Free, public calculators and safety-screened DIY guides for carpet cleaning, upholstery cleaning, and limited water-damage cleanup.
Claiming proves that you control this connector and unlocks listing details, thumbnails, health checks, and analytics. It does not change or interrupt the running server.
Complete one method below. Compare the methods and read troubleshooting steps.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.9/5 across 3 of 3 tools scored.
Each tool targets a distinct cleaning scenario: carpet, upholstery, or water damage. There is no overlap in purpose, so an agent can easily select the appropriate tool based on the surface or incident type.
All tool names begin with 'plan_' and follow a predictable pattern. The only minor inconsistency is the suffix: two use 'cleaning' while one uses 'cleanup', which is slightly inconsistent but still clear and readable.
With only three tools, the set is well-scoped for a specialized cleaning planner. Each tool covers a major category, and the count is within the ideal 3-15 range, feeling neither sparse nor bloated.
The tools cover carpet, upholstery, and water damage, but the server name suggests a broader 'Advanced Cleaning' scope. Missing common DIY areas like hard floors or tile create noticeable gaps, though the included scenarios are each handled thoroughly.
plan_carpet_cleaningPlan DIY carpet cleaningAEstimate conservative labor, elapsed time, generic chemistry, extraction water, drying time, equipment and steps for a DIY carpet cleaning project. Uses product-label dilution and includes fiber safety warnings.
| Name | Required | Description | Default |
|---|---|---|---|
| fiber | No | synthetic | |
| stairs | No | Number of individual stair treads | |
| workers | No | ||
| areaSqFt | Yes | Measured carpet area in square feet | |
| petSpots | No | ||
| furniture | No | light | |
| soilLevel | No | moderate | |
| equipmentType | No | rental | |
| petSpotAreaSqFt | No | ||
| coverageSqFtPerGal | No | ||
| labelDilutionOzPerGal | No | Product-label concentrate ounces per ready-to-use gallon |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive. Description adds behavioral context: estimates are 'conservative', uses product-label dilution, and includes fiber safety warnings, which informs the user about calculation assumptions and output nature.
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 clear sentences, front-loaded with the core purpose, no wasted words.
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 11 parameters and no output schema, the description covers the main output dimensions and key assumptions, though it could be more explicit about the returned structure and edge cases. Still, annotations and schema defaults fill many gaps.
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 only 27%, and the description partially compensates by referencing product-label dilution and fiber safety, which relate to labelDilutionOzPerGal and fiber. However, most parameters (stairs, petSpots, furniture, soilLevel, etc.) are not individually explained, leaving some ambiguity.
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?
Provides a specific action (estimate) and resource (DIY carpet cleaning project), listing the output dimensions (labor, time, chemistry, etc.), and distinguishes itself from siblings by focusing on carpet cleaning with safety warnings.
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 context is implied from the name and description, but there is no explicit guidance on when to choose this over plan_upholstery_cleaning or plan_water_damage_cleanup, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
plan_upholstery_cleaningPlan DIY upholstery cleaningAUse furniture, manufacturer care code, material and soil level to screen wet-cleaning safety and estimate conservative labor, chemistry, equipment, drying and steps.
| Name | Required | Description | Default |
|---|---|---|---|
| workers | No | ||
| careCode | Yes | Manufacturer cleaning code; use UNKNOWN when no reliable tag exists | |
| itemType | Yes | ||
| material | Yes | ||
| petSpots | No | ||
| itemCount | Yes | ||
| soilLevel | No | moderate | |
| stainType | No | none | |
| hasDryVacuum | No | ||
| hasExtractor | No | ||
| looseCushions | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey that the tool is read-only, idempotent, and non-destructive. The description adds behavioral context by noting that it screens for wet-cleaning safety and provides conservative estimates. This clarifies that the tool is for planning and safety assessment rather than execution, even though it doesn't detail how unsafe combinations are handled.
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 a single, front-loaded sentence that packs key information without unnecessary words. It lists both inputs and output categories efficiently. While a slightly more structured breakdown could improve readability, the description remains concise and to the point.
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 no output schema, the description conveys the general plan structure (safety assessment plus estimates of labor, chemistry, equipment, drying, and steps), but it lacks specifics on how parameters influence results, how to handle unknown codes or materials, or the output format. Given the tool's complexity, the description provides an adequate but not fully complete picture.
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 only 9%, placing the burden on the description to explain parameters. The description mentions four inputs (furniture, care code, material, soil level) but does not add meaningful semantics for the other seven parameters (workers, petSpots, stainType, hasDryVacuum, hasExtractor, looseCushions, itemCount). The mapping helps but is insufficient for a tool with 11 parameters.
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 clearly identifies the tool's function as screening wet-cleaning safety and estimating labor, chemistry, equipment, drying, and steps for upholstery cleaning. The specific verbs 'screen' and 'estimate' combined with the resource 'upholstery' distinguish it from other planning tools like carpet cleaning or water damage cleanup.
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 implies the appropriate use case: planning DIY upholstery cleaning when furniture type, manufacturer care code, material, and soil level are known. It does not explicitly exclude carpet or water damage scenarios, but the focus on upholstery makes the context clear. No alternative tools are mentioned, though the resource specificity provides sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
plan_water_damage_cleanupTriage limited DIY water damage cleanupAScreen a water incident for mandatory professional-only stop conditions. Only eligible limited cases receive water-volume, labor, equipment, chemistry and procedural DIY guidance.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | ||
| gasRisk | No | ||
| workers | No | ||
| ageHours | Yes | ||
| areaSqFt | Yes | ||
| materials | Yes | ||
| depthInches | Yes | ||
| hasWetVacuum | No | ||
| hvacAffected | No | ||
| moldAreaSqFt | No | ||
| roomsAffected | No | ||
| sourceStopped | Yes | ||
| electricalRisk | No | ||
| floorsAffected | No | ||
| structuralRisk | No | ||
| tankCapacityGal | No | ||
| hasMoistureMeter | No | ||
| availableAirMovers | No | ||
| vulnerableOccupants | No | ||
| availableDehumidifiers | No |
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 useful context: it actively screens out cases with mandatory professional-only stop conditions and only returns guidance for eligible limited incidents. However, it does not describe the specific stop conditions or the structure of the returned guidance, so behavioral detail is modest.
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 two sentences, with the core screening purpose front-loaded. Every clause adds value—first the screening action, then the conditional guidance output. No fluff or repetition.
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 20 parameters, zero schema descriptions, and no output schema, the description provides a solid high-level contract: triage and DIY guidance for eligible cases. It lists the output categories and clarifies the eligibility limitation, but it omits decision thresholds, return format, and any explanation of what happens when a stop condition is met.
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 0%, and the description does not explain any of the 20 input parameters. It mentions output categories like water-volume, labor, equipment, chemistry, and procedural guidance, but gives no guidance on how to set source, ageHours, areaSqFt, materials, or any other parameter. This leaves the agent without parameter-level meaning.
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 clearly states the tool's specific function: screening a water incident for mandatory professional-only stop conditions and providing DIY guidance for eligible limited cases. It distinguishes itself from sibling tools by focusing on water damage cleanup rather than carpet or upholstery cleaning.
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 implies when to use the tool—for water incidents that need triage to decide between professional-only and DIY cleanup. It clearly scopes to 'limited DIY' cases but does not explicitly name alternatives or state when not to use it, so it lacks explicit exclusions.
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.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, bound to the signed-in Glama account, and expire after seven days. They contain no email address or other personal information. 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.
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
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
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Centralized credential management – store and rotate API keys and OAuth tokens in one place
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
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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.
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