Compare two to five projects in one explicit decision context.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | en | |
| context | No | ||
| criteria | No | ||
| request_id | No | ||
| project_ids | Yes |
| 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, idempotentHint, and destructiveHint=false, so the description adds little beyond the project-count constraint. It does not mention output format, errors, or behavior for invalid counts; the output schema may cover return values, but the description itself contributes minimal behavioral transparency. No contradiction exists.
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 sentence with no filler and front-loads the core action and constraint. It is appropriately concise for a tool whose annotations and output schema carry the safety and return-value burden.
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?
Given five parameters, zero schema descriptions, and a sibling list with overlapping comparison/search tools, this one-line description is not enough for reliable selection and invocation. The output schema reduces the need to document return values, but context/criteria semantics and usage boundaries remain missing.
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%, so the description must compensate, but it only clarifies that project_ids should contain two to five projects. The semantics of context, criteria, locale, and request_id are left entirely unexplained, making it hard for an agent to invoke the tool with correct parameter intent.
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 ('Compare'), names the resource ('projects'), and adds a precise cardinality ('two to five') plus a decision context. This clearly distinguishes it from siblings like search_ai_projects or find_alternatives, and aligns with the annotation title 'Compare open-source AI projects'.
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 phrase 'in one explicit decision context' implies the intended use case, and the range 'two to five' sets a boundary. However, there is no explicit guidance on when to prefer this tool over siblings such as find_alternatives or browse_radar_projects, and no exclusions are stated.
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.