get_case_studyRetrieve the full write-up of a livestream advertising case study by providing its slug, enabling detailed campaign insights and performance analysis.
Full write-up for one case study. Pass a slug returned by list_case_studies.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Case-study slug, e.g. 'turknet-x-gingerlive-case-study-gen-z-livestream-advertising-campaign' |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it returns a 'full write-up', implying a read operation, but does not explicitly label it as read-only or mention any potential side effects, error conditions, or authentication requirements. For a simple getter this is acceptable, but it leaves some ambiguity about failure behavior.
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 extremely concise: two short sentences that immediately convey the purpose and the required input. Every word adds value, and it is front-loaded with the core action. No redundancy or unnecessary detail.
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 low complexity (one parameter, no output schema, no annotations), the description adequately covers what the tool does and how to invoke it. It explains the return type ('full write-up') and the source of the slug. It does not mention error handling or return format, but those are not critical for an agent's decision to use this tool. Slight room for improvement by noting it is read-only, but overall sufficient.
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?
The parameter slug is fully described in the input schema with a clear example. The tool description reinforces that the slug comes from list_case_studies, which adds context beyond the schema. Since schema coverage is 100%, the baseline is 3, and the description does not need to elaborate further.
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 clearly states it retrieves the full write-up for a single case study, using a specific verb ('get') and resource ('case study'). It explicitly distinguishes itself from list_case_studies by mentioning the slug comes from that list, making its purpose 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?
The description provides a clear prerequisite: a slug returned by list_case_studies. This implies the sequence (list first, then get), which is valuable guidance. However, it does not explicitly contrast this tool with alternatives beyond the implicit mention of list_case_studies, nor does it state when NOT to use it, so it stops short of full explicitness.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.