Skip to main content
Glama
GoldenPathAI

AI Consultant MCP Server

by GoldenPathAI
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: consult_ai is for executing AI consultations, while list_models is for retrieving model information. There is no overlap in functionality, making it impossible for an agent to confuse them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (consult_ai, list_models) with clear, descriptive names. The naming convention is uniform and predictable across the set.

    Tool Count2/5

    With only two tools, the server feels under-scoped for an 'AI Consultant' domain. While the tools cover consultation and model listing, there are likely missing operations like managing consultation history, configuring model parameters, or handling feedback, making the set feel incomplete for the stated purpose.

    Completeness2/5

    For an AI consultant server, the tool surface is severely incomplete. It lacks essential operations such as saving or retrieving past consultations, adjusting consultation settings, or providing feedback on model performance. The current tools only cover the most basic consultation flow, leaving significant gaps that will hinder agent workflows.

  • Average 3.5/5 across 2 of 2 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

  • Behavior2/5

    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 what data is returned (descriptions and use cases) but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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?

    The description is a single, efficient sentence that directly states the tool's purpose without any redundant or unnecessary words. It is front-loaded with the core action and resource, making it highly concise and well-structured.

    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 the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate as a basic overview. However, it lacks details on behavioral traits and usage context, which are needed for full completeness, especially without annotations to fill those gaps.

    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?

    The tool has 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description appropriately doesn't discuss parameters, which is correct for this case, earning a high score as it doesn't need to compensate for any gaps.

    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 verb ('List') and resource ('all available AI models'), specifying what information is returned ('with their descriptions and best use cases'). However, it doesn't differentiate from the sibling tool 'consult_ai', which might be a related AI interaction tool, so it doesn't reach the highest score.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus the sibling 'consult_ai' or any alternatives. It lacks context about prerequisites, timing, or exclusions, offering only a basic statement of function without usage instructions.

    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?

    No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the ability to auto-select models, sequential multi-model consultation, and that 'models' parameter takes precedence over 'model'. However, it lacks details on rate limits, authentication needs, response format, or error handling. For a complex AI consultation tool with no annotations, this is adequate but leaves significant gaps.

    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 front-loaded: two sentences that efficiently cover the core functionality and key usage patterns. Every sentence earns its place by providing essential information without redundancy or fluff.

    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 the tool's complexity (AI consultation with multiple parameters and no output schema), the description is minimally complete. It covers the basic purpose and usage but lacks details on response format, error conditions, or practical constraints. With no annotations and no output schema, the description should do more to compensate, but it provides just enough to be functional.

    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 6 parameters thoroughly. The description adds marginal value by explaining the auto-selection logic and precedence of 'models' over 'model', but doesn't provide additional semantic context beyond what's in the schema. Baseline 3 is appropriate when 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: 'Consult with an AI model via OpenRouter.' It specifies the action ('consult') and resource ('AI model'), though it doesn't explicitly differentiate from the sibling tool 'list_models' (which presumably lists available models rather than consulting them). The description is specific but lacks sibling differentiation for a perfect score.

    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 usage context: 'You can either specify a model or let the system auto-select based on your task.' It also mentions an alternative approach: 'For sequential multi-model consultation, use the 'models' parameter to specify multiple models.' However, it doesn't explicitly state when NOT to use this tool or compare it to the sibling 'list_models', preventing a score of 5.

    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

ai-consultant-mcp MCP server

Copy to your README.md:

Score Badge

ai-consultant-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/GoldenPathAI/ai-consultant-mcp'

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