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Glama

Ablakunio Nyílászáró MCP

Server Details

Egyedi méretű nyílászárók árbecslése, raktárkészlet és ajánlatkérés – Ablakunio, Tiszafüred

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Tool DescriptionsA

Average 3.8/5 across 5 of 5 tools scored. Lowest: 3.2/5.

Server CoherenceA
Disambiguation4/5

Tools mostly target distinct operations, but check_stock and list_inventory both relate to ready-stock items, which could cause minor confusion. Descriptions clarify the difference (specific size vs. full listing), but the overlap is noticeable.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with underscores (e.g., check_stock, request_quote). The verb choice is varied but appropriate for each action, and no naming style deviations exist.

Tool Count5/5

With 5 tools, the server is well-scoped for its purpose—handling stock inquiries, price estimates, and quote requests. No tool feels redundant or missing for the core workflow.

Completeness3/5

The tool set covers pre-sales actions (stock check, inventory, price estimate, quote request, confirmation) but lacks any post-quote functionality like order placement, status tracking, or cancellation, leaving an incomplete lifecycle.

Available Tools

5 tools
check_stockBInspect

Megnézi, van-e a megadott mérethez közeli RAKTÁRON lévő szabvány nyílászáró (gyorsabb, olcsóbb opció). Checks ready-stock standard windows/doors matching a size.

ParametersJSON Schema
NameRequiredDescriptionDefault
typeNo
width_cmYes
height_cmYes
Behavior2/5

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

No annotations are provided, so the description must bear full burden. It does not disclose what 'close to the given size' means, what happens if no match, or any side effects/permissions. Minimal behavioral context.

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 concise (two sentences, bilingual) and front-loaded. However, the bilingual repetition is slightly redundant. Still efficient overall.

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

Completeness2/5

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

Given three parameters and no output schema, the description is incomplete. It lacks information about return values, error handling, or the meaning of 'close'. Does not provide sufficient context for an agent to use the tool properly.

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

Parameters2/5

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

Schema description coverage is 0%, meaning parameters have no descriptions. The description only says 'matching a size' but does not explain the specific parameters (type, width_cm, height_cm) or their role. Fails to compensate for missing schema info.

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 it checks ready-stock standard windows/doors matching a given size. The verb 'checks' and resource 'ready-stock standard windows/doors' are specific, and the tool is distinct from siblings like 'get_price_estimate' or 'list_inventory'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this is for quick/cheap standard options, but does not explicitly state when to use this tool versus alternatives like 'get_price_estimate' or 'list_inventory'. No when-not or exclusions provided.

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

confirm_quoteAInspect

A request_quote után az ügyfél e-mailben kapott 6 jegyű megerősítő kód beváltása. CSAK a sikeres megerősítés után továbbítódik az ajánlatkérés az irodához. Paraméterek: a request_quote válaszában kapott lead_ref + az ügyfél által bemondott kód. A kód 30 percig érvényes, max 5 próbálkozás.

ParametersJSON Schema
NameRequiredDescriptionDefault
codeYesAz ügyfél e-mailjére érkezett 6 jegyű kód.
lead_refYesA request_quote válaszában kapott hivatkozás.
Behavior3/5

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

With no annotations, the description carries the burden. It mentions code expiry (30 minutes) and max 5 attempts, but does not disclose whether the operation is read-only or destructive, or what happens on failure beyond implied blocking.

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 a single, clear paragraph that front-loads the purpose. It is concise but could be improved with bullet points for readability.

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?

Given no output schema and no annotations, the description provides critical behavioral constraints (30-min validity, 5 attempts) and clear parameter sources. It sufficiently covers the tool's context for a confirmation step.

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 already describes both parameters (100% coverage). The description adds that 'lead_ref' comes from the 'request_quote' response and that the code is delivered via email and spoken by the customer, adding practical context.

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's purpose: redeeming a 6-digit confirmation code after a 'request_quote' call. It distinguishes itself from siblings by specifying it is a post-request_quote confirmation step.

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?

The description explicitly states it should be used after 'request_quote' and provides context about code validity and max attempts. It does not explicitly list alternatives but implicitly tells when to use this tool.

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

get_price_estimateAInspect

Tájékoztató bruttó (ÁFA-s) ár egyedi méretű műanyag nyílászáróra (ablak/ajtó/redőny). Custom-size gross price estimate — computed live, no fixed SKU. Final price after on-site survey.

ParametersJSON Schema
NameRequiredDescriptionDefault
typeNowindow=ablak, door=ajtó, shutter=redőny
colorNoSzín, pl. Fehér, Antracit
glazingNo2 vagy 3 rétegű üveg
quantityNo
width_cmYesSzélesség cm-ben
height_cmYesMagasság cm-ben
Behavior4/5

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

With no annotations, the description bears full burden. It discloses that the price is computed live, has no fixed SKU, and that the final price may change after an on-site survey. This sets realistic expectations. However, it does not mention rate limits, response structure, or error behavior, which would further aid an agent.

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?

