Used-car listings in Portugal: search, price statistics, comparables and articles.
Given one listing, find comparable ones: same make, similar year, mileage and price band. Use this when the user likes a car but wants alternatives — 'find me something similar', 'the same but cheaper', 'what else is around this price'. Pass maxPrice for the cheaper case; without it, comparables are drawn from a band around the reference price. Comparability is by structure (make/model/year/km/price), not by text similarity: it answers 'is this well priced for what it is', which a keyword search cannot.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Slug of the reference listing, as returned by search_cars or get_car_detail. | |
| limit | No | Maximum number of comparables (default 6, max 20). | |
| maxPrice | No | Optional budget ceiling, in EUR. Use it for 'similar but cheaper'; it overrides the default band around the reference price. |
| Name | Required | Description |
|---|---|---|
| cars | Yes | |
| total | Yes | Comparables returned. |
| market | Yes | |
| currency | No | |
| reference | Yes | The listing the comparables are measured against, echoed back for the comparison. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds meaningful behavioral details beyond those: the default price-band behavior when maxPrice is omitted, that maxPrice overrides the band, and the structural nature of matching. This gives an agent a clear model of how the tool behaves at runtime without contradicting the annotations.
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?
The description is tightly structured: the core verb and resource come first, followed by trigger examples, then parameter behavior, then a sibling-differentiation note. Every sentence adds distinct information and there is no repetition of the schema fields. It is dense but not bloated.
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?
For a tool with moderate complexity and a rich output schema, the description covers all essential dimensions: what it does, when to use it, parameter behavior, and how it differs from related tools. It does not need to explain return values because an output schema exists. An agent has enough context to select and invoke this tool correctly in the appropriate scenario.
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%, so the baseline is 3. The description adds value beyond the schema by explaining the semantics of maxPrice ('Use it for 'similar but cheaper'; it overrides the default band around the reference price') and by indicating that slug is the reference listing. These use-case-oriented explanations help the agent map user intent to parameters more reliably than the schema descriptions alone.
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?
The description states a specific action with a resource: 'Given one listing, find comparable ones' and defines comparability precisely (same make, similar year, mileage and price band). It also distinguishes itself from sibling tools by clarifying that it is structural, not text-based: 'Comparability is by structure (make/model/year/km/price), not by text similarity'. This leaves no ambiguity about what the tool does and how it differs from search_cars_by_description.
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?
The description gives explicit trigger patterns: 'Use this when the user likes a car but wants alternatives' with concrete natural-language examples ('find me something similar', 'the same but cheaper'). It also explains when to pass maxPrice. However, while it implies the alternative domain with 'which a keyword search cannot', it does not explicitly name the sibling tool (e.g., search_cars_by_description) that should be used instead for text-similarity searches, so the routing guidance is slightly incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the complete record of one used-car listing in Portugal by its slug. Use this after search_cars when the user asks about a specific car: full description, features, photos, seller, and the original source URL to cite.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Unique listing slug, as returned by search_cars. |
| Name | Required | Description |
|---|---|---|
| km | No | |
| url | Yes | Canonical listing page on this market's domain. |
| body | No | |
| fuel | No | |
| make | Yes | |
| slug | Yes | Stable identifier; pass to get_car_detail or find_similar_cars. |
| year | No | |
| color | No | |
| doors | No | |
| model | Yes | |
| price | No | null means the source does not publish a price (on request). |
| seats | No | |
| title | Yes | |
| origin | No | NACIONAL = domestic; IMPORTADO = imported. |
| photos | No | Up to 5 image URLs. |
| source | No | Aggregator platform the listing came from; null when published directly with us. |
| variant | No | |
| currency | Yes | ISO 4217 code of this market. |
| district | No | |
| engineCc | No | |
| features | No | |
| power_cv | No | Engine power in metric horsepower (CV/PS). |
| condition | No | |
| sourceUrl | No | Original listing URL at the source. Cite this alongside url. |
| sellerType | No | |
| description | No | |
| publishedAt | Yes | ISO 8601 date the listing was first seen at the source. |
| transmission | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral value by enumerating what the returned record includes—full description, features, photos, seller, and original source URL to cite—which helps the agent know what to expect beyond the output schema.
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?
Two sentences with no filler; the first sentence states the core action and scope, and the second provides usage context and return-value expectations. Every clause earns its place, and the most important information is front-loaded.
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?
Given a single well-documented required parameter, an output schema, and annotations covering safety and idempotency, the description supplies the remaining contextual need: when to invoke it and what the result is used for. Nothing critical is missing for an agent to select and call the tool correctly.
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?
