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quequiere

perplexity-web-mcp

by quequiere
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TypeScript

Server Quality Checklist

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

  • Latest release: v1.1.1

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: login handles authentication, search performs general queries with default settings, and search_advanced offers source control. The descriptions explicitly differentiate when to use each, eliminating ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent snake_case pattern with simple verbs (login, search) and a descriptive variant (search_advanced). The naming is predictable and logical.

    Tool Count4/5

    With only 3 tools, the server is minimal but appropriately scoped for its purpose of providing Perplexity AI search capabilities. The count is on the low end but still reasonable and focused.

    Completeness4/5

    The tool set covers the core workflow: authentication (login) and two search modes (general and advanced). This is sufficient for most use cases; missing features like result filtering or account management are not critical gaps.

  • Average 4.1/5 across 3 of 3 tools scored. Lowest: 3.5/5.

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

    • 1 of 4 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • 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 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 are provided, so the description carries the transparency burden. It discloses that it uses default Perplexity settings and returns a synthesized answer with sources, but it does not mention potential rate limits, authentication needs, or other behavioral traits.

    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 front-loads the purpose and result, and the second adds relevant context about default settings. It is efficiently structured.

    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 simple tool with one parameter and no output schema, the description is fairly complete. It explains what the tool does and mentions the default setting context. However, it could mention any output format or prerequisites.

    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?

    With 100% schema description coverage, the baseline is 3. The description does not add any additional meaning to the 'query' parameter beyond what the schema already provides, so it meets the baseline.

    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 searches the web using Perplexity.ai and returns an AI-synthesized answer with cited sources. It distinguishes itself from the sibling 'login' but does not explicitly differentiate from 'search_advanced', though mentioning 'default settings' hints at a distinction.

    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 that normal searches use defaults, but it doesn't explicitly state when to use this tool versus 'search_advanced' or provide any prerequisites or context for usage.

    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?

    Discloses key behaviors (authentication check, browser opening) without annotations, but could detail post-login behavior.

    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?

    Single, front-loaded sentence with no wasted words, efficiently conveying purpose and action.

    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?

    Covers core functionality well; lacks detail on return value or post-login state, but adequate for a simple 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?

    No parameters, so baseline 4 applies; description adds no parameter info but doesn't need to.

    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 the tool checks authentication and optionally opens a browser for login, distinguishing it from sibling search tools.

    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?

    Describes when to use (to check/login) and what happens next, though no explicit when-not or alternatives are given.

    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?

    No annotations are provided, so the description carries the full burden. It correctly describes the operation as a search, implying non-destructive behavior. While it doesn't detail auth, rate limits, or output format, the core behavior is transparent. A small deduction for missing details that could be useful.

    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 sentences that front-load purpose and usage guidelines. Every sentence adds value with no redundancy or fluff.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the simplicity of the tool (two required parameters, no output schema, no nested objects), the description adequately covers its function and when to use it. It is complete for an AI agent to decide and invoke correctly.

    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?

    Both parameters have schema descriptions, providing 100% coverage. The description reinforces the ability to combine sources (e.g., web + academic), which adds some context but does not significantly enhance understanding beyond the 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 states it searches Perplexity.ai with specific source selection, and distinguishes from the sibling 'search' tool by noting that 'search' is preferred for general queries. This provides a specific verb-resource pair with differentiation.

    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?

    Explicitly tells when to use this tool ('when source control matters') and when not to ('prefer `search` for general queries'), along with an example of combining sources. This is excellent guidance for an AI agent.

    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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Glama performs regular codebase and documentation scans to:

  • 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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