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acashmoney

BioMCP

by acashmoney
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one analyzes protein structures (active sites), while the other searches for disease-related proteins. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the task.

    Naming Consistency4/5

    Both tools use a consistent verb-noun pattern with hyphens (e.g., analyze-active-site, search-disease-proteins). This naming scheme is readable and predictable, though with only two tools, it's hard to assess full consistency across a larger set.

    Tool Count2/5

    With only two tools, the server feels thin for a bioinformatics domain, which typically involves more operations like sequence alignment, structure prediction, or data retrieval. This limited set may not cover common workflows adequately.

    Completeness2/5

    The tool surface is severely incomplete for a bioinformatics server. It lacks basic operations such as fetching protein sequences, aligning sequences, predicting structures, or managing datasets. This will likely cause agent failures in handling typical tasks in this domain.

  • Average 2.9/5 across 2 of 2 tools scored.

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

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

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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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden but only states the action without disclosing behavioral traits. It doesn't mention computational requirements, output format, error conditions, or whether the analysis is read-only or has side effects, leaving significant gaps.

    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 a single, efficient sentence with zero wasted words. It's appropriately sized for a simple tool and front-loaded with the core purpose, making it easy to parse.

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

    Completeness2/5

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

    Given no annotations and no output schema, the description is incomplete for a tool that performs analysis. It doesn't explain what the analysis entails, what results to expect, or any behavioral context, which is inadequate for guiding an AI agent effectively.

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

    Parameters3/5

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

    Schema description coverage is 100%, so the schema already documents the single parameter 'pdbId'. The description adds no additional meaning beyond what the schema provides, such as examples of analysis outputs or constraints on PDB IDs, meeting the baseline for high coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('analyze') and target resource ('active site of a protein structure'), making the purpose understandable. However, it doesn't differentiate from the sibling tool 'search-disease-proteins' or specify what aspects of the active site are analyzed, preventing a perfect score.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided on when to use this tool versus the sibling 'search-disease-proteins' or other alternatives. The description implies usage for protein structure analysis but lacks explicit context, prerequisites, or exclusions.

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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what the tool does ('Search for proteins') without describing how it behaves—no information about response format, pagination, error handling, rate limits, authentication requirements, or whether it's a read-only or mutating operation. This leaves significant gaps in understanding the tool's 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 extremely concise—a single sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core functionality, making it efficient and easy to parse, though this conciseness comes at the cost of completeness in other dimensions.

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

    Completeness2/5

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

    Given the lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what the search returns (e.g., protein names, IDs, relevance scores), how results are structured, or any behavioral aspects. For a search tool with no structured output documentation, the description should provide more context about the expected results and operational characteristics.

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

    Parameters3/5

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

    The schema description coverage is 100%, with the single parameter 'disease' fully documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema (e.g., it doesn't elaborate on search algorithms, result filtering, or disease name formatting). This meets the baseline expectation when schema coverage is complete.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose with a specific verb ('Search') and resource ('proteins related to a disease'), making it immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'analyze-active-site', which appears to be a different type of analysis tool rather than a direct alternative for searching.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'analyze-active-site' or any other potential tools, nor does it specify prerequisites, constraints, or typical use cases beyond the basic purpose.

    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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  • Evaluate tool definition quality.

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