Crawl4AI Complete MCP

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation3/5
Crawl, crawl_stream, and crawl_job_submit present overlapping crawling functionality, while extract and llm_job_submit both offer LLM-based extraction. These clusters create ambiguity, though most other tools are clearly distinct.
Naming Consistency3/5
Tool names mix single nouns (screenshot, pdf, html), bare verbs (extract, crawl), verb_noun forms (execute_js, crawl_stream), and noun_verb constructions (crawl_job_submit, job_status). The inconsistent pattern makes it harder to predict tool names, though all are readable and lowercase.
Tool Count5/5
15 tools for a 'Complete' MCP covering crawling, extraction, rendering, and job management is well-scoped. Each tool adds a distinct capability without unnecessary bloat.
Completeness4/5
The surface covers core workflows: fetch content, crawl (sync/stream/async), extract (sync/LLM), check job status, and retrieve artifacts. Minor gaps like job cancellation or listing jobs are absent but not critical.
Average 3/5 across 15 of 15 tools scored. Lowest: 2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 20 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does not state whether extraction is read-only, what the output looks like, whether side effects exist, or what 'pinned' means. This leaves the agent with no safety or behavior expectations.
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 a single concise sentence that front-loads the list of strategies, which is useful. However, it is sparse enough that it feels under-specified rather than purely concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has five parameters, nested objects, no output schema, and no annotations. The description fails to cover what the strategies do, how to configure parameters, or what results to expect, making it inadequate for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description adds no parameter meaning. The schema shows required fields (urls, strategy, strategy_params) but the description only enumerates potential strategy values without explaining how to populate urls or strategy_params.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description implies an extraction tool and lists supported strategies, giving some sense of function. However, it lacks a specific verb+resource structure ('extract what from where?') and does not distinguish itself from siblings like html or crawl.
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?
No guidance is provided on when to use extract versus alternative tools. The strategy list hints at different extraction types but offers no context, exclusions, or preferred scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It hints at a confirm step for administrative control but does not explain side effects, permissions, rate limits, or what 'confirm' actually does. The phrase 'explicitly confirm' implies some action requires confirmation, but the consequences of that confirmation are left entirely ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words, which is concise. However, it is under-specified and the 'or' conjunction creates structural ambiguity, making it less effective than a clearer, more explicit phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, an output schema that is not described, and 3 parameters at 0% coverage, the description carries a heavy burden. It fails to explain what the output looks like, what operations are available, or what an administrative control is. The tool appears to be a generic operation handler, but the description is missing critical context for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for 3 parameters. The description mentions 'confirm' verbally but does not map it to the confirm parameter or explain the operation values. The payload parameter is completely unexplained. This is entirely insufficient for a generic operation dispatcher with a string operation field.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action 'Read native Crawl4AI monitoring data', but immediately muddies it with 'or explicitly confirm an administrative control', which is vague and introduces an undefined second purpose. It doesn't clearly distinguish from sibling tools like job_status or artifact_info.
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?
No guidance is provided on when to use this tool versus alternatives. The description does not mention when to choose monitoring over job_status or native_api_operation, nor does it state prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of describing behavior. It only states that Markdown is fetched, without explaining rendering, config effects, output format details, or limitations. 'Fit Markdown' adds confusion rather than transparency.
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 description is very short but wastes words on the unclear 'and fit Markdown' phrase. It is under-specified rather than concise, leaving the reader with an incomplete and slightly confusing message.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with three parameters, no output schema, and no annotations, this description is inadequate. It omits any explanation of browser/crawler configs, return behavior, or relationships to sibling tools, leaving major gaps in actionable context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not compensate by explaining the url, browser_config, or crawler_config parameters. The parameter names are only minimally self-explanatory, and the config objects remain undefined.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the core action ('Fetch Markdown for a URL') but muddies it with the ambiguous phrase 'and fit Markdown,' which is not meaningful. It vaguely distinguishes from siblings like html or extract, but does not explicitly state the markdown-specific scope.
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?
No guidance is given about when to use this tool versus alternatives such as extract, html, or crawl. The description lacks any contextual cues about appropriate use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, but it only says 'submit a job,' leaving ambiguous whether it is asynchronous, what it returns, whether it triggers costs, or what side effects occur. The 'confirm' parameter in the schema suggests potential cost confirmation but is not addressed.
