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AI-powered social media intelligence — profile analysis, engagement scoring, trend detection, and hashtag research across major platforms.
This server has significant quality gaps across definition, schema, and error handling. All 4 tools have descriptions (good baseline), but the descriptions are generic and lack actionable detail for LLM selection. Input schemas are present but lack depth, most parameters are minimally constrained (no enums, no bounds, no format specifications). Output schemas are completely absent from the source code; we cannot verify what these tools return. The tool names are reasonably verb-forward (analyze_, score_, research_, detect_) but descriptions do not explain WHEN to use each tool vs alternatives, dependencies between tools, or what makes each unique. Parameter descriptions exist but are often vague (e.g., 'platform' described as 'Specific platform... or null' without explaining what null returns). No evidence of error handling patterns, recovery guidance, or permission gating. The server appears to be a read-only intelligence tool with no destructive operations, but lacks the sophistication needed for production agent use.
Analyze a social media profile or brand presence — posting patterns, content themes, audience indicators, and growth recommendations.
🔒 [Pro] Benchmark your social media against competitors — side-by-side comparison of engagement, content strategy, audience growth, and competitive gaps.
🔒 [Pro] Generate a detailed social media content calendar — specific posts with captions, hashtags, optimal timing, and content templates for 1-4 weeks.
Detect trending topics and conversations in a niche — viral content patterns, emerging topics, sentiment shifts, and opportunity alerts.
Research effective hashtags for a topic — popularity estimates, related hashtags, niche vs broad classification, and recommended hashtag sets.
Score social media engagement for a brand or topic — engagement rate estimates, content type effectiveness, posting time analysis, and benchmarks.
No output schemas documented. The source code shows input schemas for all 4 tools but provides NO documentation of return types or output structure. LLMs cannot plan downstream tool chaining or extract required fields without knowing what data is returned.
Parameter constraints are missing. The 'platform' parameter accepts a free-form string but does NOT enforce the list of supported values (twitter, instagram, linkedin, facebook, tiktok, youtube) as an enum. LLMs will hallucinate platform names not in the list.
Descriptions lack context for tool selection. 'Analyze the social media profile and presence...' does not explain WHEN to call this vs score_engagement or detect_trends. Descriptions do not mention prerequisites, dependencies, or typical use cases.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 16 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 28 | - | v1 |
The 'count' parameter in research_hashtags has NO type constraint. Is it a string? Integer? The description says 'max 30' but does not specify minimum, data type, or default. LLMs will guess.
The 'timeframe' parameter in detect_trends accepts only three values ('today', 'this_week', 'this_month') but is not declared as an enum. It's a free-form string parameter, so LLMs will invent other values like 'last_month' or 'next_week'.
No error handling or recovery guidance in visible code. The source shows auth, logging, and OAuth infrastructure, but no evidence of error classification, actionable error messages, or recovery suggestions in tool implementations (tools/ files not shown in full detail).
Parameter descriptions are generic and lack specificity. 'The username or handle to analyze' does not explain: is this a single username or a comma-separated list? Does the tool accept @mentions? Spaces? What length? LLMs will make wrong assumptions.
No documentation of pagination or result limits. If research_hashtags returns hashtags, how many? Is there a default? Is pagination supported? Large unbound results waste tokens and exhaust context windows.
Parameter 'business_name' in analyze_profile is optional with no description of when to use it. Does it override the lookup? Does it add context? Without clarity, LLMs may pass it inconsistently, causing unpredictable behavior.