The input schema already provides 100% coverage for the only parameter, slug, describing it as the unique listing slug returned by search_cars. The description merely repeats 'by its slug' without adding new semantic detail, so the schema carries the full burden and the baseline of 3 is appropriate.
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?
The description starts with a specific verb and resource: 'Get the complete record of one used-car listing in Portugal by its slug.' It clearly names the listing scope and the identification mechanism, and it differentiates from sibling search tools by focusing on a single record's full detail rather than a list.
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?
The description explicitly says 'Use this after search_cars when the user asks about a specific car,' providing a clear trigger condition and preceding-step context. It does not explicitly enumerate when-not-to-use or name alternatives like find_similar_cars or get_price_stats, but the context is unambiguous enough to guide correct selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get price statistics for a slice of the Portugal used-car market: how many listings match, the median price, the p25–p75 range, the median price per kilometre and a breakdown by body type. Use this when the user asks what something costs, whether an asking price is fair, or how two makes compare — instead of listing individual cars. Takes the same filters as search_cars and describes exactly the set that tool would return, so the two never disagree. Reports medians and quartiles rather than averages, because car prices have a long right tail and a single luxury listing moves an average.
| Name | Required | Description | Default |
|---|---|---|---|
| fuel | No | Fuel type. GASOLINE = gasolina, ELECTRIC = eléctrico, HYBRID = full hybrid, PLUG_IN_HYBRID = PHEV. | |
| make | No | Make slug (e.g. toyota, bmw, volkswagen). Not free text — the slug is the same in every market. | |
| kmMax | No | Maximum kilometres on the odometer. | |
| model | No | Model slug (e.g. corolla, serie-3, golf). Requires `make` to disambiguate. | |
| yearMax | No | Maximum registration year. | |
| yearMin | No | Minimum registration year. | |
| bodyType | No | Body style. SEDAN = berlina, HATCHBACK = citadino, COMBI = carrinha, CABRIO = descapotável, MINIVAN = monovolume. | |
| district | No | District or region inside the market (e.g. Lisboa, Porto, Berlin, Madrid). Matched exactly. | |
| priceMax | No | Maximum price in the market currency. Listings with no published price are excluded by either bound. | |
| priceMin | No | Minimum price in the market currency. | |
| condition | No | Vehicle condition. new = 0 km / registered this year; semi_new = <2 years and <20 000 km; used = everything else. |
| Name | Required | Description |
|---|---|---|
| count | Yes | Listings matching the filters, with or without a published price. |
| price | No | Price distribution, in percentiles. null when no matching listing publishes a price. There is deliberately no min or max: listings come from third-party sources and the extremes are set by data errors, not by the market. |
| market | Yes | |
| priced | Yes | Subset with a published price — the base of every figure below. A large gap to `count` means many listings in this slice are 'price on request'. |
| currency | Yes | |
| byBodyType | Yes | Up to 6 most represented body types, each with its own median. |
| pricePerKmMedian | No | Median price per kilometre on the odometer, in the listing currency (order of magnitude 0.05–3). Small = the car has done a lot for its price. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description does not need to repeat basic safety. It adds meaningful behavioral context beyond the schema: it reports medians and quartiles rather than averages and explains why, and it guarantees consistency with search_cars by describing exactly the same listing set.
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?
The description is four purposeful sentences: what it returns, when to use it, how its filter semantics relate to search_cars, and why it uses medians. Every sentence adds information and there is 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?
Given the output schema exists and the annotations cover safety traits, the description provides everything an agent needs: purpose, output metrics, usage conditions, filter relationship to a sibling, and statistical rationale. No material information is missing.
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%, so the schema already documents all 11 parameters thoroughly. The description adds the useful context that the filters are identical to search_cars, but it does not need to compensate for gaps, so the baseline 3 is appropriate.
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?
The description opens with a specific verb and resource: 'Get price statistics for a slice of the Portugal used-car market' and enumerates the exact statistics returned. It clearly distinguishes itself from search_cars by stating it aggregates rather than lists individual cars.
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?
It explicitly says when to use the tool: when the user asks what something costs, whether a price is fair, or how two makes compare, 'instead of listing individual cars.' It also names the sibling search_cars and clarifies that both tools share filters, giving the agent a clear routing rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search editorial articles about the used-car market in Portugal: buying guides, reviews, comparisons and market analysis. Use this when the user asks how something works — financing, warranty, IUC, IPO/ITV, the buying process — rather than asking for listings.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results (default 5, max 10). | |
| category | No | Article category. |
| Name | Required | Description |
|---|---|---|
| articles | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds scope beyond the annotations by defining the exact corpus searched (editorial articles about the Portuguese used-car market) and the article categories. It does not describe result ordering or defaults, but the output schema and parameter descriptions reduce the need for that detail.
