EzBiz Social Media Analytics
Server Details
AI-powered social media intelligence: profile analysis, engagement scoring, and trend detection.
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
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Tool Definition Quality
Average 3.5/5 across 6 of 6 tools scored. Lowest: 2.9/5.
Each tool targets a distinct aspect of social media analytics: profile analysis, competitor comparison, content planning, trend detection, hashtag research, and engagement scoring. No overlap in purposes.
Most tool names follow a verb_noun pattern (e.g., analyze_profile, detect_trends), but competitor_benchmarks and content_calendar are noun-based, creating slight inconsistency. Still readable and predictable.
Six tools is an ideal count for a social media analytics server—each tool earns its place without overwhelming the agent. The scope is well-defined.
Covers core analytics needs: profile analysis, competitor benchmarks, content calendar, trend detection, hashtag research, and engagement scoring. Minor gaps like historical data comparison or export features, but overall comprehensive.
Available Tools
6 toolsanalyze_profileBInspect
Analyze a social media profile or brand presence — posting patterns, content themes, audience indicators, and growth recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | No | Social media platform to analyze | |
| username | Yes | Social media username or handle (e.g., '@hubspot') | |
| business_name | No | Business name for broader cross-platform search |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions analysis outputs but fails to state read-only nature, error handling, authentication needs, or rate limits. This is insufficient for an agent to safely invoke the 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?
The description is a single, concise sentence that front-loads the main action. However, it could be more structured, e.g., separating purpose and outputs.
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?
With no output schema, the description should give richer context about return values (e.g., metrics, text, recommendations). It lists outputs vaguely but omits details like whether data is real-time or historical, or the format of recommendations.
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 does not add parameter-specific meaning beyond the schema; it only repeats the overall purpose. No additional help with format or constraints.
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 analyzes a social media profile or brand presence, listing specific aspects like posting patterns and audience indicators. It distinguishes from sibling tools (e.g., competitor_benchmarks, detect_trends) by focusing on profile-level analysis rather than competitive or trend data.
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 (analyzing a profile or brand) but does not explicitly state when not to use or contrast with siblings. Given the sibling list, some guidance on alternatives would improve selection accuracy.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
competitor_benchmarksBInspect
🔒 [Pro] Benchmark your social media against competitors — side-by-side comparison of engagement, content strategy, audience growth, and competitive gaps.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | Yes | Your brand name | |
| platform | No | Platform to focus on (analyzes all if omitted) | |
| competitors | Yes | Comma-separated competitor names (e.g., 'Nike,Adidas,Puma') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description bears full burden. It does not disclose whether the tool is read-only, what authentication or permissions are needed, or any side effects. The 'Pro' mention relates to pricing, not behavior.
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?
Single sentence is concise and front-loaded with the core purpose. However, it could be slightly more structured without losing brevity.
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?
No output schema, so description should explain the return format. It vaguely mentions 'side-by-side comparison' but does not specify whether the result is a report, data visualization, or raw metrics. Also lacks any behavioral or usage context.
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 covers 100% of parameters with descriptions (brand, platform, competitors). Description adds context about the comparison dimensions but does not enrich individual parameter meaning beyond the schema.
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?
Description clearly states the tool benchmarks social media against competitors with specific comparison dimensions (engagement, content strategy, audience growth, competitive gaps). The verb 'benchmark' and resource are explicit, distinguishing it from sibling tools like analyze_profile or score_engagement.
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?
Description implies use for competitive comparison but provides no explicit guidance on when to use this tool versus alternatives like analyze_profile for individual analysis. No exclusions or when-not-to-use are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
content_calendarAInspect
🔒 [Pro] Generate a detailed social media content calendar — specific posts with captions, hashtags, optimal timing, and content templates for 1-4 weeks.
| Name | Required | Description | Default |
|---|---|---|---|
| duration | No | Calendar duration (default: 2_weeks) | |
| platforms | No | Comma-separated platforms (default: 'instagram,twitter,linkedin') | |
| business_or_niche | Yes | Business name or niche (e.g., 'fitness brand', 'Acme Plumbing') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavior. It includes the pro requirement (🔒 [Pro]) and describes output components. However, it does not mention whether this tool is read-only (idempotent), requires any permissions, or has rate limits. For a generation tool, the lack of side-effect clarity is a minor gap.
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 sentence that efficiently conveys the tool's purpose and key outputs. While it is concise, it could be slightly improved by breaking into multiple lines for readability, but overall it is well-structured and front-loaded.
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 covers the return value by listing post components. It also mentions the pro lock. However, it does not clarify the nature of the tool (e.g., whether it creates/modifies data or just generates a plan), nor does it describe error conditions or limitations beyond duration. For a tool with 3 simple params, it is reasonably complete but could add idempotence and scope.
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 the schema provides descriptions for all three parameters: duration, platforms, and business_or_niche. The description does not add significant new semantics beyond what the schema already states. It reiterates the duration range (1-4 weeks) which aligns with the enum, but no extra guidance on formatting or usage.
