Get production brief requirements
get_brief_requirementsReturn the fields needed to prepare a structured production brief.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | ru |
get_brief_requirementsReturn the fields needed to prepare a structured production brief.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | ru |
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?
The annotations already communicate that the tool is read-only, idempotent, and non-destructive. The description adds the return concept but provides no extra behavioral context such as output structure, locale sensitivity, or any side effects. No contradiction with annotations.
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, front-loaded sentence with no filler. Every word contributes to understanding what the tool returns.
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?
The tool is simple and the annotations cover safety, but there is no output schema and the description does not specify the shape of the returned fields or how locale changes the result. It is minimally adequate but leaves small gaps.
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 0%, and the description does not mention the locale parameter or its effect on the returned fields. The schema itself does document the enum and default, so the parameter is not completely opaque, but the description adds no parameter-level 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?
The description uses a specific verb "Return" and identifies the resource: the fields needed to prepare a structured production brief. This makes the tool's purpose clear, though it does not explicitly distinguish it from sibling tools such as get_deliverables.
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 intended use is implied by the resource name and description: retrieving fields needed for a production brief. However, there is no explicit guidance on when to prefer this tool over siblings, when not to use it, or how locale affects the output.
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 targets a different public knowledge resource (cases, services, evidence, provenance, clients), so an agent can usually tell them apart. However, get_industry_experience overlaps with search_cases, and get_public_evidence versus get_media_provenance could be confused without careful reading.
All tool names follow a consistent snake_case verb_noun pattern, with get_ as the dominant verb and list_/search_/compare_ as clear variants. No camelCase or inconsistent verb styles appear.
15 tools sits at the upper edge of a well-scoped read-only knowledge server. Each tool has a distinct purpose, but a few could be consolidated (e.g., industry experience as a search_cases filter) without losing capability.
The surface covers the main public knowledge workflows: service catalog, cases, evidence, provenance, pricing, clients, and production context. Minor gaps exist, such as no direct get_claim stable-identifier lookup and no list_cases endpoint, but search_knowledge and search_cases provide workarounds.