Skip to main content
Glama
agent-engineering-studio

mcp-geo-server

by agent-engineering-studio
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

    Only one tool exists, so there is no possibility of confusion between tools. The single tool serves all purposes.

    Naming Consistency5/5

    With a single tool, naming is trivially consistent. The name 'geoserver-agent' clearly indicates its role.

    Tool Count2/5

    One tool for a broad domain (GeoServer administration, queries, OGC operations) is too coarse. It would be better to split into multiple focused tools (e.g., workspaces, datastores, layers). The single agent obscures individual capabilities.

    Completeness3/5

    The tool claims to handle many GeoServer aspects (workspaces, datastores, feature types, layers, styles, OGC operations), so coverage is broad. However, because it's a single monolithic agent, it's unclear whether all operations are reliably exposed or if there are gaps. A proper tool set would make completeness more assessable.

  • Average 3.2/5 across 1 of 1 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 25 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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?

    No annotations are provided, so the description carries full burden. It only lists resource types and suggests read/write capability, but fails to disclose that the tool interprets free-text tasks via an LLM, potential side effects, authentication needs, or rate limits. Critical behavioral traits are missing.

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

    Conciseness4/5

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

    The description is a single sentence that efficiently conveys the tool's purpose and scope. It is front-loaded with the key action ('administers and queries') but lists several resources, making it slightly verbose. Overall, it earns its place without excessive wordiness.

    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 (one free-text parameter, no output schema) and the agent-like nature, the description adequately outlines the domain and operations. However, it does not explain how the agent processes tasks (e.g., LLM interpretation), which is important for understanding behavior. Slightly incomplete.

    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% with a description for 'task' that is minimal ('Task for geoserver-agent'). The tool description adds context (GeoServer administration) but does not elaborate on the format or expected input for the task parameter. Baseline score of 3 applies as schema already covers the parameter.

    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 it is an intelligent assistant for administering and querying a GeoServer instance, listing specific resources and operations. However, the phrase 'intelligent assistant' is somewhat vague, and the tool name is generic. Still, it effectively conveys the tool's domain and actions.

    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?

    No sibling tools exist, so differentiation is not needed. However, the description provides no guidance on when to use this tool versus alternatives (none exist), nor does it specify prerequisites or typical use cases. Usage is implied but not explicitly guided.

    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-geo-server MCP server

Copy to your README.md:

Score Badge

mcp-geo-server 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/agent-engineering-studio/mcp-geo-server'

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