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pansapiens

uniprot-unipressed-mcp

by pansapiens
OverviewSchemaRelated ServersScoreDiscussions
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

    Each tool has a clearly distinct purpose: uniprot_fetch retrieves specific entries by IDs, while uniprot_search performs complex queries. There is no overlap in functionality.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with snake_case (uniprot_fetch, uniprot_search), making them predictable and easy to understand.

    Tool Count3/5

    With only 2 tools, the server is very thin for the vast UniProt domain. While fetch and search are core operations, the lack of other tools (e.g., for updates or batch operations) makes it feel limited.

    Completeness3/5

    The server covers basic retrieval operations (fetch by ID and search) but lacks any write or update functionality. For a read-only interface this might be acceptable, but tools for batch processing or data submission are missing.

  • Average 4/5 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
    • 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior2/5

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

    No annotations are provided, and the description lacks disclosure of behavioral traits such as rate limits, authentication requirements, or error handling for missing IDs. It only describes the return format but not edge cases or limitations.

    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 well-structured with clear sections (Args, Returns), but it is overly verbose due to the exhaustive list of fields. This could be shortened or referenced externally for better conciseness.

    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 the absence of annotations and an output schema, the description provides adequate information about parameters and return format. However, it lacks details on error handling, performance considerations, or rate limits, leaving some gaps for a complete understanding.

    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?

    Schema coverage is 0%, so the description fully compensates by providing detailed parameter explanations, including examples, defaults, allowed values (enum for database and response_format), and an extensive list of available fields with categories.

    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 states 'Fetch specific protein entries by their UniProt accession IDs,' which is a specific verb+resource combination. It clearly differentiates from the sibling tool 'uniprot_search' by focusing on fetching by ID rather than searching.

    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 does not explicitly specify when to use this tool versus alternatives. While the sibling name implies search vs. fetch, there is no guidance on when to choose one over the other or any conditions or prerequisites.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries full burden. It explains that the tool queries UniProt databases, supports pagination via cursor, and describes response formats. It does not mention destructive behavior or rate limits, but for a search tool this is acceptable and transparent.

    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 lengthy but well-structured with sections for query syntax, parameters, and return values. It is front-loaded with the basic purpose. While not concise, the structure makes it easy to navigate, and all information is relevant given the tool's complexity.

    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 (multiple databases, extensive query fields, pagination, response format), the description is complete. It covers all parameters, explains output schema with results, total, and nextCursor, and provides links to external documentation.

    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 schema has 0% description coverage, but the description compensates fully by detailing each parameter: query with numerous examples, database with allowed values, limit with range, fields with a categorized list, cursor for pagination, and response_format with enum values and descriptions. This adds significant meaning 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 it searches the UniProt protein database using query syntax, with a specific verb and resource. The sibling tool name 'uniprot_fetch' implies a fetch operation, so the search purpose is distinct and clear.

    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?

    While the description provides extensive query examples and lists available fields, it does not explicitly state when to use this tool versus the sibling 'uniprot_fetch' tool. There is no guidance on when not to use it, leaving the agent to infer usage context from examples.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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