craft-content-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds mode-behavior context but says nothing about return format, whether generated content is saved or ephemeral, or how interview_notes are parsed. With annotations carrying the safety burden, 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two compact sentences with the mode concept front-loaded and no filler. The second sentence efficiently maps the two modes to their conditions, though the capitalized 'DISCOVERY MODE' adds slight emphasis noise.

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 but full schema description coverage, the description supplies the key missing behavioral framing (the two operational modes) an agent needs to call correctly. It is nearly complete for this tool, lacking only minor detail on how raw interview_notes are handled.

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 schema already documents all nine parameters including the mode enum's meaning and the full-mode requirements. The description reinforces the mode distinction but adds no syntax, format, or edge-case detail beyond what the schema provides, so 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 states a specific verb ('Generate') and resource ('case studies') and names its distinctive DISCOVERY MODE. It is clearly distinguishable from siblings like testimonial_capture or newsletter_builder, though it doesn't explicitly reference any sibling by name.

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?

It gives a clear conditional for choosing between the two modes: 'If you have the full story, get a complete case study. If not, get interview questions to gather the story first.' This is genuine usage guidance, though it doesn't address when to prefer this tool over adjacent siblings like testimonial_capture.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds genuine behavioral context — the default of generating 5 formats — though this partly duplicates the target_formats schema description and omits things like processing latency or output structure.

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 tight sentences with the core action front-loaded and the default/output-count detail immediately after. The promotional 'most useful formats' phrasing is slightly loose but does not bloat the text.

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 read-only transformation tool with full schema coverage and no output schema, the description conveys the essential contract (paste content, get 5 formats by default, override as needed). It could say more about the returned structure, but nothing critical to invocation 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?

Schema description coverage is 100%, so the schema documents every parameter and both enums, establishing a baseline of 3. The description only loosely alludes to target_formats ('specify exactly what you need') and adds no syntax or format guidance 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?

The description states a specific verb (transform) and resource (source content) plus the outcome (multiple output formats), which is clear. It does not, however, name or differentiate itself from the many sibling generators (case_study_generator, newsletter_builder, webinar_script), so an agent must infer the boundary from the format list alone.

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?

It explains the default behavior (5 formats) and that you can override by specifying formats, which implies usage. But it never states when to choose this tool over siblings or any prerequisites/exclusions, leaving the routing decision to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is clear. The description adds useful detail about what is returned (scores, suggestions, a revised version), but it does not clarify scoring methodology, whether the output includes the revised text or only suggestions, or any limitations. With annotations covering safety, a 3 is appropriate.

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 efficient sentence that front-loads the core actions and output. It is appropriately sized and free of filler, though it could be slightly more structured with explicit usage cues.

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 read-only annotations and fully documented schema, the description provides enough context for an agent to invoke the tool correctly. It covers what the tool does and what it returns, though it could clarify the scoring dimensions or output format more explicitly.

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%, so all parameters are fully documented in the schema. The description does not add any parameter-level meaning beyond noting that a version with jargon replaced and sentences split is returned, which is an output detail rather than input semantics. Baseline 3 is correct.

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 sequence (analyze, score, return suggestions) and states exactly what the output contains, including jargon replacement and sentence splitting. It is clearly distinct from siblings like case_study_generator or content_repurposer, which create or transform content rather than analyze it.

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?

Usage is implied by the description—use this to analyze and improve existing content—but it does not explicitly state when to choose this over alternatives like content_repurposer, nor does it mention prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true and non-destructive, so the safety profile is covered. The description adds the generative behavior (suggests 5 points when missing) which is useful, but doesn't disclose output length, format, or any limits on the generated content. With annotations present, a 3 is appropriate.

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 that front-load the purpose and immediately clarify the two modes. Zero waste, highly scannable.

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 read-only generative tool with full schema coverage, the description covers the core modes and fallback behavior. Missing output format hints, but annotations and schema carry most of the burden; nearly 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%, so all parameters and enums are already documented. The description reinforces the key_points fallback behavior but adds no syntax or format details beyond the schema. Baseline 3 is correct.

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 states a specific verb (build) and resource (newsletter content) with a clear two-mode behavior: suggest key points from a topic, or craft content from key points. It doesn't explicitly differentiate from siblings like content_repurposer or thought_leadership_series, but the output type is clear.

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 two usage paths (topic-only vs. key-points) but doesn't state when to use this versus sibling tools or what conditions warrant which mode beyond the presence of key_points. No exclusions or alternatives are named.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, destructiveHint=false, so safety is already covered. The description adds a behavioral note about auto-suggesting objections and deriving value props, but this duplicates what the schema descriptions already state for those parameters, so the added value is modest.