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 one concise sentence with no filler or unnecessary detail. It is appropriately front-loaded but somewhat too terse to convey all necessary context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given six parameters, no annotations, and minimal description, the tool's full behavior and input semantics are far from adequately captured. The presence of an output schema mitigates return-value ambiguity but does not compensate for missing parameter context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description offers no explanation for any of the six parameters (url, query, schema, confirm, provider, webhook_config). The required fields 'url' and 'query' are self-explanatory from context but not explicitly defined.
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 action (submit), the resource (Crawl4AI LLM extraction job), and the context (configured native server). The phrase 'LLM extraction job' distinguishes it from sibling crawl_job_submit, making the tool's unique role apparent.
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 explicit guidance on when to choose this tool over alternatives such as crawl_job_submit or extract. No usage context, prerequisites, or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 fails to mention whether the job is asynchronous, how results are tracked, what side effects occur, or what implications the Redis-backed native server have for the caller.
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 a single, well-formed sentence with no redundant words. It is concise and front-loaded with the core action, although it sacrifices detail for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is too sparse for a tool with five parameters and no annotations. It lacks context about how the configuration objects relate to the submission process, and even though an output schema exists, the description does not explain the overall workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%; the description does not explain any of the five parameters (urls, confirm, browser_config, crawler_config, webhook_config). It adds no meaning beyond what the schema already exposes.
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 uses a specific verb ('Submit') and a clear resource ('Crawl4AI Redis-backed crawl job'), and mentions the server context, which distinguishes this from sibling tools like crawl or crawl_stream.
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 explicit guidance on when to use this tool versus alternatives such as crawl or crawl_stream. It only states what it does, leaving usage to be inferred from the name and sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description offers no behavioral details beyond the core action. It does not disclose side effects (e.g., fetching external resources, storing files), permission requirements, or operational characteristics. This leaves the agent blind to safety and side-effect implications.
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 a single, front-loaded sentence that delivers the essential purpose without any wasted words. It is an exemplary model of conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool appears to involve significant complexity (two open-ended config objects, a crawl-and-render process), yet the description provides only the surface-level purpose. With no output schema and no annotations, this is insufficient for an agent to understand configuration options, return format, or operational constraints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema includes three parameters (url, browser_config, crawler_config) with 0% schema description coverage. The description does not explain any parameter semantics—not even the required url beyond its obvious role, and it completely ignores the optional config objects, leaving their meaning and allowed structures undocumented.
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 uses a specific verb ('Render') with a clear resource ('a URL') and output ('PDF'), and names the underlying engine (Crawl4AI). This distinctly differentiates it from sibling tools like screenshot, html, and md, which produce other output formats.
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?
The description implies the tool is for generating PDFs from URLs, which tells an agent when to choose it, but it does not explicitly mention alternatives or exclusion criteria. Sibling tools like screenshot or md could overlap for some web capture needs, but no guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits. It implies a read-only operation ('retrieve'), but does not disclose error behavior, size limits implied by 'bounded', authentication requirements, or response characteristics. This is a significant gap for a tool with no structured safety hints.
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?
A single, clear sentence that is front-loaded and contains no fluff. It efficiently communicates the core action and the key identifier format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter), but 'bounded artifact' and 'Crawl4AI artefact' are unexplained. Given there is no annotation or usage context, the description does not adequately inform an agent about what the artifact represents or how it fits into the broader tool ecosystem.
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 only provides a title and type for artifact_id, so the description's phrase 'opaque hexadecimal ID' adds crucial format information. However, it does not explain how to obtain the ID or its exact constraints, so coverage remains incomplete.
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 uses a specific verb ('retrieve') and resource ('Crawl4AI artefact'), clearly indicating this tool fetches an existing artifact by ID. It distinguishes from sibling tools that perform crawling or extraction, though 'bounded' is ambiguous and not explained.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisite steps (e.g., obtaining an artifact ID from a crawl job) or scenarios where another tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions 'validated' config fields, hinting at input validation, but does not disclose return format, side effects, rate limits, or synchronous/asynchronous behavior. This is minimal transparency.
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 a single, focused sentence with no wasted words. It is easy to parse and front-loads the core action, though it may be too terse given the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with two complex, open-ended config objects and no output schema, this one-sentence description is under-specified. It lacks information about return values, error cases, or how it differs from crawl_stream and crawl_job_submit, leaving the agent with significant ambiguity.
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 provides only property names with no descriptions (0% coverage). The description compensates by naming Crawl4AI BrowserConfig and CrawlerRunConfig, which gives the agent a key reference for what the config objects should contain. However, it does not explain the fields within these configs or the urls parameter beyond the obvious.
