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teoobarca

perplexity-mcp

by teoobarca
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.4.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one for quick answers and tech lookups, the other for deep research with extensive citations. There is no overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent pattern with the 'perplexity_' prefix and a descriptive verb ('ask', 'research'), making it easy to understand their purpose.

    Tool Count4/5

    With only two tools, the server covers the core use cases of quick queries and deep research. While minimal, it is well-scoped and not overly sparse for its intended purpose.

    Completeness4/5

    The tool set covers the primary interactions with the Perplexity AI model: quick synthesis and comprehensive analysis. There are no major gaps, though additional tools for streaming or specific models could be considered.

  • 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
    • 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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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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 carries the full burden. It notes that Perplexity is an AI model (not a search engine) and returns synthesized answers with citations. It does not mention potential limitations, auth needs, or rate limits, leaving gaps in behavioral understanding.

    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 two sentences. The first sentence states the purpose, and the second provides guidance and output expectations. It is efficiently structured and front-loaded.

    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?

    The description covers the basic purpose and output (citations) but lacks details like default sources, limitations, or comparison with the sibling tool. It is adequate for a simple Q&A tool but not fully comprehensive.

    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 baseline is 3. The description adds a usage tip for the query parameter (provide context) but does not add significant meaning beyond the schema for the other parameters.

    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: an AI-powered answer engine for tech questions, documentation lookups, and how-to guides. It distinguishes from a search engine but does not differentiate from its sibling tool perplexity_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?

    It advises providing context and specific requirements for better results, implying usage scenarios. However, it does not explicitly state when to use this tool vs. its sibling, nor does it mention when not to use it.

    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 provided, so description carries full burden. It discloses extensive research behavior and citation count, but omits potential downsides like cost, latency, or rate limits. Adequate but not exhaustive.

    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?

    Two concise paragraphs, front-loaded with purpose. Each sentence adds value; no wordiness. Slightly longer than necessary but still efficient.

    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 no output schema, description covers return type (extensive reports with citations) and parameter usage. Might benefit from output format details, but overall sufficient for a research tool.

    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%, and description adds valuable context for the 'query' parameter with an example and guidance on detail level. Sources and language are adequately described in schema.

    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 identifies the tool as a 'deep research agent' for comprehensive analysis, distinguishing it from the sibling 'perplexity_ask' by emphasizing deeper synthesis, more time, and extensive citations.

    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?

    Explicitly states use cases like architecture decisions and technology comparisons, and guides on providing detailed context. Lacks explicit 'when not to use' but context implies lighter queries belong to sibling.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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