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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one writes/persists context and the other reads/lists context. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: save_context and read_context. The naming is predictable and clear.

    Tool Count4/5

    With only two tools, the server is minimal but appropriately scoped for persisting and reading context files. It is slightly thin, but each tool serves a distinct and necessary function.

    Completeness4/5

    The core save/read workflow is covered, including listing topics via read_context when no topic is provided. An explicit delete/update tool is missing, but saving can overwrite existing context, so this is a minor gap.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 31 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.

  • Tools from this server were used 8 times in the last 30 days.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

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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

  • 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 of behavioral disclosure. It does reveal that the tool writes to a specific file path in the current working directory, which is helpful. But it does not disclose whether existing files are overwritten, whether directories are created implicitly, or any other side effects, such as the absence of a return 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 a single, tight sentence that front-loads the primary action and includes concrete content examples. Every word contributes to clarity, and there is no redundancy or padding.

    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?

    The description covers the essentials for a simple save operation, with fully documented parameters and a clear destination file. However, it omits overwrite semantics (what happens if the topic already exists) and gives no nod to the sibling read_context for retrieval. Given that there are no annotations or output schema, this leaves a notable but minor gap.

    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% for both required parameters. The description adds no parameter-specific meaning beyond what the schema already provides, so the baseline of 3 is appropriate: the schema fully documents topic naming and markdown content.

    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 states a specific verb ('persist'), a resource ('markdown project context, architectural rules, or decisions'), and a precise destination ('.opencontext/<topic>.md in the current working directory'). It clearly identifies a write operation and is readily distinguishable from the read_context sibling.

    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?

    The verb 'persist' and the file-target phrasing imply the use case: save context content for later retrieval. However, the description does not explicitly state when to use this tool versus read_context, nor does it mention that read_context is the appropriate counterpart for reading the saved files.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries the behavioral disclosure burden. It transparently reveals the two modes of operation and the optional parameter behavior, and the verb 'read' implies a non-mutating operation. It does not discuss error handling or return format, but that is a minor gap for such a simple read tool.

    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 with no filler. The primary read behavior is front-loaded, and the list-all alternative is stated compactly.

    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?

    This is a low-complexity tool with one optional parameter, and the description adequately covers both invocation modes. There is no output schema, and the description does not detail the return shape or error behavior, but an agent still has enough information to invoke the tool correctly in either mode.

    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%, and the schema already documents that 'topic' is an optional snake_case/kebab-case name and that omitting it lists all topics. The description adds little beyond what the schema already provides, so the baseline score of 3 is appropriate.

    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 states a specific verb ('read') and resource ('saved OpenContext topic'), and explicitly covers the alternative list-all behavior. It is clearly distinguishable from the sibling tool 'save_context'.

    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 clearly indicates when to call the tool: provide a topic to read it, or omit the topic to list available topics. It does not explicitly name 'save_context' as the alternative for writing, but the read-vs-save contrast makes the usage obvious.

    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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  • Confirm that the MCP server is working as expected.
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  • Evaluate tool definition quality.

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