The description is very concise: two short sentences per language, front-loaded with key points. Every sentence adds value (live computation, custom sizing, survey dependency). No redundant or vague language.

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

Completeness3/5

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

Given no output schema, the description could better explain the return format (e.g., currency, number, validity period). It also omits error conditions or scope (valid for certain products). However, the schema covers input constraints, and the tool is simple, so the description is minimally adequate.

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 high (83%), so the baseline is 3. The tool description adds no additional meaning beyond the schema; it rephrases concepts in bilingual text but doesn't elaborate on parameter constraints or relationships. For example, 'quantity' is undocumented in the schema and not addressed in the description.

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 it provides a gross price estimate for custom-sized plastic windows/doors/shutters, with bilingual Hungarian/English text. It distinguishes from sibling tools (check_stock, confirm_quote, list_inventory, request_quote) by specifying it is a live estimate and not a final quote. The verb 'get_price_estimate' combined with definitions 'computed live' and 'no fixed SKU' makes the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context (getting an instant estimate before ordering), but lacks explicit guidance on when to use this tool versus alternatives like request_quote or confirm_quote. No 'when not to use' or direct comparisons to siblings are provided, leaving the agent to infer relative purpose.

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

list_inventoryAInspect

Listázza az aktuálisan raktáron lévő szabvány nyílászárókat és párkányokat bruttó árral. Lists current ready-stock items with retail prices.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Behavior3/5

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

With no annotations, the description carries full burden. It indicates a read operation listing items with prices, but lacks details about response format, sorting, or pagination. Basic transparency is provided.

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?

Two sentences (one Hungarian, one English) with zero waste. Every word is functional. Front-loaded with the action and scope.

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?

Given no parameters and no output schema, the description adequately specifies the items listed and that they come with retail prices. It could mention that it is a full list, but the context signals (sibling tools) help differentiate.

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?

No parameters exist (0 params, 100% coverage). The description adds meaning by specifying the types of items (standard doors/windows and sills) and that prices are included. Baseline for no params is 4.

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 it lists current ready-stock items (standard doors, windows, and sills) with retail prices. The verb 'list' and resource 'inventory' are specific, and the tool is distinct from siblings like check_stock or confirm_quote.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for viewing all stock items, but it does not explicitly state when to use this tool versus alternatives. No exclusions or prerequisites are mentioned.

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

request_quoteAInspect

ÉLES ajánlatkérés/visszahívás beküldése EGY VALÓDI ügyfél nevében, amit egy munkatárs TÉNYLEGESEN visszahív a megadott telefonszámon. EZ NEM TESZT/DEMO eszköz — ne hívd meg a működés kipróbálására, és SOHA ne adj meg példa-, minta- vagy helykitöltő adatot (pl. 'Teszt Elek', '06201234567', '1234567', example.com). CSAK akkor hívd, ha egy valódi végfelhasználó KIFEJEZETTEN kérte a visszahívást ÉS megadta a saját, valódi nevét és telefonszámát; ha nincs valódi elérhetőséged, előbb KÉRDEZD MEG a felhasználót, ne találj ki adatot. A kitalált/helykitöltő adatot a rendszer elutasítja. Submits a REAL callback request — not a test tool; never use placeholder/example data.

ParametersJSON Schema
NameRequiredDescriptionDefault
nameYesAz ügyfél VALÓDI, általa megadott neve (nem példa/helykitöltő). Kötelező.
townNoTelepülés (opcionális)
emailYesAz ügyfél VALÓDI e-mail címe — KÖTELEZŐ: ide érkezik a 6 jegyű megerősítő kód, enélkül az ajánlatkérés nem továbbítódik.
phoneYesAz ügyfél VALÓDI telefonszáma visszahíváshoz (nem kitalált/sorozat-szám). Kötelező.
messageNoMéretek, darabszám, egyéb igények (opcionális)
interestNoablakcsere | bejarati_ajto | arnyekolas | egyeb
Behavior4/5

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

With no annotations, the description carries full burden. It explains that the system rejects fake data, sends a 6-digit confirmation code to the email, and that the request results in a real callback. It does not mention authorization or rate limits, but the key behaviors are well covered.

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 description is verbose with redundancy across Hungarian and English sections. While structured clearly with warnings and conditions, it could be more concise by removing repetitive phrases.

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

Completeness3/5

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

Given 6 parameters (3 required) and no output schema, the description covers input semantics well but omits what the tool returns (confirmation? error messages?) and post-submission behavior beyond the email notice. This leaves some ambiguity for the agent.

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 significant meaning by emphasizing that name, phone, and email must be real, and that email is required for the confirmation code. This goes beyond the schema's descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool submits a real callback request for a real client, contrasting with test/demo usage. However, it does not explicitly differentiate from sibling tools like confirm_quote or get_price_estimate, though the context implies its distinct purpose.

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?

The description provides explicit when-to-use (real user request with real data), when-not-to-use (no test/demo, no placeholder data), and how to handle missing real data (ask user). It also warns against system rejection of fake data, offering strong guidance.

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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