Skip to main content
Glama

sheetsdata-mcp

by io.github.octoco-ltd

search_parts

Read-onlyIdempotent

Search for electronic components by part number, description, or keyword. Start here — this is the best entry point for finding components. Queries all configured providers in parallel. Results are merged by MPN with indicative pricing and stock from each source. Each result includes datasheet_status so you know which parts have datasheets available for read_datasheet. Best with specific part numbers or keywords (e.g. 'STM32F103', 'buck converter 3A'). For spec-based discovery in natural language, use search_datasheets instead. When the calling org has a private parts library, matching org-uploaded parts are appended to the results with source='private_library' and any tags the team has applied — including private parts whose MPN, manufacturer, description, type, category, or tag matches the query.

DATASHEET STATUS VALUES:

  • 'ready' — extracted and indexed; call read_datasheet, search_datasheets, or analyze_image.

  • 'extracting' / 'in_progress' / 'queued' / 'pending' — extraction running or scheduled. Poll check_extraction_status every 5-10s until 'ready' or 'failed'. Typical time: 30s-2min.

  • 'not_extracted' — known part but datasheet hasn't been fetched yet. Trigger it via prefetch_datasheets (cheapest) or by calling read_datasheet (auto-triggers on first read).

  • 'no_source' — we couldn't find a public datasheet URL for this MPN. First, retry prefetch_datasheets in 10-30s (the URL resolver re-runs and often finds a source on the second pass). If still 'no_source', the agent can upload the PDF manually via request_datasheet_upload + confirm_datasheet_upload (see those tools). Org-uploaded datasheets are private to the org.

  • 'unsupported' — PDF exists but can't be extracted (scanned image-only, encrypted, or corrupted). Upload a clean text-based PDF via request_datasheet_upload to override.

  • 'failed' / 'error' — extraction errored. The response includes the error reason. Retry via prefetch_datasheets or escalate to support.

  • 'rejected' — input wasn't a real MPN (bare value like '100nF', description, or reference designator). Fix the input and re-call.

  • 'deduplicated' — another part in the family already has this datasheet; same content is returned under the primary MPN.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results per provider (default 20). With providers='all', total results can be up to 3× this value (one set per provider).
queryYesSearch query (part number or keyword)
providersNoWhich providers to query: 'all' (default), 'jlcpcb', 'mouser', or 'digikey'all

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / limit / description
      Previous value: -"Max results per provider (default 20)"New value: +"Max results per provider (default 20). With providers='all', total results can be up to 3× this value (one set per provider)."
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Even with readOnlyHint=true, idempotentHint=true, and destructiveHint=false, the description adds substantial behavioral context: queries providers in parallel, merges results by MPN, includes private library behavior, and details all datasheet_status values with retry/fallback actions. This goes far beyond annotations without contradiction.

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 long but structured with a clear opening, parameter context, and a detailed datasheet_status breakdown. Every section is purposeful; the length is justified by the need to explain eight status values. Not overly verbose for the complexity.

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

Completeness5/5

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

No output schema exists, but the description fully covers return behavior (merged results, pricing/stock, datasheet_status), provider options, private library augmentation, and related tools for further actions. Complete for a search tool with rich edge cases.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value with example queries ('STM32F103', 'buck converter 3A') and clarifies the results-merge behavior tied to providers/limit, though the schema already covers the 3× limit detail. Slight extra enrichment for parameter use.

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 the tool searches for electronic components by part number, description, or keyword, and identifies it as the best entry point. It explicitly distinguishes from sibling search_datasheets by directing spec-based natural language discovery there.

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?

Provides explicit guidance: 'Start here — this is the best entry point' and 'For spec-based discovery in natural language, use search_datasheets instead.' Also explains when to use prefetch_datasheets or read_datasheet based on datasheet_status, giving clear when-to vs alternatives.

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

All sheetsdata-mcp toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.8/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: search_parts finds parts, search_datasheets searches extracted content, read_datasheet reads sections, analyze_image extracts visual data, compare_parts batches comparisons, find_alternative finds substitutes, check_design_fit validates specs, prefetch/check/manage extraction status, and request/confirm handle uploads. The descriptions cross-reference each other to further eliminate ambiguity.

Naming Consistency5/5

All 12 tools follow the verb_noun snake_case pattern consistently (analyze_image, check_design_fit, compare_parts, prefetch_datasheets, request_datasheet_upload, etc.). No mixing of camelCase, verb styles, or vague generic names.

Tool Count5/5

12 tools is ideal for a datasheet-centric server, covering the full workflow from part discovery and datasheet extraction to analysis, validation, comparison, and upload. The count is neither bloated nor thin, and every tool earns its place.

Completeness5/5

The tool surface is comprehensive for the domain: part search, spec-based semantic search, detailed part data, datasheet reading (section/search), image analysis, design-fit checking, part comparison, alternative finding, extraction lifecycle management, and manual datasheet upload (request+confirm). No obvious dead ends or critical missing operations.

Resources