from siblings like scrape, map, and crawl by stating when those alternatives should be used instead, making the differentiation clear and actionable.
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 explicit guidance with dedicated sections: 'Best for' (finding information across multiple websites when the source is unknown), 'Not recommended for' (when the website is known, recommending scrape, map, or crawl), and 'Common mistakes' (avoiding crawl/map for open-ended questions). This comprehensive coverage clearly defines when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a research tool that may take time ('deep research can take time'), involves multiple sources and LLM analysis, and returns a final analysis. However, it lacks details on potential errors, rate limits, or authentication needs, which could be relevant for an AI 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 like 'Best for:', 'Arguments:', and examples, making it easy to scan. It's appropriately sized, with each sentence adding value, though it could be slightly more concise by integrating some details more tightly.
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 tool's complexity (deep research with multiple parameters) and no output schema, the description does a good job explaining the purpose, usage, and returns ('Final analysis generated by an LLM'). It covers key aspects but could benefit from more detail on output structure or error handling to be fully complete.
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%, so the schema already documents all parameters. The description adds default values (e.g., 'default: 3' for maxDepth) and a prompt example, providing some extra context, but doesn't significantly enhance meaning beyond what's in the schema. This meets the baseline for high schema coverage.
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 'conduct[s] deep web research on a query using intelligent crawling, search, and LLM analysis.' This specifies the verb ('conduct deep web research'), resource ('query'), and method ('crawling, search, and LLM analysis'), distinguishing it from siblings like 'scrape' or 'search' by emphasizing depth and analysis.
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 explicitly provides 'Best for:' and 'Not recommended for:' sections, detailing when to use this tool (complex research questions requiring multiple sources) versus alternatives (e.g., use 'scrape' for specific information from known pages). It also mentions time considerations, offering clear guidance on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by explaining what the tool returns ('Array of URLs found on the site'), its discovery scope ('indexed URLs'), and behavioral constraints (e.g., it's for discovery, not for content). It could improve by mentioning potential limitations like rate limits or authentication needs.
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 well-structured with clear sections (Best for, Not recommended for, Common mistakes, Prompt Example, Usage Example, Returns), front-loaded with key information, and every sentence adds value without redundancy.
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 tool's complexity (6 parameters, no output schema, no annotations), the description is mostly complete, covering purpose, usage, examples, and return values. It could be slightly improved by adding more context on error handling or performance expectations, but it's largely sufficient.
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%, so the schema already documents all 6 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, but it meets the baseline of 3 since the schema does the heavy lifting.
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 with specific verbs ('map', 'discover') and resources ('indexed URLs on the site'), and explicitly distinguishes it from sibling tools like 'scrape', 'batch_scrape', and 'crawl' by explaining what it's not for.
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 explicit guidance with 'Best for' and 'Not recommended for' sections, naming specific alternatives (scrape, batch_scrape) and clarifying when not to use it (e.g., when you already know the URL). It also addresses common mistakes like using crawl instead of map.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (scrapes content from a URL with advanced options), mentions output formats (markdown, HTML, etc.), and includes practical examples and common pitfalls. However, it lacks details on potential side effects like rate limits, authentication needs, or error handling, which are important for a scraping tool.
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 well-structured and front-loaded with a clear purpose statement, followed by organized sections like 'Best for:', 'Not recommended for:', 'Common mistakes:', 'Prompt Example:', 'Usage Example:', and 'Returns:'. Each sentence adds value without redundancy, making it efficient and easy to scan for key information.
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 tool's complexity (13 parameters, nested objects, no output schema, and no annotations), the description does a good job of providing context. It covers purpose, usage guidelines, examples, and return formats, which helps an agent understand when and how to use it. However, without annotations or output schema, it could benefit from more details on behavioral traits like error handling or performance constraints.
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?
The schema description coverage is 100%, so the schema already documents all 13 parameters thoroughly. The description adds minimal parameter-specific information beyond the schema, such as implying the 'url' parameter in the prompt example and mentioning output formats. This meets the baseline of 3, as the schema does the heavy lifting, but the description doesn't significantly enhance parameter understanding.
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 as 'Scrape content from a single URL with advanced options,' which is a specific verb+resource combination. It explicitly distinguishes from siblings by naming alternatives like 'batch_scrape' for multiple pages, 'search' for unknown pages, and 'extract' for structured data, making the distinction clear and actionable.
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 explicit guidance with sections like 'Best for:' (single page content extraction when the page is known), 'Not recommended for:' (multiple pages, unknown pages, structured data), and 'Common mistakes:' (using it for a list of URLs). It names specific sibling tools as alternatives, offering clear when-to-use and when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden of behavioral disclosure and excels at this. It clearly explains that this is an asynchronous operation, warns about potential token limit issues, mentions that crawling can be slow, describes the return format (operation ID), and explains the need to use a separate tool (firecrawl_check_crawl_status) to check progress. This provides rich behavioral context beyond basic functionality.
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 exceptionally well-structured with clear sections (Best for, Not recommended for, Warning, Common mistakes, Prompt Example, Usage Example, Returns) that make information easy to find. Every sentence earns its place by providing essential guidance, warnings, or examples without redundancy. The formatting with bold headers and code blocks enhances readability.
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 tool's complexity (12 parameters, asynchronous operation, no output schema, no annotations), the description provides comprehensive context. It explains the asynchronous nature, return format, progress checking mechanism, performance considerations, token limit warnings, and sibling tool relationships. For a complex tool with no annotations or output schema, this description provides all necessary contextual information.
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?
With 92% schema description coverage, the baseline would be 3, but the description adds significant value through the usage example that shows practical parameter combinations and the prompt example that contextualizes parameter use. While it doesn't explain individual parameters, it provides semantic guidance about how parameters work together in real scenarios, elevating the score above baseline.
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 with specific verbs ('starts an asynchronous crawl job', 'extracts content from all pages') and distinguishes it from siblings by explicitly mentioning when to use scrape instead. The opening sentence provides a complete, unambiguous statement of what the tool does.
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 excellent usage guidance with explicit 'Best for' and 'Not recommended for' sections, names specific alternative tools (scrape, map + batch_scrape), and includes a 'Common mistakes' section with concrete examples. This gives comprehensive context about when to use this tool versus alternatives.
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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