Maskr.io MCP Server

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

No annotations provided, so description carries full burden. It successfully discloses return structure (broken down by wallet vs subscription credits) and format (text summary), adding value beyond the schema. Lacks auth/rate limit details but adequate for simple read operation.

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?

Front-loaded with purpose first. Slight redundancy between 'Returns your current...' and 'Returns: A text summary', but overall efficient three-sentence structure with no extraneous 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?

Appropriate for a zero-parameter read operation. Despite having output schema (per context signals), description adds useful context about the credit breakdown structure thatschema likely doesn't convey.

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?

Zero parameters present. Per guidelines, baseline is 4 for zero-parameter tools. Description correctly implies no inputs needed via 'Check your...' phrasing.

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?

Clear specific verb ('Check') + specific resource ('maskr.io credit balance'). Distinguishes from siblings: get_credit_cost (pricing), get_packages/plans (offerings), and image processing tools.

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?

Clear what the tool does, but lacks explicit when-to-use guidance versus siblings like get_credit_cost. Usage is implied by the specific purpose but no explicit 'when not to use' or alternatives mentioned.

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

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

With no annotations provided, the description carries the full disclosure burden. It clarifies the dual return (credit cost + current balance) and identifies the return format as 'a text summary.' However, it omits operational traits: whether the call consumes credits (likely not), idempotency guarantees, rate limits, or permission requirements. Basic behavioral context is present but safety/operational details are sparse.

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 follows a clear docstring structure (summary, Args, Returns) with the main purpose front-loaded in the first sentence. While the Args section duplicates information already present in the 100%-covered schema, the formatting is efficient and every sentence earns its place by clarifying inputs or outputs. Length is appropriate for a three-parameter tool.

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 high schema coverage (100%), clearly defined enums in the schema (mapping to sibling operations), and existence of an output schema, the description is sufficiently complete. It adds necessary domain context ('maskr.io') and clarifies the return type. It appropriately does not replicate detailed return value documentation since an output schema exists.

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?

Input schema has 100% description coverage, establishing a baseline of 3. The Args section in the description lists the three parameters but essentially mirrors the schema content without adding syntactic nuances (e.g., it repeats 'The service to check pricing for' from the schema and omits the schema's crucial upscaling dimension note: 'provide the INPUT dimensions'). Since schema coverage is complete, the description serves as readable documentation but adds minimal semantic value beyond structured data.

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 'Check how many credits a maskr.io service costs' — a specific verb (check) + resource (credit cost) + domain context (maskr.io). It clearly distinguishes from siblings: unlike 'remove_background' or 'upscale' (which perform processing), this queries pricing; unlike 'get_account_balance' (which only returns balance), this calculates service-specific costs based on dimensions.

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 usage context by mentioning 'image dimensions' and 'service,' hinting it precedes processing calls. However, it lacks explicit guidance such as 'use this before calling remove_background/upscale' or 'use get_account_balance if you only need your balance without cost calculation.' No when-not-to-use or alternative specifications are provided.

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

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

With no annotations provided, the description carries full burden and successfully discloses behavioral scope: filters to 'active' packages only, specifies content includes 'pricing and per-credit cost', and clarifies these are for 'one-time purchase'. Minor gap on auth/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?

Efficiently structured with clear front-loading. Minor deduction for slight redundancy between narrative 'Returns all active...' and the 'Returns:' section stating 'A summary...', though both sentences earn their place by serving different documentation conventions.

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?

Complete for a zero-parameter read tool. Given output schema exists (per context signals), the description appropriately focuses on business logic clarification (active packages, pricing details) rather than return value documentation.

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?

Baseline 4 applies as there are 0 parameters. The description appropriately does not invent parameter semantics where none exist.

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 uses specific verb 'List' with specific resource 'maskr.io credit packages' and distinguishes scope by specifying 'one-time purchase' versus subscription plans, clearly differentiating from sibling tool get_plans.

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?

Provides clear usage context ('help users decide between subscription plans and credit packs') implying when to use this vs. subscription options. Would be a 5 if it explicitly named 'get_plans' as the alternative rather than just referencing 'subscription plans'.

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

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

With no annotations provided, the description carries the full burden of disclosure, successfully detailing the returned data structure including 'monthly pricing, credit allowances, and badges' and the scope ('all active'). It could be improved by mentioning authentication requirements or cache behavior, but adequately covers the core read-only semantics.