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 crawls one or more URLs, providing a specific verb and resource. However, it does not differentiate from sibling tools like crawl_stream or crawl_job_submit, so it stops short of full distinction.
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?
No guidance is provided on when to use this tool versus alternatives such as crawl_stream or crawl_job_submit. The description solely focuses on the action without contextual prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It does disclose that operations are authenticated and allow-listed, which are safety-relevant constraints, but it doesn't mention side effects, error behavior, or whether operations can be destructive. For a generic operation invoker, this is insufficient.
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 a single, information-dense sentence with no redundancy. It front-loads the core purpose and adds a useful constraint about not being an HTTP proxy. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although an output schema exists (so return values are covered), the description still lacks context for how to formulate operations, what parameters mean, and what constitutes an allow-listed operation. For a tool with four parameters and no schema descriptions, this is a significant gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain any of the four parameters. 'operation' is implicitly required, but payload, task_id, and artifact_id remain completely unexplained. The description adds no meaning beyond the bare parameter names.
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 function: calling an authenticated, allow-listed native-server operation, using a specific verb and resource. It also distinguishes itself from arbitrary HTTP proxying, which sets it apart from a generic proxy tool. However, it doesn't enumerate available native operations, so the scope remains somewhat open.
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?
The description gives a negative usage guideline ('never an arbitrary HTTP proxy') but does not provide positive guidance on when to choose this tool over siblings like execute_js or crawl_stream. The context is implied: use it for authenticated, allow-listed operations, but there's no explicit when-to-use or alternative comparison.
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 responsibility for behavioral disclosure. It adds useful context by stating it 'returns the completed result sequence', implying a buffered (non-streaming) behavior, and 'stdio-safe' suggests a particular output handling. However, it omits details on error handling, side effects, permissions, or rate limits, which a fully transparent description might include.
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 a single, concise sentence that front-loads the main verb ('Crawl') and directly states the output. No word is wasted, and it remains informative despite its brevity.
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 has an output schema, return values are covered. But the description does not explain the optional config parameters, the exact semantics of 'completed', error behavior, or how it differs from the sibling 'crawl'. It provides a minimal baseline but leaves significant gaps for a tool with multiple configurable parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only mentions 'multiple URLs', which maps to the 'urls' parameter, but says nothing about 'browser_config' or 'crawler_config'. The parameter names are self-explanatory to some degree, but the description adds minimal value beyond what the schema already shows.
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 'Crawl multiple URLs' and specifies the return behavior ('returns the completed result sequence'), giving a specific verb and resource. It also hints at differentiation from a sibling via 'stdio-safe streaming equivalent', though this is somewhat vague without knowing what the non-streaming equivalent does.
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?
No explicit guidance on when to use this tool versus alternatives. The phrase 'stdio-safe streaming equivalent' implies a use case but does not state it clearly, nor does it mention any exclusions or comparisons to sibling tools like 'crawl' or 'crawl_job_submit'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It mentions 'rendered' HTML, which hints at browser rendering, but does not disclose side effects, redirect handling, authentication needs, rate limits, or output formatting. This is insufficient for a network-fetching 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 a single, front-loaded sentence with no wasted words. It conveys the core purpose efficiently and is easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has three parameters (including complex config objects), no output schema, and no annotations, the description is incomplete. It does not explain what 'rendered' means in practice, what the browser_config/crawler_config options do, or the structure of the returned HTML. The description is too minimal for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only implies the 'url' parameter by saying 'for a URL', but does not explain 'browser_config' or 'crawler_config' at all. The description adds little value beyond the schema's field names and defaults.
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 function: 'Fetch rendered HTML for a URL.' It uses a specific verb ('fetch'), a specific resource ('rendered HTML'), and a target ('URL'). This distinguishes it from sibling tools like 'screenshot', 'pdf', 'md', and 'extract' by the output format.
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 does not mention scenarios, exclusions, or relations to sibling tools such as 'md' (markdown) or 'screenshot'. Users are left without context for choosing this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only mentions 'using Crawl4AI's browser runtime' but does not explain return format, side effects, authorization needs, or that this is a read-only operation.
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 a single sentence with no fluff. Every word is useful, and it is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and three parameters, yet the description gives no context about return values, config usage, or expected behavior. The minimal description is insufficient for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description provides no explanation for the three parameters (url, browser_config, crawler_config). It fails to add any meaning beyond the parameter names.
