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davidhariri

Artificial Analysis MCP Server

by davidhariri
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JavaScript
Remote

Server Quality Checklist

67%
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: 'get_model' retrieves detailed information about a specific model, while 'list_models' provides a filtered and sortable overview of all available models. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on whether it needs detailed data on one model or a broader list.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern ('get_model' and 'list_models'), using simple, descriptive verbs that clearly indicate the action. The naming is uniform and predictable, with no deviations in style or convention.

    Tool Count2/5

    With only two tools, the server feels under-scoped for its apparent domain of LLM model analysis. While the tools cover basic retrieval and listing, a more comprehensive server might include operations like comparing models, updating model data, or managing user preferences, making the current set feel thin and potentially limiting for agent workflows.

    Completeness2/5

    The tool set is severely incomplete for a server focused on LLM model analysis. It lacks essential operations such as comparing models, filtering by specific benchmarks, updating or adding model information, and handling user-specific queries or alerts. This creates significant gaps that could lead to agent failures when trying to perform common analytical tasks.

  • Average 3.7/5 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.

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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 lacks important behavioral details. It doesn't mention whether this is a read-only operation, what authentication might be required, rate limits, pagination behavior (beyond the 'limit' parameter), or what the response format looks like. The description only covers basic functionality without behavioral transparency.

    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 perfectly concise - a single sentence that efficiently communicates the core purpose, key capabilities, and available filters/sorting options. Every word earns its place with zero wasted text.

    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 with 4 parameters. It doesn't explain what the return values look like (structure, fields, data types), error conditions, or important behavioral aspects like whether this makes external API calls. The description covers basic functionality but leaves critical contextual gaps.

    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 fully documents all parameters. The description mentions filtering by creator and sorting options, which aligns with the schema but doesn't add meaningful semantic context beyond what's already in the parameter descriptions. This meets the baseline for high schema coverage.

    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 verb ('List') and resource ('all available LLM models from Artificial Analysis') with specific attributes included (pricing, speed, benchmark data). It distinguishes from the sibling 'get_model' by emphasizing comprehensive listing rather than retrieving a specific model.

    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 provides clear context for when to use this tool (to get a filtered/sorted list of models with pricing and benchmark data). It doesn't explicitly state when NOT to use it or name alternatives, but the sibling tool 'get_model' is implied as an alternative for single-model retrieval.

    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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what information is returned but does not mention whether this is a read-only operation, potential rate limits, authentication needs, error conditions, or data freshness. For a tool with no annotations, this leaves significant behavioral 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 that front-loads the purpose and lists specific details without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function.

    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 tool's low complexity (1 parameter, no output schema, no annotations), the description is complete enough for basic understanding. However, it lacks details on behavioral aspects like error handling or data sources, which would be helpful for an agent to use it effectively in varied contexts.

    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 parameter 'model' well-documented in the schema. The description does not add any additional meaning beyond what the schema provides, such as format examples or constraints, but the schema already covers this adequately, resulting in a baseline score of 3.

    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 verb 'Get' and the resource 'detailed information about a specific LLM model', with specific examples of the information returned (pricing, speed metrics, benchmark scores). It distinguishes from the sibling 'list_models' by focusing on details for a single model rather than listing multiple models.

    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 implies usage context by specifying 'a specific LLM model', suggesting this tool is for detailed lookup rather than browsing. However, it does not explicitly state when to use this versus 'list_models' or provide any exclusions or prerequisites for 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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  • Evaluate tool definition quality.

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