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FutureEnterprises

@unihodl/mcp-server

by FutureEnterprises
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.5

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: list_sessions discovers available sessions, while resume fetches details of a specific session. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Both tools follow a verb_noun pattern (list_sessions, resume), though 'resume' is a single verb without an explicit noun. The naming is mostly consistent and readable.

    Tool Count3/5

    With only 2 tools, the server is on the lower end of reasonable scope. It covers the basic read operations for sessions but feels minimal; additional tools like searching or filtering might be expected.

    Completeness3/5

    The server provides essential read functionality (listing and resuming sessions) but lacks any mutation operations or advanced filtering, leaving notable gaps for full session management.

  • Average 4.9/5 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
    • Last stable release on
    • 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.

  • This repository includes a glama.json configuration file.

  • 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

  • Behavior5/5

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

    The description adds behavioral context beyond annotations: 'Read-only and idempotent: it never modifies the session'. It also details error behavior (malformed id rejected before network call, unknown/expired/revoked session returns error message). This complements the annotations which already include readOnlyHint, destructiveHint, idempotentHint, and openWorldHint.

    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 concise and well-structured. It starts with the core purpose, then details contents, behavioral properties, usage guidance, format options, and error handling. Every sentence adds necessary information without redundancy.

    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?

    Given the tool's complexity (fetches multiple context items) and no output schema, the description provides a complete picture: what is returned (prompt-ready text or raw JSON object), error conditions, and integration with sibling tools. It covers all essential aspects for an AI agent to correctly invoke the tool.

    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?

    With 100% schema description coverage, the description still adds value: it explains the session_id pattern and how to obtain one, and clarifies the 'format' parameter with use cases for 'prompt-ready' and 'json', including the default and a reference to the JSON schema. This goes beyond the schema descriptions.

    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?

    The description clearly states the tool's purpose: 'Fetch a UNIHODL session as Resume Context'. It specifies the content (open tabs, scroll positions, etc.) and distinguishes from sibling tool list_sessions by noting that this tool is for fetching context given a session_id, while list_sessions is for discovering sessions.

    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 the tool: 'Use it when you have a session_id... and need the human's working context before continuing their task'. It also directs to list_sessions for session discovery. While it doesn't explicitly state when not to use it, the context is clear.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

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

    The description adds significant behavioral context beyond annotations: read-only and no side effects are stated, pagination mechanics are detailed, return shape is given, error handling for malformed cursors or since values is explained, and sandbox key behavior is noted. Annotations already indicate read-only and idempotent, so this adds more value.

    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 extremely concise, using three sentences to convey purpose, usage, return data, and errors. Every sentence adds value, and the most critical information (purpose and when to use) is front-loaded.

    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?

    Given that annotations cover safety (read-only, idempotent) and the input schema is fully described, the description adds all necessary behavioral and usage context. Return type is described, error conditions are noted, and the relationship to sibling tool is clarified. The tool is fully specified.

    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%, with descriptions for limit, cursor, and since. The description adds value by explaining how to use the cursor (pass next_cursor back as 'cursor') and that since expects ISO 8601 format. This goes beyond the schema's basic descriptions.

    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?

    The description clearly states the tool lists sessions in the workspace, sorted newest first, with cursor pagination. It distinguishes itself from the sibling tool 'resume' by specifying that this tool is for discovering sessions when no session_id is known, while resume is for directly accessing a known session.

    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?

    Explicitly states when to use this tool (when you do not have a session_id) and when to use the alternative (resume if you have one). Also provides clear instructions on pagination: pass next_cursor back as 'cursor' and that null means no more pages.

    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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