Skip to main content
Glama

lib_recommend_part

Find a purchasable component that meets electrical requirements. Specify category and specs to get ranked part recommendations with codes and pricing.

Instructions

Recommend a purchasable part given electrical requirements.

Args: category: Component category keyword to search (e.g. "LDO regulator", "N-channel MOSFET", "ferrite bead", "ESD protection"). requirements: Dict of electrical parameter hints used for post-search filtering. Common keys: voltage_v, current_a, vgs_v, rds_on_mohm, psrr_db, capacitance_uf, resistance_ohm. Values can be numbers (min) or {"min": x, "max": y} dicts. package: Optional SMD package filter (e.g. "SOT-23", "SOIC-8"). only_basic: Prefer JLCPCB basic parts (lower assembly cost). source: Parts source: "jlcsearch", "nexar", or "digikey". max_results: Maximum number of recommendations to return.

Returns: Ranked list of part recommendations with LCSC code, MPN, package, price.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNojlcsearch
packageNo
categoryYes
only_basicNo
max_resultsNo
requirementsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full transparency burden. It explains post-search filtering, ranking, source options, and the return format, adding real behavioral context. It does not disclose potential network/external API dependencies or error behavior when no parts match, which prevents a 5.

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?

The docstring is concise and well-structured: a one-line purpose followed by Args and Returns sections. Every line adds useful detail, including examples and type hints, with no redundancy or fluff.

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?

The description thoroughly covers all input parameters, the nested requirements object, and expected outputs, especially given the existence of an output schema. It lacks explicit error semantics or ranking criteria, but the overall workflow is well-specified and sufficient for an agent to invoke the tool correctly.

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?

Schema description coverage is 0%, so the description's 'Args' section fully compensates. It explains all six parameters with practical examples, allowed values, and meaning—especially the nested 'requirements' dict with min/max formats and common keys—far exceeding raw schema information.

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 'Recommend a purchasable part given electrical requirements,' which is a specific verb+resource+context statement. It also details the return value ('Ranked list of part recommendations') and parameter scope, clearly distinguishing it from sibling search tools like lib_search_components or lib_find_alternative_parts.

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?

The use case is clear: recommend a part based on electrical specs, with examples of category keywords and requirement keys. However, it does not explicitly state when to use this vs alternatives or provide exclusionary guidance, so it stops short of a 5.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Valxyria/kicad-mcp-pro'

If you have feedback or need assistance with the MCP directory API, please join our Discord server