Convert text between case styles: camelCase, snake_case, kebab-case, PascalCase, CONSTANT_CASE, Title Case, Sentence case.
Scored across 2 tools
convert_case and detect_case have clearly distinct purposes: one transforms text, the other identifies its style. There is no overlap or ambiguity between them, so an agent can select correctly without hesitation.
Both tools follow a strict verb_noun snake_case pattern (convert_case, detect_case), sharing the same _case suffix and pairing a clear action verb with the same noun. The convention is perfectly predictable.
Two tools is on the thin side per the rubric for a 1-2 tool server, though for a narrow single-purpose case-conversion utility it is arguably adequate. A helper like list_supported_cases would have rounded it out.
The pair covers both directions of the core domain: converting between case styles and detecting them, with convert_case enumerating a broad range of target styles. Minor gaps exist, such as no explicit way to list supported styles or handle non-convertible input, but the essential lifecycle is covered.
Convert text between case styles: camelCase, snake_case, kebab-case, PascalCase, CONSTANT_CASE, Title Case, Sentence case.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to convert | |
| target | Yes | Target case style |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full burden of behavioral disclosure. It conveys that this is a deterministic text-to-text transformation with an enumerated set of outputs, but says nothing about invalid input, already-matching input, or handling of mixed/ambiguous styles.
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?
A single front-loaded sentence with a colon-delimited list of valid styles; every token earns its place and nothing is padded.
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 two-parameter, low-risk pure transformation with a fully described schema and no output schema, the description is nearly sufficient. Only edge-case behavior (invalid or already-converted text) is left unstated, which is a minor gap for this tool class.
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 both parameter names ('text', 'target') are self-explanatory, with the enum fully enumerating valid targets. The description restates the same style list as the enum, adding confirmation but no new meaning.
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 (convert) and resource (text) plus the full set of target case styles, so the operation is unambiguous. It does not explicitly distinguish itself from the sibling detect_case, but the convert/detect contrast is strongly implied by the verb choice.
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 only implied: an agent can infer this is for transforming case rather than identifying it from the verb and the sibling name. There is no explicit when-to-use or when-not-to-use statement, nor any routing guidance to detect_case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detect which case style a given text is in.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It implies a non-mutating read but says nothing about the return value, the set of case styles it can report, or ambiguity handling — meaningful gaps for a detection 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?
A single front-loaded sentence with no filler; the purpose is the first thing read. It is appropriately sized but too terse to cover the behavioral burden it must carry.
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 annotations and no output schema, the description is the only source for what the tool returns. It never states the output shape or the possible case-style labels, which is precisely the information an agent needs to use the result.
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?
Only one parameter exists and schema coverage is 100%, so the schema already documents 'text' fully. The description adds no format, encoding, or length guidance beyond the schema, so the baseline 3 applies.
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 ('Detect') and resource ('case style') applied to a given text, which is immediately distinguishable from the sibling convert_case. It does not explicitly name the sibling, but the detect-vs-convert distinction is unambiguous.
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 only implied: an agent can infer this is the read-only analysis tool versus convert_case. There is no explicit statement of when to prefer it, no prerequisites, and no mention of the alternative, leaving routing to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
convert_casedetect_case
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