Web scraping, code review, content gen, sentiment. Zero Core Tools.
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Average 3.9/5 across 9 of 9 tools scored. Lowest: 3.1/5.
Each tool has a clearly distinct purpose: sentiment analysis, structured data extraction, content generation, health check, tool listing, code review, web scraping, screenshot, and web search. There is minor overlap between scrape_url and extract_structured_data, but the different outputs (raw text vs. structured JSON) make them easily distinguishable.
Most tool names follow the verb_noun snake_case pattern (e.g., analyze_sentiment, scrape_url). The only outlier is 'health', which is a single noun rather than a verb_noun like 'check_health'. This minor inconsistency slightly reduces coherence.
With 9 tools, the server is well-scoped for a general-purpose utility toolkit. Each tool serves a useful function without redundancy, and the count is within the ideal range (3-15) for clarity and manageability.
The tool set covers a broad range of common AI/automation tasks such as text analysis, web scraping, content generation, search, and code review. While some potential utilities (e.g., translation or file conversion) are missing, the lack of a specific domain makes the set feel reasonably complete for a general-purpose toolkit.
analyze_sentimentAInspectAnalyze sentiment of text with entity extraction, confidence scores, and key phrase identification. Returns positive/negative/neutral/mixed with detailed breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze for sentiment |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since annotations are empty, the description carries the burden. It discloses that the tool returns sentiment labels (positive/negative/neutral/mixed), includes entity extraction, confidence scores, and key phrases, and provides a detailed breakdown. However, it does not define the exact output structure or any limitations, which prevents a higher score.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action and followed by output summary. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema or annotations, the description provides sufficient information about what the tool does and returns. It could be more detailed about output structure, but the simplicity of the tool makes this description reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the only parameter 'text' with 100% coverage, so the description's repetition that it analyzes text adds no additional semantics. The baseline of 3 applies because the schema handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Analyze sentiment of text' with specific additional features (entity extraction, confidence scores, key phrase identification) and expected output categories. This distinguishes it from sibling tools like extract_structured_data or generate_content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for analyzing sentiment but does not provide explicit guidance on when to use this versus alternatives, nor does it mention any exclusions or prerequisites. The context is clear from the name and description, but there is no explicit usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract_structured_dataAInspectScrape a URL then use AI to extract structured JSON data matching your schema description. Combines Playwright scraping with Grok LLM extraction.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to scrape | |
| schema_description | Yes | Description of the data to extract and desired JSON structure. Example: 'Extract all product names and prices as {products: [{name, price}]}' |
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 it uses Playwright scraping and Grok LLM extraction, which are relevant behavioral traits. However, it doesn't mention potential non-determinism of LLM output, failure modes, rate limits, or whether the action is read-only, leaving notable gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the primary action, and includes no filler. Every word contributes to understanding what the tool does and how it works.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with 100% schema coverage, the description is mostly complete. It explains the workflow and expected output (structured JSON), though it lacks explicit mention of return format details or error scenarios. Still, it provides sufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and both parameters have descriptions. The description adds minor value by clarifying that the schema_description defines the desired JSON structure, but it largely echoes what the schema already provides. The baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: scrape a URL and use AI to extract structured JSON matching a schema description. It uses a specific verb and resource, and distinguishes it from siblings like scrape_url and screenshot_url by combining scraping with structured extraction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool: when you need structured JSON data from a webpage. It does not explicitly name alternatives or exclusions, but the context of combining scraping with AI extraction is clear enough for an agent to choose it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_contentAInspectGenerate high-quality written content. Supports blog posts, product descriptions, documentation, social posts, and emails. Customizable tone, length, and keywords.
