pcprice.watch used PC part prices
Server Details
Real eBay sold prices for used GPUs, CPUs, RAM and more in 7 markets, and eBay listing price checks.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 3 tools
Each tool targets a clearly distinct object: one judges an individual listing, one returns aggregate sold-price statistics for a model, and one enumerates the model catalog. Descriptions explicitly delineate the boundary between per-listing evaluation and model-level price stats, so misselection is unlikely.
All three tools follow the same snake_case verb_noun convention (check_*, get_*, list_*). The verbs also match the operation type (retrieve vs. evaluate vs. enumerate).
Three tools is on the thin side but coherent for a narrow read-only price-data service. Each tool earns its place, though one or two more (e.g. a model search) would round out the surface.
Core price workflow is covered (find model → get price stats → judge a listing), but there are notable gaps: no way to search models or listings by name, and despite 'tracked models' there is no add/remove tracking operation. Agents must already know a model ID or listing to act.
Available Tools
3 toolscheck_ebay_listingARead-onlyIdempotentInspect
Judge whether an eBay listing is a good price: compares its delivered price with what the same model actually sold for recently (great / fair / above typical / overpriced, and % vs the sold median), and lists cheaper listings that ship to the buyer.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | the eBay listing link the user pasted (ebay.com, ebay.co.uk, ebay.de, ebay.fr, ebay.it, ebay.es, ebay.pl, or an ebay.us/ebay.io share link) | |
| country | No | the buyer's ISO 3166-1 alpha-2 country code, e.g. DE; used for delivered price and shipping. Ask the user if unknown |
Output Schema
| Name | Required | Description |
|---|---|---|
| model | No | |
| reason | No | |
| status | Yes | |
| listing | Yes | |
| verdict | No | |
| page_url | Yes | |
| data_as_of | No | |
| sold_basis | No | |
| attribution | Yes | |
| buyer_country | No | |
| no_ship_to_buyer | No | |
| cheaper_alternatives | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive and open-world, so safety is covered; the description adds substantive context beyond that — that it benchmarks against actual recent sold prices, reports a % delta vs the sold median, and surfaces cheaper listings filtered by shipping to the buyer. It does not mention failure modes for unsupported links or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence that leads with the core judgment task and then qualifies it with the comparison basis and the secondary output. Dense but every clause carries information; no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With rich annotations, full schema coverage and an output schema, the description covers what the tool does and how its verdict is derived, which is enough for correct invocation. Minor gap: no note on what happens with an unsupported or expired listing link.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, including the supported domain list and the ISO country code semantics, so the schema carries the parameter burden. The description adds only the concept of 'delivered price' and shipping relevance, which is already implied by the country parameter description, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb+resource ('Judge whether an eBay listing is a good price') and enumerates the concrete output verdicts (great / fair / above typical / overpriced) plus the comparison basis (recent sold median). It is clearly distinguishable from siblings by operating on a listing URL rather than a model, though it never names get_used_price or list_tracked_models as alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Judge whether an eBay listing is a good price' plus the schema note about 'the eBay listing link the user pasted' makes the trigger context clear: the user has a specific listing and wants a price verdict. No explicit when-not conditions or sibling routing are given, 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.
get_used_priceARead-onlyIdempotentInspect
What a used PC part actually sells for on eBay: sold-price median and p25-p75 range (EUR) over the last 30 and 90 days, for one market or all seven pooled.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | the part model, e.g. 'RTX 3060', 'RX 7900 XT', 'Ryzen 7 5800X'; call list_tracked_models if unsure | |
| country | No | ISO country code of one of the scraped markets (US, GB, DE, FR, IT, ES, PL); omit for all markets pooled |
Output Schema
| Name | Required | Description |
|---|---|---|
| note | Yes | |
| model | Yes | |
| scope | Yes | |
| model_id | Yes | |
| msrp_usd | No | |
| page_url | Yes | |
| used_30d | No | |
| used_90d | No | |
| part_type | Yes | |
| data_as_of | Yes | |
| attribution | Yes | |
| all_conditions_30d | No | |
| used_30d_by_market | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the description is free to add real value and does: EUR currency, fixed 30- and 90-day aggregation windows, and pooling semantics when no market is given. It stops short of noting data freshness, coverage, or minimum sample sizes for the median.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, front-loaded with the value proposition, then the metric, unit, time windows, and scope. Nothing redundant and nothing padded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return shape need not be explained, and the annotations carry the safety profile; the description covers purpose, unit, and scoping. The remaining gap is routing guidance among the two siblings, which a one-param-tool description could still afford to include.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage the baseline is 3; the description earns above that by clarifying the country parameter's omission semantics ('for one market or all seven pooled'), which is the one non-obvious behavior in the schema. The 30/90-day framing is not a parameter, so no further lift is possible.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb-and-resource pair: returns sold-price median and p25-p75 range for a used PC part, with currency, time windows, and market scope all named. It does not explicitly distinguish itself from sibling check_ebay_listing (active-listing inspection) or list_tracked_models, so the agent must infer the split from the names.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied rather than stated: 'what a used PC part actually sells for' suggests pricing/valuation queries, and 'one market or all seven pooled' hints at when to pass country. There is no explicit when-not guidance and no pointer to check_ebay_listing or list_tracked_models as alternatives (the list_tracked_models hint lives only in the schema).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_tracked_modelsARead-onlyIdempotentInspect
The PC part models pcprice.watch tracks, with their IDs, optionally filtered by part type.
| Name | Required | Description | Default |
|---|---|---|---|
| part_type | No | optional filter: GPU, CPU, RAM, MOTHERBOARD, PSU, LAPTOP, IPHONE |
Output Schema
| Name | Required | Description |
|---|---|---|
| models | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile needs no repetition. The description adds that results carry IDs, which is useful, but says nothing about result size, ordering, or what happens with an invalid part_type—gaps that matter for a catalog listing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single compact sentence that front-loads the resource and appends the filter behavior; nothing is padded. Phrasing is slightly awkward ('PC part models pcprice.watch tracks') but efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema, rich annotations, and a fully documented single parameter, the description covers what an agent needs to decide and call. Only ordering/result-size expectations are left unstated, a minor gap given the structured fields already present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the single parameter enumerates its allowed values (GPU, CPU, RAM, etc.) directly in the schema. The description only restates 'optionally filtered by part type,' so baseline 3 applies—no added semantics or format detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific resource (PC part models tracked by pcprice.watch) and what is returned (their IDs), with an optional filter. It clearly reads as a catalog-listing tool, distinguishable from check_ebay_listing and get_used_price, though the listing verb itself is only implied by the name/title.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Mentions the optional part_type filter, which implies the main use case (enumerate the catalog, narrow by category). However, it gives no explicit guidance on when to prefer this over the sibling price/listing tools, nor any prerequisite or exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
- First observed
check_ebay_listing - First observed
get_used_price - First observed
list_tracked_models
Related MCP Connectors
Real eBay sold listings: 3 years of price history, real accepted Best Offer prices, 8 marketplaces.
Live eBay sold comps for AI agents: median sold price, middle range and recent sales for any item.
Live eBay market intelligence: underpriced listing scans, price distributions, flip margins.
Live second-hand price estimates with confidence for AI agents. Cached lookups free.
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