recommend_mcp_for_taskRecommend the best MCP servers for a natural-language task. Returns top 3 ranked picks with reasoning, install commands, and quality scores. Use this when the user asks for the right MCP server for a task they want to do.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | Natural-language description of the task, e.g. "read PDFs and write to S3". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses the return format (top 3 picks, reasoning, install commands, quality scores) and implies it does not execute actions beyond recommending. It lacks caveats about data sources or edge cases, but for a read-only recommendation tool this is adequate.
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 exactly two sentences with no filler. It front-loads the purpose and output, then adds the usage trigger. Every sentence 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?
For a one-parameter tool with a fully described schema and no output schema, the description provides enough context: purpose, output contents, and when to use it. It is complete for its simplicity.
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?
The input schema already fully documents the 'task' parameter with an example. The description only restates 'natural-language task' without adding new semantic detail, so it stays at the baseline for 100% schema coverage.
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 the tool's exact function: recommend the best MCP servers for a natural-language task, and specifies the concrete output (top 3 ranked picks with reasoning, install commands, and quality scores). This clearly distinguishes it from sibling tools like search_mcp_servers or compare_servers.
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 explicitly says 'Use this when the user asks for the right MCP server for a task they want to do,' providing clear and direct usage context. However, it does not mention when not to use it or point to alternatives among sibling tools.
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 clearly distinct purpose: server-level assessment vs tool-level trust, comparison vs installation, task-based recommendation vs search. No two tools overlap significantly.
All tool names follow a consistent verb_noun pattern in snake_case, making the set predictable and easy to navigate.
Six tools is well-scoped for a server discovery and evaluation service, covering search, recommendation, comparison, trust assessment, and installation without excess.
The tool set covers the core workflows of finding, evaluating, and installing MCP servers. A minor gap is the lack of a dedicated 'get server details' tool, but search likely covers this need.