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atla-ai

Atla

Official
by atla-ai
OverviewSchemaRelated ServersScoreDiscussions
Python
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: one evaluates a single criterion, the other evaluates multiple criteria. Their names and descriptions make the difference unambiguous.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun_qualifier pattern, starting with 'evaluate_llm_response' and differentiating with '_on_multiple_criteria'. No mixing of conventions.

    Tool Count3/5

    With only 2 tools, the server feels thin. While the tools cover the core evaluation functionality, a typical well-scoped server has 3-15 tools, making this borderline insufficient.

    Completeness3/5

    The tools provide basic evaluation for single and multiple criteria, but lack supporting tools such as managing criteria, listing models, or retrieving history. The surface is minimal and may leave agents with limited options.

  • Average 3.9/5 across 2 of 2 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • 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?

    With no annotations, the description adds some behavioral context (uses Atla model under the hood, returns list of dicts) but does not disclose side effects, cost, failure modes, or restrictions. It adds value beyond annotations but falls short of full transparency.

    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 concise (two sentences plus a return format note) and front-loaded with purpose. Every sentence adds value; no obvious fluff. Minor improvement possible by incorporating usage guidance.

    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 complexity (6 parameters, output schema exists), the description provides basic purpose and return format but lacks usage guidelines, behavioral warnings, and parameter elaboration. It is incomplete for fully understanding when and how to use the tool 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 high (83%), and the description does not add new meaning to individual parameters beyond the schema. It explains the return format, which aids understanding of the output but does not directly enhance parameter semantics.

    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 that the tool evaluates an LLM response across multiple criteria using an Atla model and returns a list of dictionaries with score and critique. It distinguishes from the sibling tool 'evaluate_llm_response' by emphasizing 'multiple evaluation criteria', making the purpose and differentiation explicit.

    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 implies usage for multiple criteria but does not explicitly state when to use this tool versus the single-criterion sibling. No when-not-to-use or alternative suggestions are provided, leaving guidance implicit.

    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?

    The description discloses that the tool uses an Atla evaluation model under the hood and returns a dictionary. However, with no annotations provided, the description carries full burden for behavioral traits. It does not mention potential rate limits, cost implications, or authentication requirements. The description is adequate but lacks deeper operational transparency.

    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 concise with three sentences covering purpose, internal mechanism, and output format. It is front-loaded and well-structured. A minor deduction because the return type is specified in a separate block rather than integrated into the prose.

    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 the complexity of 6 parameters (3 required) and the presence of an output schema (described in the return type), the description provides a complete overview. The parameter-level descriptions are very detailed, covering formatting and examples. The sibling tool is referenced, ensuring completeness around alternatives. Slightly lacking in explaining edge cases or error handling.

    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 coverage is 100%, so the input schema already documents all parameters thoroughly. The description does not add new meaning beyond what the schema provides; it mainly reiterates the return format. Baseline score of 3 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's purpose: evaluating an LLM response using given criteria. It specifies that it uses an Atla evaluation model internally and returns a dictionary with score and critique. It also distinguishes from the sibling tool by referencing evaluate_llm_response_on_multiple_criteria in the evaluation_criteria parameter description.

    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 evaluation_criteria parameter description explicitly instructs to use the sibling tool for multiple criteria, providing clear guidance on when to use this tool versus the alternative. Additionally, the parameter descriptions include examples and detailed instructions on crafting evaluation criteria, which serves as usage guidance.

    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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