Skip to main content
Glama
belugatempo-dot

Perplexity Search MCP Server

by belugatempo-dot
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

Server Quality Checklist

25%
Profile completionA complete profile improves this server's visibility in search results.

  • A "release" on Glama is not the same as a GitHub release. To create a Glama release:

    1. if you haven't already.
    2. Go to the Dockerfile admin page, configure the build spec, and click Deploy.
    3. Once the build test succeeds, click Make Release, enter a version, and publish.

    This process allows Glama to run security checks on your server and enables users to deploy it.

    Read the detailed guide

  • Disambiguation3/5

    The two tools have overlapping purposes—both perform web searches using Perplexity AI—but their descriptions help differentiate them: one is for 'simple queries' with 'fast, real-time information,' while the other is for 'comprehensive web searches' with 'citations.' However, the distinction is subtle and could still lead to confusion, as both essentially search the web with real-time data.

    Naming Consistency5/5

    The tool names follow a consistent pattern: both use 'perplexity_' prefix and 'search' suffix, with descriptive adjectives ('quick' vs. 'web') to differentiate them. This clear and predictable naming convention makes it easy to understand their roles within the server.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a search domain, as it lacks operations like filtering results, saving searches, or managing search history. While the tools cover basic search functionality, the count is too low to provide a robust or complete search experience.

    Completeness2/5

    The server is severely incomplete for a search domain; it only offers two search variants without any supporting operations such as result pagination, query refinement, or citation management. This creates significant gaps that will limit an agent's ability to handle complex search tasks effectively.

  • Average 3.6/5 across 2 of 2 tools scored.

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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions 'real-time information' and 'citations,' it doesn't describe important behavioral aspects like rate limits, authentication requirements, error conditions, response format, or whether this is a read-only operation. For a web search tool with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.

    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 perfectly concise and well-structured. It uses just two sentences: the first states the core functionality, and the second provides usage guidance. Every word earns its place, with no redundancy or unnecessary elaboration. The information is front-loaded with the most important details first.

    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 that there's no output schema and no annotations, the description should do more to explain what the tool returns. While it mentions 'citations,' it doesn't describe the response format, structure, or what 'comprehensive' means in practice. For a search tool with 4 parameters and no structured output documentation, the description provides adequate but incomplete context.

    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 already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'citations' which relates to the 'return_citations' parameter, but this is already covered in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting.

    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: 'Search the web using Perplexity AI with real-time information and citations.' It specifies the verb ('Search'), resource ('the web'), and key capabilities ('real-time information and citations'). However, it doesn't explicitly differentiate from its sibling 'perplexity_quick_search' beyond mentioning 'comprehensive web searches,' leaving some ambiguity about when to choose one over the other.

    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 for when to use this tool: 'for comprehensive web searches that require up-to-date information, research, and source citations.' This gives specific use cases (research, needing citations) and distinguishes it from basic search tools. However, it doesn't explicitly state when NOT to use it or mention alternatives like the sibling tool, which would be needed for a perfect score.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool provides 'fast, real-time information' and requires 'minimal configuration,' which adds useful context about performance and setup. However, it doesn't disclose important behavioral aspects like rate limits, authentication requirements, error conditions, or what 'quick' specifically means operationally.

    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 perfectly concise with two sentences that each earn their place. The first sentence states the core functionality, and the second provides usage guidance. There's zero wasted language, and the information is front-loaded appropriately.

    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?

    For a single-parameter search tool with no annotations and no output schema, the description provides adequate basic context about purpose and usage. However, it lacks important details about what the tool returns (format, structure, limitations), error handling, and explicit differentiation from the sibling tool. The description is complete enough for basic understanding but has clear gaps for effective agent usage.

    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 with the single parameter 'query' well-documented as 'The quick search query.' The description adds some context by mentioning 'simple queries' and 'minimal configuration,' which provides qualitative guidance about parameter usage. However, it doesn't add significant semantic value beyond what the schema already provides.

    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 a 'quick web search using Perplexity AI' with the purpose of obtaining 'fast, real-time information.' This specifies both the action (search) and resource (web via Perplexity AI). However, it doesn't explicitly differentiate from its sibling 'perplexity_web_search' beyond implying 'quick' vs presumably more comprehensive.

    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 for when to use this tool: 'for simple queries when you need fast, real-time information with minimal configuration.' This gives practical guidance about query complexity and speed priorities. However, it doesn't explicitly state when NOT to use it or name the sibling tool as an alternative, though the implication is there.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

perplexity-skill MCP server

Copy to your README.md:

Score Badge

perplexity-skill MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/belugatempo-dot/perplexity-skill'

If you have feedback or need assistance with the MCP directory API, please join our Discord server