Social Media Manager MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: planning, captions, reporting, engagement, hashtags, cadence, metrics interpretation, and a combined pack. No overlap, descriptions effectively differentiate them.
Naming Consistency5/5
All tools follow a verb_noun pattern: seven use 'get_' for retrieval and one uses 'build_' for creation. This consistency makes the API predictable and easy to navigate.
Tool Count5/5
8 tools cover the core aspects of social media management without being excessive. The scope is focused on knowledge and planning, which fits well with the tool count.
Completeness4/5
The set covers most important areas: content calendar, captions, hashtags, posting cadence, engagement, metrics interpretation, and client reporting. Missing features like actual posting or data fetching are reasonable omissions for a guidance-oriented server.
Average 3.6/5 across 8 of 8 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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{
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}
```
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description lacks any behavioral traits such as whether it is read-only, required permissions, or what the output format is. The description is too vague to inform an AI about side effects or constraints.
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 concise with one sentence, front-loading the purpose. However, the colon and list could be better structured for readability.
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 no output schema and a single parameter, the description should explain what the tool returns (e.g., interpretation text or structured data). It does not, leaving the agent uncertain about the tool's output.
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?
Schema description coverage is 0%, so the description must compensate. It lists all enum values and adds the important behavior that omitting the parameter interprets all metrics. This provides meaningful extra context beyond the schema.
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 indicates the tool interprets social metrics and provides actions, listing the supported metrics. The name 'interpret_metric' aligns well, and it is distinct from sibling tools like 'build_content_calendar' or 'get_caption_framework'.
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 'Omit for all' hints at usage but does not provide clear context 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 provided, so description must fully disclose behavior. It outlines rules like pillar-first assignment and weekly shape but fails to explain side effects (e.g., does it save or return data?), authentication needs, or output format.
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?
Two sentences with no filler. The first sentence packs dense jargon, but the second provides clear usage. Could be slightly restructured for readability, but overall efficient.
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?
Missing critical details: what does 'build' entail? Is it a generation, plan output, or database update? No output schema, no annotations, and rules are mentioned without explaining how they apply. Incomplete for a tool with no other structured info.
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?
Input schema has no parameters, so baseline is 4. Description adds valuable context about the calendar's internal rules (e.g., weekly shape, batch-by-task rule), which helps infer the tool's logic without parameters.
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?
Description mentions 'content-calendar system' and 'use to plan a week/month', clearly indicating the tool builds a calendar. It distinguishes from sibling 'get_*' tools but could be more explicit with the verb 'build'.
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?
States to use for planning a week/month for an account, providing basic usage context. However, no explicit when-not-to-use or alternatives are given, leaving guidance incomplete.
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 provided, so description carries full burden. It does not disclose whether it is read-only, what it returns, or any side effects. Listing topics is minimal behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence listing topics, efficient. Could be slightly restructured for readability but no redundancy.
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 zero parameters and no output schema, description lists topics but does not specify output format (e.g., text, structure). Leaves ambiguity about what the agent receives.
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?
Zero parameters, schema coverage 100%. Baseline 4 applies; description adds no parameter info but none needed.
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 it's a playbook for community/engagement topics, listing specific areas like first-hour replies and DM handling. It distinguishes from sibling tools like get_hashtag_strategy or get_posting_cadence.
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 on when to use this tool versus alternatives. It lists topics but does not explain context, prerequisites, or when not to use it.
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 for behavioral disclosure. It describes the output structure ('structure, when to use, and the rule') but does not mention any behavioral traits like idempotency, authorization needs, or side effects. This is insufficient for a tool with no annotations.
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 two short sentences with no wasted words. It front-loads the key purpose and ends with a clear usage instruction.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 optional enum param, no output schema, no annotations), the description is adequate but minimal. It explains the output content and how to get all frameworks, but does not specify return format or other details that might be needed for full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning to the parameter. It lists the enum values and explains what each framework provides (structure, when to use, rule). However, it does not detail the differences between the frameworks, leaving some ambiguity.
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 it returns 'Caption structures by goal' and lists the specific frameworks available (hook_expand_cta, story, list, question_seed). This distinguishes it from sibling tools like get_full_pack or get_engagement_playbook.
