Skip to main content
Glama
Donnyb369

MCP Spine

by Donnyb369
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.2.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: spine_recall is for retrieving cached results from past tool calls, while spine_set_context is for updating the active tool set based on the current task. There is no overlap in functionality, making it easy for an agent to choose the correct tool.

    Naming Consistency5/5

    Both tools follow a consistent snake_case pattern with a 'spine_' prefix and descriptive verb_noun combinations (recall and set_context). This predictable naming scheme enhances readability and usability.

    Tool Count2/5

    With only 2 tools, the server feels thin for a general-purpose 'Spine' system that manages tool visibility and caching. This minimal set may limit functionality and require agents to work around gaps, suggesting an under-scoped implementation.

    Completeness2/5

    For a server focused on tool management and caching, there are significant gaps: no tools for clearing or managing cached data, no way to list available tools or contexts, and no operations for tool lifecycle beyond setting context. This incompleteness could lead to agent failures in dynamic workflows.

  • Average 4.1/5 across 2 of 2 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
    • 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 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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It discloses that the tool affects tool visibility ('re-routes which tools are visible'), which is useful behavioral context, but it lacks details on permissions, side effects, or response format, leaving gaps for a mutation-like operation.

    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 front-loaded and concise with two sentences that directly explain the tool's function and effect, with no wasted words or redundant information, making it highly efficient.

    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 moderate complexity (a single parameter with full schema coverage) and no output schema, the description adequately covers the core purpose and usage. However, it could improve by addressing potential side effects or error cases to be fully complete.

    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?

    The schema description coverage is 100%, so the schema already documents the single 'task' parameter. The description implies the parameter's purpose ('what you are currently working on') but does not add significant meaning beyond the schema, aligning with the baseline for high coverage.

    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 with specific verbs ('Tell the Spine what you are currently working on') and resource ('re-routes which tools are visible'), distinguishing it from the sibling 'spine_recall' by focusing on context-setting rather than recall functionality.

    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 context for when to use this tool ('what you are currently working on'), but it does not explicitly mention when not to use it or name alternatives like 'spine_recall' for comparison, which limits it from a perfect score.

    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 the tool's read-only nature by stating it 'recalls' cached results without mentioning mutations, but lacks details on cache behavior (e.g., expiration, scope), error handling, or response format. It adds some context but is incomplete for behavioral transparency.

    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 front-loaded with the core purpose in the first sentence, followed by usage guidance. Both sentences earn their place by adding value, with zero wasted words, making it highly efficient and well-structured.

    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 moderate complexity (3 parameters, no output schema, no annotations), the description is reasonably complete. It clarifies the tool's purpose and usage but lacks details on output format or cache mechanics. With no output schema, more guidance on return values would improve completeness, but it's adequate for a recall operation.

    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 fully documents the three optional parameters. The description adds no parameter-specific information beyond what the schema provides, such as how 'query' matches keywords or how 'last_n' interacts with other filters. Baseline 3 is appropriate as the schema does the heavy lifting.

    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 specific action ('Recall cached results from previous tool calls') and resource ('cached results'), distinguishing it from its sibling 'spine_set_context' which likely sets rather than retrieves context. It avoids tautology by explaining functionality beyond the name.

    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?

    It provides explicit guidance on when to use this tool ('to check what a tool returned earlier without re-calling it') and a specific scenario ('especially if that tool is no longer in your active tool set'), which helps differentiate it from alternatives like re-invoking tools directly.

    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-spine MCP server

Copy to your README.md:

Score Badge

mcp-spine 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/Donnyb369/mcp-spine'

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