Skip to main content
Glama
tanigami

Perplexity MCP Server

by tanigami

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool 'ask_perplexity' has a singular, well-defined purpose for internet research with source-backed information, making it completely distinct by default.

    Naming Consistency5/5

    The single tool name 'ask_perplexity' follows a clear verb_noun pattern and is the only naming convention present. There are no other tools to compare it to, so consistency is inherently perfect.

    Tool Count2/5

    A single tool for a server named 'Perplexity MCP Server' feels thin and limited in scope. While the tool is specialized for research, the server's purpose suggests it could benefit from additional tools (e.g., for query refinement, citation management, or batch processing) to provide a more complete research workflow.

    Completeness2/5

    The tool surface is severely incomplete for a research-oriented server. It only offers a single query function without supporting operations like saving results, managing search history, filtering citations, or handling different query types. This creates significant gaps that will limit agent capabilities in research scenarios.

  • Average 3.6/5 across 1 of 1 tools scored.

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

    • 0 of 1 issues responded to in the last 6 months
    • No commit activity data available
    • 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 provided, the description carries full burden and adds valuable behavioral context: it discloses that responses include citations and choices, mentions timeout errors and retry advice, and details the response structure. However, it doesn't cover rate limits, authentication needs, or error handling beyond timeouts.

    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?

    The description is front-loaded with purpose and usage, but includes a detailed response structure section that might be verbose for a tool description. Some sentences (e.g., about citations and choices) could be more concise, and the structure listing feels like documentation rather than concise guidance.

    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 annotations, no output schema, and a simple 2-parameter tool, the description is reasonably complete: it explains purpose, behavioral traits (citations, timeouts), and response format. However, it lacks details on error types beyond timeouts and doesn't clarify if the tool is read-only or has side effects.

    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 both parameters (model and messages) thoroughly. The description adds no parameter-specific information beyond what's in the schema, meeting the baseline of 3 for high schema coverage without compensating value.

    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 as 'gathering source-backed information from the internet' with specific use cases like research and fact-checking. It distinguishes itself by mentioning citations and choices in responses. However, without sibling tools, differentiation isn't tested, and the verb 'ask' in the name isn't explicitly connected to the described functionality.

    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 usage scenarios ('research, fact-checking, or contextual data') but lacks explicit guidance on when to use this tool versus alternatives or prerequisites. No sibling tools exist, so comparative guidance isn't needed, but it doesn't specify constraints like query types or best practices.

    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

mcp-server-perplexity MCP server

Copy to your README.md:

Score Badge

mcp-server-perplexity 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/tanigami/mcp-server-perplexity'

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