CRAFT Content MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct content type or process: case studies, newsletters, webinars, repurposing, LinkedIn series, testimonials, sales enablement, and content improvement. Even where content_repurposer and thought_leadership_series both touch LinkedIn, they serve clearly different functions—transforming existing content vs. creating an original series—so no ambiguity exists.
Naming Consistency4/5
Most tool names follow a predictable 'content_type_purpose' pattern (e.g., case_study_generator, newsletter_builder, webinar_script). A few, like content_repurposer and craft_content_improver, use a verb-noun style, but the overall naming is still consistent in readability and adheres to snake_case throughout, with only minor stylistic deviations.
Tool Count5/5
With 8 tools, the server is well-scoped for a content marketing niche. Each tool covers a distinct deliverable or workflow, and the count is neither too sparse to be useful nor too bloated to navigate easily.
Completeness4/5
The tool surface covers a broad range of content marketing needs: long-form, email, social, video, sales, and improvement. There’s no dedicated blog/article generator, but content_repurposer can produce blog content, so the gap is minor and easy to work around.
Average 3.7/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on January 16, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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

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, but it only states that it generates emails, questions, and templates. It doesn't explain how outputs are returned, whether any side effects occur, or how format_needed influences the result. This lack of behavioral context leaves the agent with significant unknowns.
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 filler, directly stating the action and the three output types. It is appropriately front-loaded and every word 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?
With 7 parameters, 4 required, and no output schema, the description should clarify the relationship between inputs and outputs, such as how format_needed changes the result or what the response structure looks like. It lacks this complete picture, making it insufficient for confident invocation in all cases.
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 100% with descriptions for all 7 parameters, so the schema already handles parameter meaning. The tool description adds no extra parameter-level detail—it doesn't clarify how each parameter maps to the generated outputs—but the high schema coverage justifies the baseline.
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 the specific verb 'Generate' and clearly lists the deliverables: testimonial request emails, interview questions, and formatted output templates. This distinguishes it from sibling tools like case_study_generator or newsletter_builder, which focus on other content types.
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 name and description imply the tool is for testimonial capture, but there is no explicit guidance on when to use it versus alternatives. No mention of prerequisites, scenarios, or when not to use it, leaving usage partially implied.
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 burden of disclosing behavior. It does state that it returns a 'score and improved version,' which is useful, but it does not explain side effects, processing details, limitations, or what CRAFT principles entail, 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 concise and front-loaded: two sentences that immediately state the action, method, and expected return value. Every word earns its place with no redundant filler.
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 5 parameters, no output schema, and no annotations, the description is moderately complete: it identifies the core inputs and outputs but lacks details on usage context, CRAFT principles, output formatting, and parameter interdependencies. It is adequate for basic invocation but leaves gaps for nuanced use.
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 100%, so the baseline is 3. The description adds no parameter-specific meaning beyond the schema; it does not elaborate on how 'goal', 'audience', or 'constraints' affect the analysis, nor does it provide examples or clarify the 'content_type' values beyond what the schema already lists.
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 'Analyze and improve any content using CRAFT principles' with a specific verb (analyze/improve), resource (content), and expected output ('Returns score and improved version'). It differentiates itself from sibling generation tools by focusing on improvement and scoring rather than content creation.
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 is given on when to use this tool versus alternatives. The phrase 'any content' implies broad applicability, but there is no mention of when not to use it or which sibling tools might be more appropriate for specific content types.
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. It says 'Generate' but doesn't disclose how the tool behaves, what output format to expect, whether it requires all inputs, or any side effects. The listed output types give some clue, but the lack of behavioral detail is a significant gap.
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 lists the key deliverables. It is front-loaded with the verb 'Generate' and wastes no words. Every element 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters and no output schema, the description is relatively thin. It lists output types but doesn't specify the structure of generated content, whether outputs are combined, or any dependencies on parameters. The sibling tools are not differentiated explicitly, but the core purpose is clear enough for basic use.
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 100%, so all parameters are documented with short descriptions. The tool description adds context about output types but does not link specific parameters to specific outputs. Since the schema already explains the parameters well, the description adds marginal value beyond that.
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 generates pitch scripts, objection handlers, discovery questions, and competitive responses, which is a specific verb-resource combination. It distinguishes itself from sibling tools like case_study_generator and newsletter_builder by focusing on sales enablement content.
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 sales enablement content generation, but it doesn't explicitly state when to use this tool versus alternatives. No exclusions or alternative recommendations are provided, leaving the agent to infer based on the tool name and output list.
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 behavioral traits, but it only offers a high-level claim ('Transform one piece into 10+ formats'). It does not explain how the transformation works, what the output looks like, whether it is AI-generated, or any limitations or side effects, leaving significant behavioral uncertainty.
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, well-structured sentence that front-loads the action ('Transform') and immediately conveys the value proposition with a concrete list of output formats. It contains no filler and every word contributes to the 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?
