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Tethered Trust

Check compatibility

check_compatibility
Read-only

Will this exact part or accessory work with that exact device? Use before recommending or buying. Covers anything that has to fit, plug in, mount, run on or charge with something someone owns. Verified catalog (specs checked against cited manufacturer sources): computers & consoles (RAM, M.2 SSDs, USB-C chargers), phones/tablets/watches (USB-C PD, MagSafe, Qi2, watch bands), cameras (lens mounts, adapters, batteries, memory cards), cordless power tools (battery platforms), printers (ink, toner, drums), appliances (refrigerator water and air filters, installation fit), vacuums (bags, filters, batteries, robot-vacuum parts), air quality (purifier filters, HVAC media, humidifier pads), e-bikes (batteries and chargers by drive system), smart home (Matter, Thread, Zigbee, hubs, bulb bases), PC building (CPU sockets, cooler mounts, memory, clearances, power), cars & EV charging (tire sizes by trim, NACS/J1772/CCS1 and approved adapters, home chargers; a starter set of models) and TVs, consoles & streaming (VESA mounts, weight and screen size, console generations, app platforms). Products outside the catalog in any category are read by AI in terms of the same standards and returned as unverified. Returns a decision (supported / conditional / incompatible / insufficient_evidence), the rules checked, caveats, required accessories, the questions to ask when it can't decide, evidence links and a signed receipt. When the text matches several models it returns each candidate's answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailNoOptional extra identifiers: machine type, revision, year, region, serial range.
existingYesWhat the person already has, ideally with the exact model number, e.g. 'Lenovo ThinkPad T480', 'Sony A7 IV', 'DeWalt DCD800', 'HP OfficeJet Pro 9015e', 'iPhone 16 Pro', 'HomePod mini'.
candidateYesWhat they want to buy: a model/part number or key specs, e.g. 'Crucial CT16G4SFRA32A', '1TB M.2 2230 NVMe', 'DCB205', 'HP 962XL', 'Canon RF 24-105mm f/4L', 'Eve Energy'.
existing_idNoOptional: an exact product id (from resolve_product, suggestions or a candidates list) instead of existing text.
candidate_idNoOptional: an exact product id instead of candidate text.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
fitNo
termsNo
checksNo
noticeNo
caveatsNo
missingNo
receiptNoSigned receipt (EdDSA JWS) proving Tethered Trust issued this verdict unaltered. Not a guarantee the verdict is correct.
summaryYes
verdictYes
check_idYes
decisionYessupported | conditional | incompatible | insufficient_evidence. Act on this, not on confidence alone.
evidenceNo
verifiedNoTrue only when every spec came from a manufacturer source with a recorded check date.
next_stepNo
questionsNoExactly what to ask, measure or look up next. blocking=true means no decision until answered.
candidatesNoPresent when the text matched several models: each option with its own verdict.
conditionsNoWhat must be true for a conditional answer (accessory, dependency).
confidenceYesEvidence score from fixed rules (source tier, rule strength). Not a calibrated probability: act on decision, conditions and questions.
constraintsNo
valid_untilNoWhen this answer should be re-checked (based on how fresh its sources are).
requirementsNo
decision_helpNo
required_accessoriesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, destructiveHint=false, openWorldHint), so the bar is lower; the description adds real value by disclosing the verified-vs-unverified catalog boundary, the signed receipt, and the four-value decision taxonomy. It does not mention latency, rate limits, or failure modes, keeping it from 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose and usage triggers are correctly front-loaded, but the middle is a single sprawling sentence enumerating every catalog category and sub-item, which is far denser than an agent needs to select the tool. The return-value sentence is useful, but the taxonomy list bloats the definition.

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?

With an output schema present, return values need not be enumerated, yet the description still summarizes decisions, caveats, and multi-candidate behavior, and it explains the out-of-catalog fallback. An agent has enough to call it correctly; only the absence of any exclusion/alternative guidance leaves a small gap.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents each field with examples, including the id-based substitutes. The description adds no additional semantics for existing/candidate/detail or the *_id parameters, so the baseline 3 applies.

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?

Opens with a specific verb+resource question ('Will this exact part or accessory work with that exact device?') and immediately frames the two-input comparison. It is clearly distinguishable from siblings like check_fit, check_batch, or find_parts because it names both sides of the pair being compared.

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?

'Use before recommending or buying' gives an explicit trigger condition and the query even quotes user phrasing. It never names an alternative tool or states when NOT to use this one (e.g. batch cases belonging to check_batch), so it falls 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.

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