| Name | Required | Description | Default |
|---|---|---|---|
| tone | No | Writing tone (default: professional) | |
| type | Yes | Content type | |
| topic | Yes | Topic or subject to write about | |
| length | No | Target length (default: medium) | |
| keywords | No | Comma-separated keywords to include |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations provided, so the description carries the full burden of behavioral disclosure. It does not mention output format, potential costs, quality caveats, or what the function actually returns beyond the generic promise of 'high-quality' content. The description gives no details about how tone, length, and keywords affect the output or any limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main action, and includes only essential information. Every clause adds value: the action, the supported types, and the customization options. There is no fluff or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low-to-moderate complexity of a content generation tool, the description is reasonably complete. It covers the main purpose, lists applicable content types, and mentions configurable attributes. There is no output schema or annotations, but the return value (generated text) is easily inferred. A slight deduction for not mentioning any potential limitations or response format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides full coverage of all five parameters (100%), including descriptions and enums. The description adds no extra semantic value; it merely repeats 'customizable tone, length, and keywords,' which are already documented in the schema. Baseline score of 3 is appropriate 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Generate high-quality written content.' It enumerates specific content types (blog posts, product descriptions, documentation, social posts, emails), which distinguishes it from sibling tools like analyze_sentiment, extract_structured_data, or search_web. The verb 'generate' and resource 'written content' are specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by listing supported content types ('Supports blog posts, product descriptions, documentation, social posts, and emails'), but it does not explicitly state when to use this tool over alternatives or provide any exclusions. Sibling tools are obviously different, but there is no direct 'when to use' or 'when not to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
healthAInspectCheck Zero Core Tools server status, uptime, and payment network configuration.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. 'Check' strongly implies a read-only, non-destructive operation, but the description does not explicitly state that there are no side effects, nor does it disclose any potential errors or authorization requirements. It provides basic resource context but lacks deeper behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that directly states the tool's purpose without any fluff. Every word adds value and the structure is optimal for a simple health-check tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (zero params, no output schema), the description adequately covers the tool's scope by listing the three health aspects checked (status, uptime, payment network configuration). It does not go into return format or error behavior, but for a health check this is likely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the baseline of 4 is appropriate. The description correctly implies that no input is needed, and there are no parameter semantics to clarify.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Check' and names the exact resource: 'Zero Core Tools server status, uptime, and payment network configuration.' This clearly distinguishes the tool from its siblings, none of which serve a health-check purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (check the server's health) but provides no explicit guidance on when to use this tool versus alternatives. Since no sibling tools are similar, the lack of explicit exclusions is acceptable, but the description could mention that it is a read-only diagnostic step.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_toolsAInspectList all available Zero Core Tools with pricing and input requirements. Use this for discovery.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses what the tool lists (pricing and input requirements) and implies a safe read-only operation. This is sufficient for a simple discovery tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with the action and scope. No wasted words; every phrase contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless listing tool with no output schema or nested objects, the description fully covers what the tool does, what it includes, and when to use it. Nothing important is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the description doesn't need to add meaning beyond the schema. Per the rubric, a no-parameter tool receives a baseline of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'List' with the resource 'all available Zero Core Tools' and explicitly mentions included details (pricing and input requirements). This clearly distinguishes it from sibling tools which perform analysis or content generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Use this for discovery' provides explicit guidance on when to invoke the tool. While it doesn't name alternatives for exclusion, the sibling tool names are obviously different in function, so the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
review_codeAInspectAI-powered security and quality code review. Analyzes for vulnerabilities, anti-patterns, performance issues, and best practices. Returns issues with severity, suggestions, and an overall score.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Source code to review | |
| focus | No | Focus area: security, quality, performance, or all (default: all) | |
| language | No | Programming language (auto-detected if omitted) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it states what the tool does and what it returns: 'Returns issues with severity, suggestions, and an overall score.' It does not mention side effects or limitations, but for a read-only analysis tool this is sufficient disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose in the first sentence, and every sentence adds value. The only minor filler is 'AI-powered,' but it does not hurt clarity or efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description compensates by explaining what the tool returns (issues with severity, suggestions, score). It covers the tool's scope and parameters sufficiently for a medium-complexity tool. It could mention edge cases like unsupported languages, but the core context is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 mentions focus areas (vulnerabilities, performance, etc.) which maps to the 'focus' parameter but does not add substantial detail beyond the schema. No further parameter clarification is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('review') and resource ('code') with a detailed scope: 'Analyzes for vulnerabilities, anti-patterns, performance issues, and best practices.' It is unambiguous and well-distinguished from sibling tools like analyze_sentiment or scrape_url, which handle non-code content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context that this tool is for code review, and the sibling tools are obviously unrelated, so an agent can infer when to use it. However, it does not explicitly state exclusions or mention alternatives, though the context makes the choice straightforward.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scrape_urlBInspectScrape any URL and return cleaned text content. Powered by Playwright headless browser. Returns title, content, word count.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to scrape | |
