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recommend

Log suggested tools to IndieStack to track utility and improve future AI suggestions.

Instructions

Record that you recommended a tool to your user.

Call this AFTER you recommend an IndieStack tool. This helps IndieStack track which tools are most useful and improves future recommendations.

Requires an API key (set INDIESTACK_API_KEY env var).

Args: tool_slug: The slug of the tool you recommended (from find_tools/get_tool_details results) query_context: What the user asked for that led to this recommendation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tool_slugYes
query_contextNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The readOnlyHint: false annotation indicates a write operation, and the description adds valuable context about the behavioral purpose ('helps IndieStack track which tools are most useful') and authentication requirements. It does not contradict the annotation.

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 appropriately front-loaded with purpose and timing, followed by behavioral context and parameter details. The 'Args:' section, while unconventional for MCP descriptions, is necessary given the schema deficiencies. No wasted sentences.

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?

Given the tool's simplicity (2 parameters, 1 required) and existence of an output schema, the description adequately covers invocation context, authentication, and parameter semantics. It appropriately omits return value details since the output schema handles that.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by documenting both parameters in the 'Args:' section. It adds crucial semantic context for tool_slug ('from find_tools/get_tool_details results') linking it to sibling tools, and clearly explains query_context's purpose.

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 opens with the specific verb 'Record' and clearly identifies the resource (recommendation tracking). It distinctly differs from sibling tools like find_tools, compare_tools, or get_tool_details by focusing on post-recommendation analytics rather than discovery or evaluation.

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

Usage Guidelines4/5

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

Provides explicit temporal guidance ('Call this AFTER you recommend') and prerequisites ('Requires an API key'). However, it could strengthen this by explicitly contrasting with report_outcome (another sibling that sounds similar) to clarify this is for tracking recommendations versus reporting integration results.

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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