get_frasma_profileReturns the verified public profile, focus areas, sectors, commercial boundaries, and discovery URLs for Frasma.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | Response language: it or en. Defaults to it. |
| Name | Required | Description | Default |
|---|---|---|---|
| kind | Yes | ||
| name | Yes | ||
| focus | Yes | ||
| founder | Yes | ||
| sectors | Yes | ||
| location | Yes | ||
| discovery | Yes | ||
| languages | Yes | ||
| description | Yes | ||
| commercialLimits | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, and non-destructive behavior, so the safety profile is fully handled. The description adds useful context by specifying that the profile is 'verified public' and enumerating the distinct content categories returned, which signals the scope and sensitivity of the data. Nothing in the description contradicts the 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 a clear verb and a concrete list of returned content. There is no filler, repeated tool name, or redundant restatement of schema details.
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 tool with zero required parameters, one optional locale, a full output schema, and annotations covering side effects, the description plus schema provide everything an agent needs to decide whether and how to call it. No hidden prerequisites, caveats, or return-value explanations are needed.
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%: the sole parameter 'locale' is fully documented with its enum values and default. The description does not add parameter-level detail beyond the schema, so the baseline score of 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?
The description uses a specific verb ('Returns') and names a unique resource: the verified public profile for Frasma, including focus areas, sectors, commercial boundaries, and discovery URLs. This makes it clearly distinguishable from sibling tools like get_diagnostic_framework or search_frasma_knowledge without needing to open their 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 intended use is implied: call this when you need Frasma's public profile or commercial-boundary data. However, there is no explicit statement of when to prefer this tool over alternatives or any exclusions. For a simple read-only lookup this is acceptable, but the guidance is not fully explicit.
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.
Most tools have clearly distinct roles: getters return canonical framework/profile objects, search handles broad knowledge queries, and the two prepare tools validate different payloads. However, get_frasma_profile and search_frasma_knowledge overlap on sectors and commercial boundaries, and both prepare tools produce handoff payloads, so there is a small amount of potential confusion.
All tool names follow a consistent lowercase snake_case verb_noun pattern: get_, prepare_, and search_. The verbs accurately describe the action and the nouns clearly identify the object, making the set predictable and easy to navigate.
With exactly five tools, the server is well-scoped for its purpose: retrieving verified knowledge, accessing canonical profiles/frameworks, and preparing validated handoff payloads. Each tool earns its place and there is no sense of bloat or thinness.
The set covers the core pre-submission workflow: retrieving verified facts, mapping diagnostics, and validating project briefs. The only notable gap is that no tool actually submits or confirms the POST /api/request-process-assessment; the descriptions deliberately leave that to an external user action.