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zxhwolfe-dev

AI Workstation Open Source Intelligence MCP Server

find_alternatives

Read-onlyIdempotent

Find verified candidate alternatives for open-source AI projects while preserving your constraints, with transparent evidence to support informed comparison.

Instructions

Find verified candidate alternatives while preserving constraints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
localeNoen
project_idYes
request_idNo
constraintsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the description does not need to cover safety. It adds modest behavioral value by stating results are 'verified' and that constraints are 'preserving,' which suggests a non-relaxing, evidence-based result set. It does not contradict the annotations, and it offers no extra disclosure about pagination, output shape, or edge cases.

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?

The description is a single short sentence with no filler or redundancy, and the core action is front-loaded. It is concise but somewhat terse, sacrificing detail that would help an agent select it confidently among siblings. Still, on the conciseness dimension it is well structured.

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

Completeness2/5

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

Despite having an output schema and useful annotations, the description lacks usage guidance, parameter clarification, and sibling differentiation. The tool has four parameters, zero schema description coverage, and several closely related siblings, so more context is needed for reliable selection and invocation. The current description is too thin to be considered complete.

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

Parameters2/5

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

Schema description coverage is 0%, so the description bears the burden of explaining parameters, but it only hints at 'constraints' and does not explain project_id, locale, request_id, or the constraints structure. The word 'constraints' maps to one parameter, but the other three are left entirely to their names and schema types. This is insufficient compensation for the lack of schema descriptions.

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

Purpose4/5

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

The description states a specific verb ('Find') and resource ('verified candidate alternatives') and adds a meaningful qualifier ('preserving constraints'). It is not a tautology and gives the agent a basic sense of the tool's function. However, it does not explicitly say what the alternatives are for (e.g., projects) or how it differs from siblings like compare_ai_projects, so it is clear but not fully differentiated.

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

Usage Guidelines2/5

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

The description implies the tool should be used when constraints need to be preserved, but it gives no explicit when-to-use or when-not-to-use guidance. It does not name alternatives like search_ai_projects or compare_ai_projects, nor does it state which sibling to choose instead. An agent is left to infer the appropriate context from the name and a single phrase.

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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