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?
Two sentences front-load the purpose and then give usage guidance. There is no filler or redundancy; every phrase contributes to either describing what the tool returns or when it should be selected.
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?
The definition is adequate for a simple filtered read with annotations and an output schema, but it omits how the search term is determined when the schema has no query parameter. It also does not explain how example topics like financing or IUC map to the category enum, which is important for correct invocation.
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%, so the baseline is 3. The description's topic examples add useful context, but they do not map cleanly to the only parameters (limit and category), and the description never explains how the search query is supplied given that the schema has no query parameter. This leaves the agent guessing about how to actually scope a search.
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?
The description states a specific verb and resource: 'Search editorial articles about the used-car market in Portugal' and lists the content types (buying guides, reviews, comparisons, market analysis). It also differentiates the tool from listing-oriented siblings by saying 'rather than asking for listings,' making the tool's boundary clear.
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?
The description explicitly says when to use the tool: 'Use this when the user asks how something works' and gives concrete examples such as financing, warranty, IUC, IPO/ITV, and the buying process. It also gives a when-not case ('rather than asking for listings'), but it does not name the sibling tool to use instead, so the guidance is clear but not fully explicit about alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search current used-car listings for sale in Portugal. Use this whenever the user wants to find, compare or explore cars — by make, model, budget, mileage, fuel or district. Returns price, mileage, fuel, body and a citable listing URL, plus total: the exact number of listings matching the filters, which makes this tool usable to answer 'how many' questions. Listings are aggregated from stands and portals and refreshed daily; each result carries publishedAt. Prefer the broadest filters that answer the question and report total as a floor, not a census.
| Name | Required | Description | Default |
|---|---|---|---|
| fuel | No | Fuel type. GASOLINE = gasolina, ELECTRIC = eléctrico, HYBRID = full hybrid, PLUG_IN_HYBRID = PHEV. | |
| make | No | Make slug (e.g. toyota, bmw, volkswagen). Not free text — the slug is the same in every market. | |
| page | No | 1-based page number. Use with `total` to walk a large result set. | |
| sort | No | Result order. Default `recent` (most recently seen first). | |
| kmMax | No | Maximum kilometres on the odometer. | |
| limit | No | Results per page (default 10, max 20). | |
| model | No | Model slug (e.g. corolla, serie-3, golf). Requires `make` to disambiguate. | |
| yearMax | No | Maximum registration year. | |
| yearMin | No | Minimum registration year. | |
| bodyType | No | Body style. SEDAN = berlina, HATCHBACK = citadino, COMBI = carrinha, CABRIO = descapotável, MINIVAN = monovolume. | |
| district | No | District or region inside the market (e.g. Lisboa, Porto, Berlin, Madrid). Matched exactly. | |
| priceMax | No | Maximum price in the market currency. Listings with no published price are excluded by either bound. | |
| priceMin | No | Minimum price in the market currency. | |
| condition | No | Vehicle condition. new = 0 km / registered this year; semi_new = <2 years and <20 000 km; used = everything else. |
| Name | Required | Description |
|---|---|---|
| cars | Yes | |
| page | Yes | |
| shown | No | |
| total | Yes | Exact number of listings in this market matching the filters, across all pages. |
| market | Yes | ISO country code of this market. |
| hasMore | Yes | |
| currency | Yes | |
| pageSize | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds useful context beyond the annotations: listings are aggregated from stands and portals, refreshed daily, each result carries `publishedAt`, and `total` has caveats. However, 'report `total` as a floor, not a census' contradicts `openWorldHint=false`, which signals a closed world; the description explicitly encourages the agent to reason as if the result set may be incomplete. Per the rubric, a description that contradicts annotations must score 1.
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?
The description is three compact sentences with no filler. It front-loads the core purpose, then states the return contract (including `total` and `publishedAt`), then gives the key caveats. Every sentence earns its place.
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?
For a 14-parameter read-only tool with an output schema, the description covers return fields, freshness, and `total` semantics well. But it lacks sibling differentiation and contains the open-world contradiction about `total` as a floor, which could mislead an agent selecting or interpreting the tool.
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%, and every parameter already has an explanatory description including enum translations and constraints. The tool description adds no per-parameter meaning beyond the schema, so the 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?
The description names a specific verb and resource — 'Search current used-car listings for sale in Portugal' — and enumerates the main filter dimensions (make, model, budget, mileage, fuel, district). It stops short of a 5 because it doesn't differentiate this tool from the sibling `search_cars_by_description`, so an agent can't tell which search path to prefer for free-text queries.