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 captures a screenshot, which is a specific verb+resource pairing. It inherently distinguishes itself from sibling tools like pdf, html, and md by the action it performs.
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 by the action 'capture a screenshot', but no explicit guidance on when to use it vs alternatives is provided, nor any exclusions or prerequisites.
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?
No annotations are provided, so the description must carry the burden. It discloses the 'bounded' nature and that arbitrary Python/shell is never accepted, which is a valuable security boundary. However, it does not mention return values, side effects, authentication, or what 'bounded' means concretely, leaving significant behavioral ambiguity.
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 a single, front-loaded sentence with no filler or redundant content. It communicates the core action and a key constraint efficiently, earning full marks for conciseness and structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 4 parameters, no output schema, and no annotations, the description is insufficient for complete understanding. It covers the tool's purpose and a security constraint but omits parameter semantics, return format, usage context, and any relationship to sibling tools, leaving significant gaps for an agent to safely select and invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% coverage with no descriptions for url, scripts, browser_config, or crawler_config. The description only vaguely implies that scripts are JavaScript, while url and configuration objects remain unexplained. It fails to compensate for the lack of schema descriptions.
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 specifies the action: execute bounded page JavaScript via Crawl4AI. This distinguishes it from sibling tools like crawl, extract, and html, which focus on fetching or parsing content rather than running scripts.
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?
The intended use case is implied by the phrase 'Execute bounded page JavaScript,' but there is no explicit guidance on when to choose this over other tools or any stated exclusions beyond rejecting arbitrary Python/shell. It does not name alternative tools or provide decision criteria.
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, the description carries the full burden. 'Poll' clearly indicates a read-only operation, and 'crawl or LLM job' and 'opaque task ID' add useful context about the input. However, it does not disclose details such as whether the tool blocks, how errors are reported, or any rate limits, leaving moderate transparency.
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 a single concise sentence with no redundancy. It front-loads the core action and resource, making it efficient and easy to scan.
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 simple polling tool with an output schema, the description covers the essential purpose and input types. It lacks an explicit link to the submit tools, but the context is clear enough for a low-complexity operation. A slightly more complete description would mention that the task ID originates from a submit call.
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 coverage is 0%, so the description must compensate. It clarifies that task_id is 'opaque' and that job_type is either crawl or LLM, aligning with the schema's enum. However, it gives no guidance on how to obtain the task_id (e.g., from a submit call) or the effect of the job_type default, so semantics are only partially explained.
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 function: 'Poll a Crawl4AI crawl or LLM job by its opaque task ID.' It identifies the specific verb (poll), resource (crawl or LLM job), and identifier (task ID), which distinguishes it from sibling submit and artifact-info tools.
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?
The description implies usage after submitting a job with crawl_job_submit or llm_job_submit, and it mentions both job types. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites beyond having a task ID.
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, the description carries the full burden. It discloses that the tool returns status and warns that unsupported operations are not emulated, which is a behavioral limitation. It does not detail side effects or authentication specifics, but for a zero-parameter read-only inventory tool, this is reasonably transparent.
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 a single sentence that efficiently conveys what is returned and a critical safety caveat. No fluff, clearly structured with a semicolon separating the main function and the limitation.
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 simple zero-parameter inventory tool with no output schema, the description gives sufficient context: it reports capability and bridge status, and warns about emulation limits. It is brief but adequate, though it could specify the exact output format or error behavior if needed.
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?
The tool accepts zero parameters, and the schema has full coverage (empty schema). The description does not need to explain parameters, and it adds context about the tool's purpose beyond the empty schema. Baseline for 0 params is 4.
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 returns 'pinned runtime capability and the authenticated native-server bridge status', specifying a distinct resource and action. It also differentiates from sibling tools by indicating it is an inventory/status check, not an operation tool like native_api_operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for checking capability and bridge status before relying on native operations, especially with the caution that 'unsupported operations are not safely emulated'. It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to know when to call it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![crawl4ai-complete-mcp MCP server](https://glama.ai/mcp/servers/lexiismadd/crawl4ai-complete-mcp/badges/card.svg)](https://glama.ai/mcp/servers/lexiismadd/crawl4ai-complete-mcp)

Score Badge

Copy to your README.md:

[![crawl4ai-complete-mcp MCP server](https://glama.ai/mcp/servers/lexiismadd/crawl4ai-complete-mcp/badges/score.svg)](https://glama.ai/mcp/servers/lexiismadd/crawl4ai-complete-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/lexiismadd/crawl4ai-complete-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server