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 four-sentence structure is appropriately front-loaded, starting with the core purpose followed by behavioral details and usage guidance. The final 'Returns:' section is slightly redundant given the existence of an output schema, but remains concise and doesn't significantly obscure the description.

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 presence of an output schema (per context signals), the description appropriately augments it with semantic details about specific fields (pricing, allowances, badges) rather than just structural information. The coverage is sufficient for this simple read-only tool with no side effects.

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?

Per the evaluation rules, zero-parameter tools receive a baseline score of 4. The description correctly omits parameter discussion since the input schema is empty, focusing instead on behavioral description without wasting space on non-existent arguments.

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 uses the specific verb 'List' with the resource 'maskr.io subscription plans', clearly defining the tool's scope and domain. It effectively distinguishes from siblings like `remove_background` and `get_account_balance` by specifying this retrieves subscription plan metadata with pricing details.

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 phrase 'Use this to help users choose the right plan for their usage' provides clear context for when to invoke the tool during user onboarding or plan comparisons. However, it lacks explicit guidance on when not to use this versus the sibling `get_packages` tool.

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

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

Without annotations, the description carries the full burden. It successfully discloses the HTTP fetch behavior ('Downloads'), processing method ('AI'), and input constraints ('Must be a publicly accessible image URL... PNG, JPEG, or WebP'). However, it lacks operational details like credit consumption, rate limits, or whether processed images are persistently stored.

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 well-structured with clear 'Args' and 'Returns' sections. The first sentence front-loads the core purpose, followed by behavioral elaboration. The formatting is efficient with no redundant filler, though the 'Returns' section duplicates information likely present in the output schema.

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 presence of an output schema (not shown but indicated), the brief return value summary is sufficient. The single parameter is adequately documented in the description to compensate for the empty schema. For a specialized media processing tool, this covers the essential operational contract.

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 0% schema description coverage, the description fully compensates by defining 'image_url' as the 'URL of the image to process' and adding critical constraints ('publicly accessible', specific formats 'PNG, JPEG, or WebP') not present in the bare schema. It provides sufficient semantic context for the agent to populate the parameter correctly.

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 explicitly states the specific action ('Remove the background') and resource ('from an image'), and the second sentence clarifies the mechanism ('Downloads... removes... using AI'). This clearly distinguishes it from sibling tools like 'upscale' and account management functions.

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?

While the description clearly states the tool's function (background removal), it provides no explicit guidance on when to choose this over the sibling 'upscale' tool or when not to use it (e.g., unsupported formats). Usage must be inferred from the stated purpose alone.

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

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

With no annotations provided, the description carries full disclosure burden. It adequately explains the operational flow (downloads from URL, processes with AI, returns image) and mentions the return structure includes 'a summary message.' However, it omits behavioral details like processing time expectations, temporary storage policies, or error handling for invalid URLs/formats.

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 uses a standard docstring structure with clear Args and Returns sections. Information is front-loaded with the core purpose first, followed by behavioral details and parameter specifics. While slightly more verbose than minimal descriptions (due to Args/Returns headers), the structure is appropriate for the three-parameter complexity and contains no redundant sentences.

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 existence of an output schema, the description appropriately summarizes the return value ('upscaled image plus summary message') without redundant detail. All three parameters are documented, input constraints are specified, and the AI processing method is disclosed. For a single-purpose image processing tool, the description provides sufficient context for 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 67% (image_url lacks schema description; others have descriptions). The Args section compensates effectively: it documents image_url constraints (public accessibility, valid formats) not present in the schema, and adds default values and enum options for scale_factor and output_format that supplement the schema definitions.

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 'Upscale[s] an image to higher resolution' using AI—specific verb, resource, and method. However, it lacks explicit differentiation from the sibling tool 'remove_background' (also an image processing tool), which would help clarify when to select this specific image manipulation function.

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 provides implicit usage context by noting inputs must be 'publicly accessible' URLs and specifying supported formats (PNG, JPEG, WebP). However, it lacks explicit guidance on when to use this versus the sibling 'remove_background' tool, or prerequisites like checking credit costs (relevant given 'get_credit_cost' sibling exists).

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