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kothar

kothar

by yahiaklk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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recommend_for_goal

Break down a complex goal into sub-tasks and find matching MCP servers for each part. Use optional project context for better recommendations.

Instructions

Decompose a multi-part goal into sub-queries and recommend MCP servers for each part. Splits on hard boundaries: '. ', '; ', ' then ', ', then ', ' and then ' (not bare ' and '). project: optional project context prepended to each sub-query for richer semantic matching. Example: goal="integrate GitHub. add Stripe payments", project="Python FastAPI backend"

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": {
      "goal": "<goal>"
    },
    "name": "recommend_for_goal"
  }
}

Input Schema

NameRequiredDescriptionDefault
goalYes
projectNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations provided, so description carries the burden. It explains splitting behavior and project prepending but does not disclose whether the tool is read-only or destructive, side effects, or error handling. Adequate but lacks safety profile.

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

Conciseness4/5

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

Three sentences plus example, no wasted words. Front-loaded main purpose, then splitting details, then optional parameter. Could be more structured but efficient.

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

Completeness3/5

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

Output schema exists, so return values not required. Explains input and behavior well, but lacks usage guidelines for sibling differentiation and does not mention prerequisites or error conditions. Fairly complete for a tool with output schema.

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

Parameters4/5

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

Schema description coverage is 0%, yet the description adds meaning: 'goal' is a multi-part goal with splitting rules, 'project' is optional context prepended. This goes beyond the schema's minimal type info.

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?

Clearly states the tool decomposes multi-part goals into sub-queries and recommends MCP servers. Distinguishes from siblings like recommend_next and recommend_for_project by focusing on multi-part goals and splitting logic.

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?

Provides splitting rules and optional parameter description but does not explicitly state when to use this tool versus alternatives like recommend_for_project or recommend_next. Usage is implied for multi-part goals.

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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