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jujubytes-a11y

Perplexity MCP Server

by jujubytes-a11y
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

  • Disambiguation3/5

    Both tools target web search via Perplexity with nearly identical descriptions, differing only in timeout and 'deep research' label. An agent may struggle to choose between them without clear use-case differentiation.

    Naming Consistency5/5

    Both tool names follow a consistent pattern: 'perplexity_search' and 'perplexity_deep_research' use the same prefix and verb_noun structure, making them clearly identifiable.

    Tool Count3/5

    With only 2 tools, the set is minimal but arguably covers the server's purpose (search and deep research). However, the overlap reduces the value of having two separate tools.

    Completeness2/5

    The set only provides two search variants, lacking any additional functionalities like result filtering, history, or configuration. Important search UX features are missing, making the surface incomplete for a comprehensive search server.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 3 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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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

  • Behavior3/5

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

    No annotations exist, so the description must carry the burden. It discloses the output format (JSON with answer, citations, optional fields), timeout behavior ('longer timeout suitable for exhaustive research'), and optional filter behavior. However, it does not discuss error states, rate limits, or authentication requirements, which could be important for agent planning.

    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 a clear front-loaded purpose and output format, followed by the timeout note and structured filter guidance. Every sentence adds value. The filter section could be more bulletized, but it remains easy to scan. No redundant or tautological content.

    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 tool's complexity (9 parameters, output schema exists), the description covers the core purpose, output format, timeout, and filter usage comprehensively. It does not need to explain return values since the output schema provides that. Missing details like error handling or limits are acceptable given the parameter and schema richness.

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

    Parameters4/5

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

    Schema description coverage is 100%, so the baseline is 3. The description adds value by connecting filter usage to real-world scenarios (e.g., 'current events / "latest" questions' for recency, 'absolute windows as MM/DD/YYYY' for date filters) and stating preferences (e.g., 'prefer over recency when exact'). This context clarifies when to use each parameter beyond the schema's individual descriptions.

    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 performs 'comprehensive synthesis via Perplexity Sonar Deep Research' and returns JSON with answer and citations. It implies a deeper, more exhaustive research capability compared to the sibling tool 'perplexity_search', though it does not explicitly differentiate. The purpose is specific and actionable.

    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 provides detailed guidance on when to use optional filters (e.g., 'search_recency_filter: current events / "latest" questions'), and advises 'Leave filters unset for broad evergreen queries.' However, it lacks explicit guidance on when to choose this tool over the sibling 'perplexity_search,' leaving the AI agent to infer based on the 'deep research' name and 'longer timeout' mention.

    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?

    No annotations are provided, so the description carries the full burden. It discloses return format and filter behaviors but does not mention rate limits, authentication, or potential side effects. Adequate but not comprehensive.

    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?

    Well-structured with front-loaded purpose, return format, and bullet-point filter guidelines. No unnecessary sentences; every part adds value.

    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 9 parameters, 100% schema coverage, and existing output schema, the description covers essential aspects. Could mention authentication or rate limits, but not critical for correct invocation.

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

    Parameters4/5

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

    Schema coverage is 100%, baseline 3. Description adds practical usage guidance beyond schema descriptions, such as when to use recency vs date filters and domain filter modes.

    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?

    Clearly states it is a general web search via Perplexity Sonar, returns JSON with answer and citations. The name and description distinguish it from sibling 'perplexity_deep_research'.

    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?

    Provides detailed guidance on when to use each optional filter, but does not explicitly compare this tool with the sibling deep research tool. Usage context is implied for general search.

    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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