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SheetsData MCP

by octoco-ltd
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JavaScript
Remote

Search Datasheets

search_datasheets
Read-onlyIdempotent

Search extracted datasheets by specification to find electronic components matching natural-language needs like low-noise LDOs with PSRR above 70dB, returning part numbers and relevance scores.

Instructions

Costs 2¢ per call. Semantic search across every datasheet already extracted (not every part that exists), for spec-based discovery such as 'low-noise LDO with PSRR above 70dB'. Returns matching chunks with part_number and a 0-100 relevance_score. To find a specific part by number, use search_parts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 3)
queryYesNatural language search query
section_typeNoOptional: limit search to a specific section typeall

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.4.0
    • changedInput schema / properties / limit / default
      Previous value: -15New value: +3
    • changedInput schema / properties / limit / description
      Previous value: -"Max results (default 15)"New value: +"Max results (default 3)"
  2. Changed2 schema fields changedv1.3.2
    • changedInput schema / properties / limit / default
      Previous value: -10New value: +15
    • changedInput schema / properties / limit / description
      Previous value: -"Max results (default 10)"New value: +"Max results (default 15)"
  3. First observedv1.0.0

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint, non-destructive), but the description adds genuinely new operational facts: a 2¢ per-call cost and the shape of the return ('matching chunks with part_number and a 0-100 relevance_score'). It does not discuss empty-result behavior or how limit affects result quality, which keeps it short of 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?

Four short sentences, all front-loaded: cost first, then scope, then return shape, then the alternative. Every sentence carries a distinct fact and none restates the name or title.

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?

There is no output schema, yet the description still supplies the return shape and relevance scale, plus corpus scope, cost, and the disambiguation rule. An agent has everything needed to decide between this and search_parts and to interpret the response.

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 description coverage is 100%, so the baseline is 3, but the description adds real meaning for the required query parameter by showing the expected style (spec-based natural-language phrase rather than a part number). The limit and section_type parameters are left entirely to the schema.

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?

States a specific verb (semantic search) and resource (extracted datasheets) and immediately bounds the corpus with 'not every part that exists'. It also names the sibling it is not: search_parts for lookup by part number, so an agent can distinguish it without opening either schema.

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?

Gives the positive trigger with a concrete example ('low-noise LDO with PSRR above 70dB') and an explicit when-not rule routing part-number lookups to search_parts. Both the condition and the alternative are named, leaving nothing to inference.

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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