List Dribba's case studies (slug, title, industry).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of case studies to return. | |
| industry | No | Case-insensitive substring match on the industry, e.g. "fintech". Omit for all industries. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the resource scope and output fields but does not disclose ordering, pagination, or default behavior beyond what the schema already provides.
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 sentence that is direct, front-loaded, and free of filler. Every word contributes to identifying the tool's purpose and output.
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 simple read-only list endpoint with two optional parameters and full schema coverage, the description is sufficient. It names the output fields, and the annotations cover safety. Minor omissions like default limit or ordering are not critical for this level of complexity.
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 fully documents limit and industry parameters. The description does not add any parameter-level detail, which is acceptable given the high schema coverage.
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 the verb ('List'), the resource ('Dribba's case studies'), and the output fields ('slug, title, industry'). This distinguishes it from sibling tools like list_services without needing to inspect schemas.
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 this is the tool for listing case studies, but it does not explicitly say when to use it over alternatives or when not to use it. There are no exclusions or comparison with list_services or other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a distinct purpose: company facts, cost estimation, project listing, service listing, and contact submission. There is no overlap or ambiguity between them.
Four tools follow a clear verb_noun pattern (estimate_project, list_projects, list_services, submit_contact_request), but 'company_facts' deviates by using a noun_noun structure, breaking the otherwise consistent convention.
With 5 tools, the server is well-scoped for its purpose—showcasing the company and generating leads. Each tool earns its place without redundancy or bloat.
The tool surface covers the core journey: learn about the company, see services and projects, get an estimate, and contact. A notable minor gap is the lack of a tool to fetch detailed information on a specific project, but this is not a blocker for most use cases.
