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allmcps-server

allmcps-server

by Jackalope-Dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

recommend_mcp_stack

Read-only

Recommend a small stack of complementary MCP servers for a developer role or workflow, such as fullstack, data, devops, or browser, from live directory search.

Instructions

Recommend a small stack of complementary MCP servers for a role or workflow (e.g. "fullstack web app", "data science", "devops on AWS"), drawn from a live directory search.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "role": "<role>"
    },
    "name": "recommend_mcp_stack"
  }
}

Input Schema

NameRequiredDescriptionDefault
roleYesDeveloper role or task (e.g. "fullstack", "data", "devops", "browser")

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.3

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds one useful behavioral fact beyond that: results are 'drawn from a live directory search', implying dynamic content. It does not characterize how many servers are returned or how the stack is composed, so it stays at a solid 3 given annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that names the action, the output, the input, and parenthetical examples. Every clause earns its place with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, but the description conveys what comes back ('a small stack of complementary MCP servers') and where it comes from, which is adequate for an agent to call it. It could go slightly further on the shape of the recommendation, but nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single 'role' parameter, so the schema already carries the load and a 3 is the baseline. The description adds workflow-level examples ('fullstack web app', 'devops on AWS') that are slightly richer than the schema's short enum-like hints, but no syntax or format guidance beyond that.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (recommend), a specific resource (a small stack of complementary MCP servers), the input dimension (role or workflow), and examples. It is clearly distinguishable from siblings like search_mcp_servers (raw search) and compare_mcp_servers (comparison), since it produces a curated complementary set.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'for a role or workflow' with concrete examples implies the intended use case, which is enough to know when to reach for it. However, it names no alternatives (e.g. search_mcp_servers for raw lookups) and states no when-not conditions, leaving routing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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