Skip to main content
Glama
fastmcp-me

Perplexity Advanced MCP

by fastmcp-me
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.3

  • Disambiguation5/5

    With only one tool, there is no ambiguity. The purpose of 'ask_perplexity' is clearly defined as searching the internet and answering queries.

    Naming Consistency5/5

    The single tool name 'ask_perplexity' follows a clear verb_noun pattern, which is consistent and intuitive.

    Tool Count3/5

    The tool count of 1 is minimal. While the tool is comprehensive, offering both simple and complex queries, a server named 'Advanced MCP' might be expected to have additional tools for features like managing query history or retrieving sources.

    Completeness4/5

    The single tool covers the core functionality of searching and answering queries, including both simple and complex modes. However, there may be minor gaps such as not providing separate tools for citation or follow-up interactions.

  • Average 4.5/5 across 1 of 1 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.

  • 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

  • Behavior4/5

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

    With no annotations, the description carries full burden. It explains the search process (searches, opens top results, finds info, provides answer), pricing/speed trade-offs, and prompt engineering requirements. However, it does not explicitly state that the tool is read-only or idempotent, though implied.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Description is overly verbose, including examples and pricing details that could be streamlined. While front-loaded with purpose, it contains redundant elaboration (e.g., step-by-step hypothetical) that increases length without proportional benefit.

    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?

    For a tool with three required parameters and no output schema, the description is highly comprehensive. It covers all parameter semantics, usage guidelines, behavioral traits, and even prompt engineering tips, making it fully complete for an agent to invoke correctly.

    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%, so baseline 3. Description adds significant value: explains query_type enum choices with detailed use cases, and mandates absolute paths for attachment_paths. Also notes English-only for query, which is not 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?

    Description clearly states that Perplexity is an LLM that searches the internet to answer queries, with explicit examples (e.g., latest Python version) and differentiation between simple and complex queries. The verb 'ask' plus resource 'perplexity' is specific and unambiguous.

    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?

    Provides explicit when-to-use guidance: use when uncertain or questionable, and instructions for choosing query type (simple for straightforward, complex for multi-step analysis). Also mandates English queries and absolute paths for attachments, leaving no ambiguity.

    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-advanced-mcp MCP server

Copy to your README.md:

Score Badge

perplexity-advanced-mcp 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/fastmcp-me/perplexity-advanced-mcp'

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