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jlbjulio

tavily-research

by jlbjulio
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clear, distinct role: direct search for focused queries, asynchronous deep research for complex questions, and status retrieval for monitoring that research. The descriptions explicitly delineate when to use each, leaving no ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent 'tavily_<action>' pattern with snake_case. The naming is predictable and makes the purpose of each tool immediately clear.

    Tool Count5/5

    Three tools is exactly the right scope for this server: one for quick searches, one for initiating deep research, and one for retrieving results. Each tool earns its place and the set is not bloated.

    Completeness5/5

    The server covers the full intended workflow: direct search, starting a research task, and checking its status/result. There are no dead ends, and the async nature of the research task is properly handled with the status tool.

  • Average 4.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 2 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

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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?

    The annotations already establish that this tool is read-only, open-world, idempotent, and non-destructive. The description adds 'current information' and 'source discovery' as useful context, but it does not disclose additional behavioral nuances such as result variability, rate limits, or response format.

    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 two short sentences with no redundant phrasing. It front-loads the core action and immediately provides practical use-case guidance.

    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?

    For a straightforward read-only search tool with a fully documented schema, the description covers the essential purpose and use cases. It omits explicit return-format details and sibling routing guidance, but those are minor gaps given the tool's simplicity.

    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 input schema has 100% description coverage, documenting all 8 parameters with descriptions, defaults, and enums. The tool description itself adds no parameter-level meaning, so the baseline score of 3 is appropriate.

    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 names a specific verb ('Search'), a resource ('the web'), and expected outputs ('current information and relevant sources'). It lists concrete use cases like fact-checking and news, which hint that this tool is lighter-weight than the tavily_research sibling, but it never explicitly contrasts them.

    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: use this for focused questions, fact-checking, news, and source discovery. However, it does not explicitly mention the alternative tavily_research tool or state when to prefer that over tavily_search.

    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?

    Annotations already indicate this is not read-only and not idempotent, but the description adds important context not present there: the task is 'billable' and involves an asynchronous follow-up via tavily_research_status. This gives the agent critical operational expectations beyond the structured metadata.

    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 three sentences with no filler: it front-loads the core purpose, adds a clear usage restriction, and ends with the required follow-up action. Every sentence contributes essential information.

    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?

    Since there is no output schema, the description appropriately mentions the returned request ID and directs the caller to the status tool. It could additionally describe failure modes or how the final report is delivered, but for starting a research task it is largely complete.

    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%, and each parameter already has defaults, enums, and explanations. The description adds no parameter-level detail, but it does not need to because the input schema fully documents the parameters.

    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 a specific action and resource: 'Start an in-depth, billable research task' that performs multiple searches and produces a cited report. It distinguishes itself from the status sibling by emphasizing the initiation behavior and the eventual cited-report output.

    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?

    It explicitly says to use the tool 'only for complex questions requiring broad analysis,' which provides a clear when-to-use boundary. It also directs the caller to follow up with tavily_research_status, but it does not explicitly name tavily_search as the alternative for simpler questions.

    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?

    Annotations already declare read-only, idempotent, and non-destructive behavior, so the bar is lower. The description adds useful behavioral context by exposing the in_progress state and advising against duplicate task creation, which goes beyond the structured hints.

    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 no filler. The core purpose is front-loaded, and the actionable polling guidance is delivered efficiently without redundancy.

    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?

    The tool has no output schema, but the description communicates the response concept ('status or completed report') and the relevant in_progress condition. It is sufficient for a single-parameter polling tool, though it could optionally mention available status values.

    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 fully documents requestId as 'The request ID returned by tavily_research.' The description does not add parameter-specific meaning beyond referencing an existing task, which is acceptable but not additive.

    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 uses a specific verb ('Retrieve') and a specific resource ('status or completed report of an existing Tavily Research task'), clearly differentiating it from siblings that search or start research. It also identifies the prerequisite that the task must already exist.

    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 description gives explicit behavioral guidance: if status is in_progress, wait; and never start a duplicate task. This tells the agent when to poll this tool versus invoking tavily_research again, which is exactly the key decision point.

    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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