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 specifies 'Generate a detailed social media content calendar', which is a clear verb-object pairing. It lists specific outputs (posts, captions, hashtags, timing, templates) and duration range. Among siblings, this is the only calendar generation tool, so it is well distinguished.
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?
No explicit guidance on when to use this tool versus siblings. The description only states what it does. Since siblings cover different tasks (analysis, benchmarks, trends, hashtags, engagement), the context implies usage, but no criteria or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
detect_trendsAInspect
Detect trending topics and conversations in a niche — viral content patterns, emerging topics, sentiment shifts, and opportunity alerts.
| Name | Required | Description | Default |
|---|---|---|---|
| niche | Yes | Industry or niche to monitor (e.g., 'AI marketing', 'fitness') | |
| timeframe | No | Timeframe for trend analysis (default: this_week) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes what the tool outputs but does not disclose behavioral traits such as read-only nature, authentication needs, rate limits, or any side effects. The descriptions of return elements are helpful but insufficient for complete 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 sentence with appropriate dash separation, efficiently conveying purpose and key outputs. Every part is informative, no 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?
Given no output schema, the description partially fills the gap by listing return elements (viral patterns, topics, shifts, alerts). It lacks details on result structure, pagination, or error cases but is fairly complete for a simple trend detection tool with two parameters.
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% for both parameters (niche and timeframe). The tool description does not add additional meaning beyond the schema; it merely reiterates the niche context. Therefore, the description adds marginal value over the schema, warranting a baseline 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 detects trending topics and conversations, listing specific outputs (viral content patterns, emerging topics, sentiment shifts, opportunity alerts). It uses a specific verb 'detect' and identifies the resource as trends in a niche, distinguishing it from sibling tools like research_hashtags or analyze_profile which focus on different aspects.
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 explicit guidance on when to use this tool versus siblings like research_hashtags or content_calendar. It implies usage for trend detection but does not state prerequisites, recommended scenarios, or when to avoid using it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
research_hashtagsAInspect
Research effective hashtags for a topic — popularity estimates, related hashtags, niche vs broad classification, and recommended hashtag sets.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | Number of hashtags to return (default: 20, max: 50) | |
| topic | Yes | Topic or keyword for hashtag research (e.g., 'real estate', 'fitness') | |
| platform | No | Target platform for hashtag optimization |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses what the tool returns (popularity estimates, related hashtags, etc.) but does not mention behavioral traits such as authentication needs, rate limits, or whether it performs any side effects. Adequate but lacks depth.
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?
Single sentence that is front-loaded with purpose and lists key outputs. No unnecessary words or repetition. Highly concise.
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 3 parameters, no output schema, and no annotations, the description covers the main purpose and outputs fairly well. It lacks details about return format or structure, but is sufficient for an agent to understand what the tool does.
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 good parameter descriptions. The tool description adds value by explaining the broader context of what the research yields (e.g., niche vs broad classification, recommended sets), which enhances understanding beyond the schema alone.
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?
Description clearly states the verb (research) and resource (hashtags for a topic) and lists specific outputs like popularity estimates, related hashtags, and classification. It distinguishes well from sibling tools like analyze_profile or detect_trends which focus on different tasks.
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?
Description implies usage for hashtag research but does not explicitly state when to use this tool versus alternatives like competitor_benchmarks or content_calendar. No exclusion or when-not guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
score_engagementCInspect
Score social media engagement for a brand or topic — engagement rate estimates, content type effectiveness, posting time analysis, and benchmarks.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | No | Platform to focus on (analyzes all if omitted) | |
| brand_or_topic | Yes | Brand name or topic to analyze (e.g., 'Nike', 'AI marketing') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the scope (brand/topic, optional platform) and types of analyses, but does not reveal behavioral traits such as authentication requirements, data sources, accuracy, or limitations. Since no annotations are provided, the description carries the full burden for transparency, which it fails to meet.
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 front-loads the main purpose and lists outputs. It is concise without unnecessary words, though it could benefit from clearer structuring of the analysis types.
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?
The description covers the tool's purpose and key outputs, but is incomplete given the lack of output schema or annotations. It does not describe the return value format, any limitations, or the calculation methodology, which are needed for a fully contextual understanding.
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 each parameter already has a clear semantic meaning. The tool description adds minor context by listing outputs, but does not enhance parameter understanding beyond the schema. Baseline 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 scores social media engagement for a brand or topic, listing specific analyses like engagement rate estimates and benchmarks. It distinguishes from sibling tools by focusing on engagement metrics, but does not explicitly differentiate from alternatives like analyze_profile or competitor_benchmarks.
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?
No guidance is provided on when to use this tool versus the listed sibling tools. The description lacks any 'when-to-use' or 'when-not-to-use' instructions, leaving the agent to infer usage without context.
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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