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bigl34

PAL MCP Server

by bigl34
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

clink

Forward user requests to configured external AI CLIs (Claude, Codex, Gemini) to reuse their capabilities within existing workflows.

Instructions

Link a request to an external AI CLI (Gemini CLI, Qwen CLI, etc.) through PAL MCP to reuse their capabilities inside existing workflows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleNoOptional role preset defined for the selected CLI (defaults to 'default'). Roles per CLI: claude: codereviewer, default, planner; codex: codereviewer, default, planner; gemini: codereviewer, default, planner
modelNoOptional CLI model name. Use native to suppress clink model injection and let the CLI default apply.
imagesNoOptional absolute image paths or base64 blobs for visual context.
promptYesUser request forwarded to the CLI (conversation context is pre-applied).
cli_nameYesConfigured CLI client name (from conf/cli_clients). Available: claude, codex, gemini
continuation_idNoUnique thread continuation ID for multi-turn conversations. Works across different tools. ALWAYS reuse the last continuation_id you were given—this preserves full conversation context, files, and findings so the agent can resume seamlessly.
absolute_file_pathsNoFull paths to relevant code

Tool Definition Quality

B3.4/5.0
Behavior2/5

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

Annotations indicate read-only is false, but the description adds little beyond that; it mentions pre-applying conversation context but omits side effects, auth needs, or rate limits.

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, well-structured sentence with no wasted words.

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?

With 7 parameters, no output schema, and complex interactions (multi-CLI, role presets), the description is too terse and fails to explain response format or error conditions.

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%, so the description adds minimal extra meaning; baseline score of 3 is appropriate.

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 clearly states it links a request to an external AI CLI to reuse capabilities, distinguishing it from direct action siblings like analyze or chat.

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?

It implies usage for integrating external CLI capabilities into workflows but lacks explicit when-not-to-use or alternative tool guidance.

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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