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 provides one usage guideline: 'Omit for all' (i.e., omit the parameter to get all frameworks). However, it does not explain when to use this tool versus alternatives such as get_full_pack, nor does it mention when not to use it.
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 provided. The description does not disclose behavioral traits such as read-only nature, auth requirements, rate limits, or side effects. Only states the output type.
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?
Single concise sentence front-loading the key information. Every word adds value, no fluff.
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?
Despite no output schema, description adequately explains the output (count, mix, note per platform). Could be more specific about format or aggregation, but sufficient for a simple tool.
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?
Adds meaning beyond the enum schema by explaining that the platform parameter is optional and omitting it returns all platforms. Clarifies that the output is per platform.
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?
Clearly states the tool returns hashtag count, mix, and note per platform, listing the exact platforms. The verb is implied (get/retrieve). Distinguishes from sibling tools which focus on other aspects like content calendar, captions, etc.
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?
Provides implicit guidance by stating 'Omit for all' for the platform parameter, but lacks explicit when-to-use or comparison with alternatives. No instruction on prerequisites or context.
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 must disclose behavioral traits. It clearly states the output content (frequency, windows, key notes) but does not mention any side effects, data freshness, caching, or access requirements. The behavioral disclosure is adequate for a simple read operation but not comprehensive.
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 two sentences with no redundant information. It front-loads the core function and includes a clear usage instruction. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has a single optional parameter and no output schema, the description provides sufficient high-level information about what the tool returns (frequency, windows, key notes per platform). It lacks detailed output structure, but for a straightforward retrieval tool, this level of completeness is adequate. A more structured description of the output format could improve it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning for the single 'platform' parameter. It does so by explaining that omitting the parameter retrieves data for all platforms. However, it does not describe the enum values or validate their usage beyond listing platform names in the description. This adds some value but not full compensation.
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 explicitly states the tool provides "Posting frequency, best windows, and the key note per platform" and lists the specific platforms (tiktok, instagram, linkedin, x, youtube). This is a clear verb+resource combination with a distinct scope, effectively differentiating it from sibling 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 includes a usage hint ("Omit platform for all") but does not explicitly state when to prefer this tool over siblings or provide exclusions. Usage is implied through context but lacks explicit guidelines for when not to use it.
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 exist, so the description carries full burden. It describes the tool as providing a template/guide, implying a read-only, nondestructive action. However, it does not explicitly state that no data is modified, permissions required, or other behavioral traits.
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?
Two sentences, each earning its place. First sentence front-loads the key deliverables; second sentence specifies the audience. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should explain what the tool returns. It implies a guide or outline but does not specify format (e.g., text, steps) or whether it is a downloadable template. Adequate but leaves some ambiguity.
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 input schema has 0 parameters, so schema description coverage is 100%. Baseline is 4. The description adds meaningful context about the template's content beyond what is in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a client reporting template with specific elements (5-part structure, report-on-the-goal rule, cadence). It distinguishes from sibling tools which focus on content creation or strategy, not reporting.
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 for SMMs managing client accounts but does not explicitly state when to use this tool versus alternatives or when not to use it. No exclusions or prerequisites are provided.
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?
No annotations are provided, so description bears full burden. It lists the components included (cadence, calendar, frameworks, etc.), indicating a read-only retrieval operation. No mention of side effects or auth, but it sufficiently describes what the tool does.
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?
Single sentence that efficiently enumerates the included components without redundancy. Front-loaded with the core value proposition, every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains the return contents fairly well. However, it does not specify the format or structure of the payload (e.g., is it a single object, keys?), leaving minor ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema provides no constraints. The description adds value by detailing the bundled content, effectively describing what the 'payload' contains beyond just 'complete library'.
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?
Description uses specific verb+resource ('complete SMM library in one payload') and distinguishes it from sibling tools that provide individual components. It clearly states what the tool does: retrieves multiple SMM resources together.
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 usage when a comprehensive payload is needed, contrasting with sibling tools that fetch individual items. While not explicit about when to avoid, the context of siblings provides reasonable guidance.
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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