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?
The description gives explicit when-to-use context ('Use this whenever the user wants to find, compare or explore cars') and adds practical guidance to prefer broad filters and treat `total` as a floor. However, it provides no exclusions and names no alternatives such as `get_car_detail`, `get_price_stats`, or `search_cars_by_description`, so the when-not-to-use side is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Portugal listings by describing the car in plain language, matched against listing text by meaning rather than keywords. Use this for wants that are not filter fields — 'a family SUV good in the snow', 'a low-mileage hybrid for the city', 'a used 4x4 to restore'. Prefer search_cars for entirely structural queries (make/model/budget); use this when the request is descriptive. Combine both by passing structural filters here too — they act as hard constraints while the description only ranks. Results are ordered by relevance, so total is the number matching the structural filters, not the number that fit the description.
| Name | Required | Description | Default |
|---|---|---|---|
| fuel | No | Fuel type. GASOLINE = gasolina, ELECTRIC = eléctrico, HYBRID = full hybrid, PLUG_IN_HYBRID = PHEV. | |
| make | No | Make slug (e.g. toyota, bmw, volkswagen). Not free text — the slug is the same in every market. | |
| kmMax | No | Maximum kilometres on the odometer. | |
| limit | No | Maximum number of results (default 10, max 20). | |
| yearMin | No | Minimum registration year. | |
| bodyType | No | Body style. SEDAN = berlina, HATCHBACK = citadino, COMBI = carrinha, CABRIO = descapotável, MINIVAN = monovolume. | |
| priceMax | No | Maximum price in the market currency. Listings with no published price are excluded by either bound. | |
| description | Yes | What the user is looking for, in their own words. Full phrases work better than keywords. Pass the qualities here and the hard limits in the other arguments. |
| Name | Required | Description |
|---|---|---|
| cars | Yes | Ordered by relevance to the description, most relevant first. |
| shown | Yes | |
| total | Yes | Listings matching the structural filters. The description narrows the ranking, not this count. |
| market | Yes | |
| currency | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the read-only/idempotent annotations, the description discloses important behavior: semantic ranking rather than keyword matching, structural filters acting as hard constraints, and the subtle `total` semantics (matching structural filters, not description relevance). This is exactly the kind of non-obvious behavioral context an agent needs.
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?
Five sentences, all dense with useful information: purpose, examples, sibling routing, combination strategy, and a critical caveat. The description front-loads the core purpose and immediately moves to actionable guidance without redundancy.
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?
For a semantic-search tool with a rich schema and output schema already present, the description covers everything essential: what it searches, when to use it vs the sibling, how filters interact, and how to interpret `total`. No critical gap remains for correct invocation.
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%, so the baseline is 3, but the description adds meaningful guidance for the `description` parameter (full phrases work better than keywords) and clarifies how structural parameters interact with the semantic search. This goes beyond simply repeating schema descriptions.
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?
The description states a specific verb+resource ('Search Portugal listings') and the distinctive method ('matched against listing text by meaning rather than keywords'). It also differentiates this tool from search_cars by naming the alternative explicitly, so an agent can tell them apart immediately.
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?
It gives concrete when-to-use guidance with examples ('a family SUV good in the snow' vs structural queries), explicitly says to prefer search_cars for structural queries, and explains how to combine both tools by passing structural filters as hard constraints. This is clear routing with no inference required.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Budget used cars in Portugal: fair-price check, running-cost (IUC) estimate, stock search, contact.
Used & imported cars in Portugal: search stock, ISV/IUC tax calc, resale value, contact.
Used car catalogue in Portugal. Search by make, model, price, mileage, fuel and district.
Property listings in Portugal: search, market stats, comparables, neighbourhoods.
Add one secure layer between your agents and this server.
Most tools have clear boundaries: search_cars handles structural queries, search_cars_by_description handles descriptive queries, and find_similar_cars finds alternatives to a reference listing. There is some overlap between search_cars and find_similar_cars when finding comparable cars, but the descriptions clarify which to use for each intent.
Names broadly follow a verb_noun pattern: search_cars, get_car_detail, get_price_stats, search_articles. The main inconsistency is mixing 'search' and 'find' verbs, and search_cars_by_description is longer and less symmetric than the others, but the pattern remains readable and predictable.
Six tools is well-scoped for a car search and market-analysis server. Each tool serves a distinct user need from listing search to price statistics to editorial content, with no redundancy or bloat.
The tool surface covers the complete read-only lifecycle of a car-search domain: structural search, descriptive search, similar-car discovery, detail retrieval, market-level price stats, and supporting editorial articles. No obvious dead ends or missing operations within the stated purpose.
