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jaweii

EraseText MCP Server

by jaweii
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    get_account and erase_text have completely distinct purposes: one for account/credit status, one for image text removal. No overlap or ambiguity.

    Naming Consistency5/5

    Both tool names use the same verb_noun snake_case pattern (get_account, erase_text), making the API predictable.

    Tool Count3/5

    With only two tools, the server feels minimal, though it covers the essential operations of the EraseText API. It sits at the thin end of the acceptable range.

    Completeness4/5

    The server provides the core account check and erase operations. Minor gaps such as history or preview functionality are absent, but no critical workflow is blocked.

  • Average 4.8/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    The description discloses a critical behavioral trait: it never spends a credit. It also makes clear that it returns data, implying a read-only operation. While it does not discuss authentication, errors, or rate limits, the absence of annotations makes this a reasonable level of transparency for the described functionality.

    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 two short sentences that directly state the purpose and the key side-effect. There is no redundant or extraneous content, making it appropriately concise and well-structured.

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

    Completeness5/5

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

    The description provides sufficient context for a simple, parameterless tool. It explains what data is returned and confirms that no credit is consumed. Given the simplicity and lack of output schema, nothing important is missing.

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

    Parameters5/5

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

    The tool has no parameters, so the schema is empty. The description does not need to explain any parameters, and the coverage is technically 100%. The description adds no unnecessary parameter-related information, which is appropriate.

    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 returns API and web credit balances, plan, and usage for the current UTC month. It also explicitly notes it never spends a credit, which distinguishes its read-only nature. This is specific and unambiguous, and it differentiates from the unrelated sibling erase_text.

    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 implicitly indicates when to use the tool (when needing balance, plan, or usage information) and explicitly states that it does not spend credits, which is a key when-not. However, it does not mention any alternative tools or provide explicit comparison to siblings, so it falls short of full explicit guidance.

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

  • Behavior5/5

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

    The description reveals important behavioral details: failures (including HTTP 402/504) are not charged, the mask's white areas indicate erase regions, and the paste_back flag controls pixel retention outside the erased area. This goes beyond a simple action statement and sets clear expectations for the tool's runtime behavior.

    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 efficient and information-dense. Every sentence provides value—purpose, mask semantics, billing, and input modes—without redundancy or unnecessary filler, making it appropriately sized for the complexity of the tool.

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

    Completeness5/5

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

    Given the tool's complexity (8 parameters, multiple options, and billing nuances), the description covers all necessary aspects: how masking works, input formats, resolution, output options, and cost implications. No external documentation is needed for a basic understanding, and the absence of an output schema is mitigated by the clear parameter explanations.

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

    Parameters5/5

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

    All 8 parameters have meaningful descriptions in both the schema and the tool description. The description adds contextual guidance (e.g., 'Use when you have bytes, not a URL' for image_file_b64, and 'Ignore if a mask is sent' for return_boxes), which enhances understanding beyond the schema.

    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: 'Erase lettering from a photo or graphic and rebuild the background.' It also explains the optional mask behavior and how to use base64 versus URL inputs, making it distinct from the sibling account tool.

    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 explicitly explains when to use each input mode (URL vs. base64), how the mask works, the effect of paste_back, and the return_boxes option. It also covers billing behavior (only charged on success) and provides links for key creation and credit purchase, leaving no ambiguity about usage.

    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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