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?

Three tightly packed sentences with the core action front-loaded. The rhetorical questions ('Know your objections?') are stylistic flourish rather than information, but they cost little and the whole thing is easy to scan.

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 8 parameters, no output schema, and no annotations describing output shape, the description should indicate what the agent gets back (handlers, talk tracks, length). It explains the suggestion fallback but leaves the returned artifact undefined.

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 all 8 parameters are already documented, including the derivation logic for value_props and common_objections. The description adds no syntax, format, or constraint detail beyond that baseline.

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 verb+resource ('Generate sales content') is combined with a clarifying second sentence that reveals the actual artifact: objection handlers. That distinguishes it functionally from case_study_generator or webinar_script, though it never names or contrasts a sibling explicitly, keeping it out of the 5 band.

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?

It gives conditional context ('Know your objections? ... New product with no sales data yet?') which implies when each mode applies, but it never states when to choose this tool over content_repurposer or craft_content_improver, nor any prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered and the description's burden is lower. It adds the notion that output is discovery-oriented rather than polished copy, but says nothing about whether drafts are persisted, what auth/context is needed, or how many variants are produced.

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 with the artifact list front-loaded and zero padding. The trailing "Discovery-focused by design" is terse to the point of being cryptic, but nothing is wasted.

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 convey what the agent gets back, and "formatted outputs" is vague given three distinct artifact types are claimed. The 8 well-documented parameters mean input side is complete, so only the return-shape and sibling-routing gaps remain.

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% across all 8 parameters, including the enum values for testimonial_type and the meaning of success_story and relationship_context, so the schema carries the semantic load. The description adds no format or expectation detail beyond the schema, which is the baseline 3 case.

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?

States a specific verb ("Generate") and three concrete artifacts (testimonial request emails, interview questions, formatted outputs), which is more than a restatement of the name. However it never distinguishes itself from the closest sibling, case_study_generator, so an agent must infer the boundary between testimonials and case studies.

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?

"Discovery-focused by design" is the only usage signal and it is opaque — it does not say when to pick this tool over case_study_generator or reference_call-adjacent siblings, nor what preconditions exist (e.g. must you already have a success story). No when/when-not guidance is present.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavior beyond that: drafts run 600-800 words, missing proof points produce suggested proof points to gather, and drafts ship with placeholders that must be completed before publishing.

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?

Three tight sentences with no filler, and the core purpose and output shape are front-loaded before the conditional caveats about proof points and placeholders.

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 correctly compensates by characterizing the return (article drafts of a stated length, flagged gaps, placeholders), which is what an agent needs to set expectations. Remaining minor gap is that it does not cover pagination, ordering, or how multiple articles are returned, but for a read-only generation tool this is close to 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% and all seven parameters carry inline descriptions, so the schema already does the heavy lifting; baseline is 3. The description restates the optionality and fallback behavior of proof_points but adds no syntax, format, or default detail beyond what the schema provides.

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 gives a specific verb and resource ('Generate thought leadership article drafts') plus concrete output scope (600-800 words) and the inputs it works from (topic, take, proof points). It is clearly distinguishable from content-type siblings like webinar_script or case_study_generator, though it never names an alternative explicitly, which keeps it just 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?

There is no when-to-use or when-not-to-use guidance and no routing to or away from any sibling tool. The conditional behavior described (what happens without proof points) is output behavior, not guidance on selecting this tool over another.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds one genuine behavioral trait beyond that — it will auto-suggest takeaways when key_takeaways is omitted — but says nothing about the resulting script's structure, length, or format.

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?

Three short sentences with the core verb+resource front-loaded and no filler. The rhetorical-question phrasing is slightly informal but still earns its place by conveying the two input paths.

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 an 8-parameter generation tool with no output schema, the description omits what the agent actually receives — script sections, approximate length, or how duration/webinar_type shape the result. The input side is fully covered by the schema, but the output side is left entirely to inference.

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 all eight parameters, including the three enums, are already documented. The description only echoes the key_takeaways fallback already stated in the schema, adding no new syntax or format detail. Baseline 3 applies when 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?

States a specific verb+resource ('Generate webinar scripts'), and the resource is distinctive enough to separate it from siblings like newsletter_builder or case_study_generator. It never explicitly names or contrasts a sibling, so it stops 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 'Know your takeaways? ... Still planning?' pair gives conditional guidance on when to supply key_takeaways versus letting the tool suggest them, which is useful. However, there is no when-to-use vs. alternative guidance and no exclusions relative to the other content-generation siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.