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woladi

pseudonym-mcp

by woladi
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TypeScript
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Server Quality Checklist

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

  • Latest release: v0.2.5

  • Disambiguation5/5

    The two tools have clearly distinct and complementary purposes: mask_text obscures sensitive data, unmask_text restores it. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (mask_text, unmask_text) with a clear semantic relationship, making them predictable and easy to understand.

    Tool Count4/5

    With only 2 tools, the server feels minimal but complete for its focused purpose of pseudonymization. The count is slightly low but still appropriate for a narrow utility.

    Completeness5/5

    The pair of tools covers the full lifecycle of pseudonymization: masking and unmasking. The masking tool handles a broad set of entity types (PESEL, phone, IBAN, email, person names, org names), leaving no obvious gaps.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

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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

  • Behavior3/5

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

    No annotations are provided, so the description bears the full burden. It explains the token replacement and the use of Ollama for NER, but does not disclose potential failures, performance implications, or the requirement for network access to Ollama.

    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?

    Three sentences with clear structure: purpose, mechanism, and return+follow-up. No redundancy or filler.

    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 4 params and no output schema, the description covers core functionality and workflow. However, it omits details about return format and error handling for NER failures.

    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%, baseline 3. The description adds value beyond schema by explaining session_id for token numbering persistence, wait_for_ner for model loading, and custom_literals for always-redacted strings.

    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 pseudonymizes sensitive entities in text, specifies the exact entities via regex and NER, and contrasts with the sibling tool unmask_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 explicitly states the use case: before sending to a cloud LLM. It advises storing session_id for later restoration, but does not include explicit when-not-to-use or alternative scenarios.

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

  • Behavior3/5

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

    With no annotations, the description carries full burden. It explains token replacement and session requirement, but does not specify return value, error conditions (e.g., invalid session), or idempotency. Adequate but not comprehensive.

    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 concise sentences with clear purpose first, then additional details. No wasted words.

    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?

    Sufficient for a simple restore tool with two parameters. Output is implied (restored text) but not explicitly stated. No mention of failure modes, but given low complexity, this is acceptable.

    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 covers both parameters (100% coverage). Description adds value by specifying token format like [PESEL:1] and that session_id must come from mask_text. Adds context beyond 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 it restores original sensitive values in text that was previously masked by mask_text, using specific verb 'restore' and resource 'text'. It distinguishes from sibling mask_text by describing the reverse operation.

    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?

    Explicitly says to use after mask_text and that session_id must come from mask_text. This gives clear context for when to use. However, no explicit when-not or alternatives beyond the sibling.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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