get_diagnostic_frameworkReturns the diagnostic method, evidence to collect, fit criteria, and commercial limits.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | Response language: it or en. Defaults to it. |
| Name | Required | Description | Default |
|---|---|---|---|
| steps | Yes | ||
| title | Yes | ||
| fitCriteria | Yes | ||
| introduction | Yes | ||
| poorFitCriteria | Yes | ||
| commercialLimits | Yes |
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, so the safety profile is fully covered. The description adds the list of returned content, which is more about output than behavior, and no additional traits such as localization defaults are mentioned beyond the schema. It does not contradict 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?
A single sentence states the verb, resource, and content components without any filler. The most important information is front-loaded, and every word earns its place.
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?
This is a simple read-only getter with one optional parameter, full schema coverage, an output schema, and comprehensive annotations. Nothing an agent needs to invoke it correctly is missing from the available structured data.
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%, with the only parameter 'locale' fully documented by an enum and a default. The tool description adds no parameter-specific meaning, but with full schema coverage the baseline of 3 is appropriate.
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 the specific verb 'Returns' with a clear resource ('diagnostic framework') and enumerates the framework's contents: diagnostic method, evidence to collect, fit criteria, and commercial limits. It is clear enough to separate from sibling tools like get_frasma_profile or search_frasma_knowledge, though it does not explicitly name an alternative.
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?
There is no guidance on when to use this tool versus the siblings, nor any mention of prerequisites or exclusions. The description only states what is returned, leaving the agent to infer appropriate usage from the tool name and context.
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.