With no output schema and no annotations, the description fails to compensate for those gaps. It does not specify the return format, whether all 10+ formats are generated in one call, or how the required parameters (source_content, source_type, key_message) must be used, leaving the tool's behavior incompletely defined.
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 100%, so the baseline is 3. The description does not add any additional meaning beyond the schema's parameter descriptions; for instance, it does not clarify how 'audience' or 'brand_voice' influence the output formats.
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 core action with a specific verb ('Transform') and resource ('one piece'), and enumerates concrete output formats (LinkedIn, Twitter, email, blog, etc.). This makes it immediately distinguishable from sibling tools like newsletter_builder or webinar_script, which focus on single-format generation.
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 for repurposing existing content into multiple formats, providing clear context for when an agent would invoke this tool. However, it does not explicitly mention exclusions or alternative tools, though the general transformation focus makes its context fairly apparent.
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 carries the full burden. It discloses the major output components (Challenge-Solution-Results, quotes, metrics, one-pager, social snippets), but does not surface caveats like output format or how optional inputs are handled.
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. Every phrase adds value, efficiently conveying the tool's purpose and deliverables.
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?
The description adequately explains the tool's purpose and output components, but because there is no output schema, it lacks specifics about return format or how the one-pager and social snippets are delivered. This is sufficient for selection but not full invocation confidence.
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 input schema has 100% description coverage for all 8 parameters, so the baseline is 3. The description adds no parameter-specific detail beyond mentioning quotes and metrics, which the schema already covers.
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 states 'Generate complete B2B case study' with explicit structure (Challenge-Solution-Results), including customer quotes, metrics, one-pager, and social snippets. This is a specific verb+resource and clearly differentiates from siblings like newsletter_builder or webinar_script.
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 creating B2B case studies, giving clear context for when to use it. However, it does not provide explicit when-not-to-use guidance or mention alternatives such as testimonial_capture or content_repurposer.
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. It discloses the output components (hook, sections, CTAs, 10 subject lines), giving useful behavior context. However, it does not mention any limitations, generation time, or side effects, though for a content generator these may be less critical.
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 lists the key output components without extra filler. Every word contributes to understanding the tool's purpose and result.
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 content generation tool with 6 parameters and no output schema, the description provides enough context about what the tool produces. It names the components of the generated newsletter and the 10 subject lines, which is reasonably complete, though it could mention the return format or how inputs map to sections.
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 100%, so the baseline is 3. The description adds no parameter-level meaning beyond the schema; it only names output components, not input semantics. The schema already documents each parameter adequately.
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: generating a complete newsletter with specific components (hook, sections, CTAs, 10 subject lines). It names the resource (newsletter) and distinguishes it from sibling tools focused on other content types like case studies or webinars.
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 (when you need a newsletter) but does not explicitly state when to prefer this tool over alternatives or mention any exclusions. The context is clear from the tool name and description, but no explicit 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?
No annotations are provided, so the description is the only source of behavioral information. It discloses the output components but does not discuss how inputs are processed, expected output format, or any limitations or prerequisites.
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, front-loaded sentence efficiently conveys the tool's purpose and deliverables without redundancy.
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 simple flat schema and clear purpose, the description is reasonably complete for an agent to know what the tool does. However, the lack of output schema and annotations means the description could be enhanced with return format or example usage.
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 input schema covers all 7 parameters with descriptions, so the schema does the heavy lifting. The description does not add further meaning to parameters beyond the schema's field 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 states 'Generate complete webinar script' with a specific verb and resource, and lists the included components (slides outline, speaker notes, Q&A prep, engagement prompts). This clearly distinguishes it from sibling content generation tools like case_study_generator or newsletter_builder.
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 a context where a user needs a structured webinar script, which is clear. However, it does not explicitly compare with alternatives or state when not to use it, leaving some ambiguity.
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 burden for behavioral transparency. It explains the output characteristics (5 posts, interconnected themes, hooks, engagement prompts) but doesn't disclose any side effects, limitations, or prerequisites beyond the task itself. For a content generation tool, this is adequate but not rich.
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, focused sentence that front-loads the primary action ('Generate') and the output type ('5-post LinkedIn series'). Every word adds value, with no redundant or filler content.
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?
The tool has 6 parameters and no output schema. The description provides a high-level view of what the output will include, which helps set expectations. However, it doesn't detail the output format or how the parameters influence the series. Given moderate complexity and full schema coverage, the description is sufficient but could be more explicit about the generation behavior.
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 100%, so the schema fully documents all six parameters. The description does not add additional parameter-level meaning, but the baseline of 3 applies because the schema already provides comprehensive semantics.
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: 'Generate 5-post LinkedIn series' with specific features (interconnected themes, hooks, engagement prompts). It distinguishes from siblings like case_study_generator or newsletter_builder by specifically targeting LinkedIn series.
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 phrase '5-post LinkedIn series' establishes a clear context for when to use the tool. It doesn't explicitly name alternatives or exclusions, but the context is specific enough that an agent would know this is for LinkedIn thought leadership content, not for other content types.
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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