| max_length | No | Max content length in chars (default: 10000) |
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 using a Playwright headless browser, which hints at JavaScript rendering, but fails to disclose key behavioral aspects such as output truncation based on `max_length`, potential failures for dynamic or restricted URLs, or performance implications. The return fields are listed, but critical limitations and error behavior are omitted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise, consisting of two short sentences that front-load the main action and output. Every word adds value: the verb, resource, output type, technology, and return fields. No filler or redundancies are present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with no output schema, the description covers the basic function and output fields adequately. However, it omits important contextual details such as when to use this tool versus `screenshot_url`, the default and maximum content length behavior, and potential edge cases. It is incomplete for full contextual understanding but sufficient for a minimal viable description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% coverage for both parameters: `url` and `max_length` with descriptions and constraints. The tool description adds no additional parameter context beyond the schema, so it does not go beyond the baseline. It neither duplicates nor enriches the schema, resulting in a standard score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: scraping a URL and returning cleaned text content with title, content, and word count. It uses a specific verb ('scrape') and resource ('URL'), making it distinct from tools like `analyze_sentiment` or `screenshot_url`. However, it does not explicitly differentiate from sibling `screenshot_url`, which also operates on URLs, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 like `screenshot_url` or `search_web`. It says 'any URL' but does not mention scenarios where this tool is preferred, any prerequisites, or exclusions. No usage context is provided beyond a straightforward statement of function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
screenshot_urlAInspectTake a full-page screenshot of any URL. Returns base64-encoded PNG image.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to screenshot | |
| width | No | Viewport width in pixels (default: 1280) | |
| height | No | Viewport height in pixels (default: 720) | |
| full_page | No | Capture full page scroll height (default: true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states the screenshot is full-page and returns a base64-encoded PNG, which are useful details. However, it does not mention potential limitations (e.g., unsupported URLs, rendering delays) or side effects beyond fetching, leaving some 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that immediately states the action and output format. It is front-loaded with the key information and contains no filler or redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no output schema) and minimal annotations, the description adequately covers the core function and return value. It does not explain error handling or edge cases, but the essential behavior is clearly conveyed, leaving only minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides 100% coverage with descriptions for all four parameters (url, width, height, full_page), including defaults and ranges. The description adds no additional parameter semantics, so a baseline of 3 is appropriate given the schema's completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Take a full-page screenshot') and a resource ('any URL'), which uniquely distinguishes it from sibling tools like scrape_url that extract data. It also mentions the output format (base64-encoded PNG), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for capturing visual representations of web pages, which is distinct from text extraction performed by scrape_url. However, it lacks explicit guidance on when to use this tool versus alternatives or any preconditions (e.g., page must be publicly accessible), so usage context is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_webAInspectSearch the web via Google and return organic results with titles, links, and snippets. Optionally returns answer box if available.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| num_results | No | Number of results to return (default: 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With empty annotations, the description carries the burden of behavioral disclosure. It states that results are organic and may include an answer box, adding useful context about the return format. However, it omits potential rate limits or error behavior, which are not critical but would enhance transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the tool's purpose and output without extraneous words. Every word contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two well-documented parameters and no output schema, the description adequately covers the return format and optional answer box. Additional details like default values are already in the schema, making the description complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both query and num_results having clear descriptions. The tool description does not add parameter-specific meaning beyond the schema, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs web searches via Google and returns organic results with titles, links, and snippets. This distinguishes it from sibling tools like sentiment analysis or URL scraping, making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for web searches but provides no explicit guidance on when to use this tool versus alternatives like scrape_url or extract_structured_data. No exclusions or alternative recommendations are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
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