Find verified candidate alternatives while preserving constraints.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | en | |
| project_id | Yes | ||
| request_id | No | ||
| constraints | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 covered. The description adds mild behavioral context by saying alternatives are 'verified' and that constraints are 'preserving,' but it does not disclose anything further such as result scope, ordering, or data sources. This is adequate but not rich.
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. It communicates the core action and the key constraint-preservation behavior in eight words, which is ideal for conciseness.
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 description is short, but annotations and an output schema carry some of the burden. However, it does not explicitly say what the alternatives are alternatives to, what 'verified' means, or when to pick this over sibling tools. It is minimally viable for an agent to infer usage, but not fully complete.
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 compensate by explaining project_id, locale, request_id, or constraints beyond the generic word 'constraints.' Nested schema documentation for ConstraintInput helps somewhat, but the tool-level description adds minimal parameter meaning and leans on the parameter names themselves.
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 states a specific action and resource: find verified candidate alternatives. The phrase 'while preserving constraints' clearly ties to the constraints parameter, and the annotation title adds that these are open-source project alternatives. It does not explicitly name a sibling tool, but the purpose is distinct enough from browse/search/compare tools.
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 no guidance on when to use this tool versus siblings like search_ai_projects or compare_ai_projects. There is no mention of prerequisites, exclusions, or the intended scenario (e.g., finding alternatives to a specific project). Usage is only implied by the tool name and the required project_id parameter.
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 has a generally distinct role: browsing radar views, searching projects, getting facts, comparing, composing stacks, and finding alternatives. A couple of tools—notably browse_radar_projects and search_ai_projects—could be confused, but their descriptions clarify exploratory browsing versus requirement-driven search.
All tool names follow a consistent snake_case verb_noun pattern: browse_*, get_*, search_ai_projects, compare_ai_projects, compose_ai_stack, find_alternatives. The naming makes the action and target object immediately clear across the entire set.
Nine tools is a well-scoped size for an open-source AI intelligence and decision-support server. Each tool covers a distinct part of the workflow without feeling bloated or redundant.
The set covers the main workflow well: overview, browsing, search, project facts, license evidence, comparison, stack composition, and alternatives. Minor gaps like project tracking/history or export utilities are non-essential for this kind of intelligence/decision-support surface.