Skip to main content
Glama

recommend

Filter and rank AI agents by category, score, tier, and use case. Returns candidates with reasons for match. Use this to get a shortlist based on buyer constraints.

Instructions

Constraint-driven recommendation across Hlido's reviewed agents. Pass any combination of: category, min_score, tier, use_case, max_results. Returns ranked candidates each with a why_match line. Use this when you have buyer constraints (budget, category, capability) and want Hlido's filtered shortlist instead of one-by-one trust_check calls.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
constraintsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It states the tool returns ranked candidates with a why_match line, implying a read-like operation. However, it does not explicitly mention safety, authentication, or error behavior, but the return format and ranking behavior are adequately disclosed.

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 compact and front-loaded with purpose, followed by parameters and usage. It is not overly verbose, though the parameter list could benefit from mentioning the nested structure for clarity.

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 no output schema, the description provides a reasonable return format (why_match line). The tool is moderately complex, and the description covers purpose, usage, and output. It lacks details on edge cases like empty results or max_results defaults, but overall it is sufficiently complete for an agent to decide.

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% for the top-level constraints parameter, and the description only lists parameter names without explaining the wrapping constraints object. This could mislead an agent into passing them as top-level parameters. The nested schema properties have descriptions, but the description does not compensate for the structural ambiguity.

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?

Description clearly states the tool performs constraint-driven recommendation across Hlido's reviewed agents, with a specific verb and resource. It distinguishes itself from sibling tools like trust_check by offering a filtered shortlist rather than one-by-one checks.

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

Usage Guidelines5/5

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

Explicit usage guidance is provided: 'Use this when you have buyer constraints (budget, category, capability) and want Hlido's filtered shortlist instead of one-by-one trust_check calls.' This clearly indicates when to use this tool versus alternatives.

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