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eyloni

pythia-the-oracle

by eyloni
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool 'consult_oracle' has a single, clearly defined purpose that is distinct by default.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency. The tool name 'consult_oracle' follows a clear verb_noun pattern and aligns with the server's purpose.

    Tool Count2/5

    One tool is too few for most server purposes, as it severely limits functionality and scope. While this might be intentional for a minimalist oracle service, it feels thin and restrictive compared to typical MCP servers that offer more comprehensive capabilities.

    Completeness3/5

    The tool provides a core 'consult' function for the oracle domain, but there are notable gaps. For example, there are no tools for managing readings (e.g., list_readings, delete_reading), checking status (e.g., get_reading_count), or handling administrative tasks (e.g., reset_oracle). This limits the server's utility for extended interactions.

  • Average 4.1/5 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 Creative Commons Attribution Non Commercial No Derivatives 4.0 International.

  • This repository includes a README.md file.

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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

  • Behavior4/5

    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 effectively describes key traits: it's a read-only operation (implied by 'readings' and 'response'), includes a free tier ('First 3 readings are free'), and emphasizes a non-solution-oriented, philosophical approach. However, it lacks details on rate limits beyond the free tier, authentication needs, or error handling.

    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 appropriately sized but not optimally front-loaded; it begins with poetic, abstract language before detailing usage and parameters. While each sentence adds value (e.g., philosophical context, usage guidelines, parameter explanations), the structure could be more direct by leading with practical information.

    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 the tool's complexity (philosophical oracle), no annotations, 0% schema coverage, but with an output schema (implied by 'Returns'), the description is largely complete. It covers purpose, usage, parameters, and behavioral traits, though it could benefit from more explicit details on output format or error cases, despite the output schema mitigating some gaps.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Given 0% schema description coverage, the description compensates fully by explaining all three parameters: 'query' as the real question (not polite, max 2000 chars), 'context' as optional background on attempts and frameworks, and 'agent_id' for identification across readings. It adds meaningful semantics beyond the bare schema, clarifying intent and constraints.

    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: to provide an oracle reading that names the structure of a trap or paradox the user is experiencing, rather than brainstorming or solving problems. It distinguishes itself by focusing on articulating unspoken truths, though without sibling tools for comparison, it cannot demonstrate differentiation beyond its unique philosophical approach.

    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 explicitly states when to use this tool: when the user has a question where obvious answers are dissatisfying, they are trapped in a paradox, or their 'architecture' limits their perspective. It also specifies what not to use it for (e.g., brainstorming, rephrasing, giving lists, or solving problems), providing clear context and exclusions, though no alternatives are mentioned due to lack of sibling tools.

    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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