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davidteren

Claude Server MCP

by davidteren

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: get_context retrieves a specific context, list_contexts lists contexts with filters, save_conversation_context saves conversation-specific context, and save_project_context saves project-specific context. The descriptions clearly differentiate between retrieval, listing, and two types of saving operations.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (get_context, list_contexts, save_conversation_context, save_project_context) with clear, descriptive names. The naming convention is uniform throughout the set, making it easy to understand each tool's function at a glance.

    Tool Count4/5

    With 4 tools, the count is reasonable for a context management server, covering key operations like retrieval, listing, and saving. It feels slightly minimal but well-scoped, as each tool serves a distinct and necessary function without redundancy.

    Completeness4/5

    The tool set provides good coverage for context management with get, list, and save operations for both conversation and project contexts. A minor gap exists in update or delete functionality for contexts, but agents can likely work around this by re-saving or managing contexts through the provided tools.

  • Average 2.9/5 across 4 of 4 tools scored.

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

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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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. 'Save' implies a write operation, but the description doesn't address permissions needed, whether this overwrites existing context with the same ID, what happens on success/failure, or any rate limits. The 'continuation support' hint is useful but insufficient for a mutation tool with zero annotation coverage.

    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 at just 5 words, front-loading the core purpose. Every word earns its place: 'Save' (action), 'conversation context' (resource), 'with continuation support' (key feature). There's zero waste or redundancy.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a mutation tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what happens after saving, what format the saved context takes, whether there are size limits on content, or how continuation actually works. The agent lacks crucial information about this write operation's behavior and outcomes.

    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 all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what's already in the schema descriptions. The baseline score of 3 is appropriate when the schema does the heavy lifting, though the description could have explained relationships between parameters like how 'continuationOf' relates to 'id'.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool saves conversation context with continuation support, which is a clear verb+resource combination. However, it doesn't distinguish this from its sibling 'save_project_context' - both appear to save context but for different types (conversation vs project). The purpose is understandable but lacks sibling differentiation.

    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 alternatives. There's no mention of when to choose 'save_conversation_context' over 'save_project_context', nor any prerequisites or typical use cases. The agent must infer usage from the tool name alone.

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

  • 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 states the action ('retrieve') but lacks behavioral details such as whether this is a read-only operation, error handling, permissions needed, or rate limits. The description is minimal and doesn't compensate for the absence of annotations.

    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 with a single sentence that directly states the tool's purpose. It's front-loaded and wastes no words, making it efficient for quick understanding.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given no annotations and no output schema, the description is incomplete. It doesn't explain what 'context' entails, the return format, or how it interacts with sibling tools. For a tool with 2 parameters and behavioral uncertainty, more context is needed to be fully helpful.

    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 both parameters. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain context types or project relationships). Baseline 3 is appropriate as the schema handles parameter documentation.

    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 ('retrieve') and resource ('context'), specifying it's done by ID with an optional project ID. However, it doesn't differentiate from sibling tools like 'list_contexts' or 'save_conversation_context', which would require more specific scope or purpose details.

    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?

    No guidance is provided on when to use this tool versus alternatives. The description mentions an optional project ID but doesn't explain when to include it or how this tool differs from siblings like 'list_contexts' or save operations.

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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden but offers minimal behavioral insight. It implies a read operation with filtering but doesn't disclose critical details like pagination, rate limits, authentication needs, or what 'list contexts' entails (e.g., format, scope). This leaves significant gaps for agent understanding.

    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 with a single sentence that front-loads the core purpose ('List contexts') and adds a brief qualifier ('with filtering options'). There is zero wasted verbiage, making it highly efficient.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given no annotations, no output schema, and a read operation with filtering, the description is incomplete. It lacks details on return values, error handling, or behavioral constraints, leaving the agent with insufficient context to use the tool effectively beyond basic parameter input.

    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 all three optional parameters (projectId, tag, type with enum). The description adds no additional meaning beyond implying filtering exists, matching the baseline for high schema coverage without extra param insights.

    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 action ('List contexts') and mentions filtering capabilities, which distinguishes it from simple listing operations. However, it doesn't explicitly differentiate from sibling tools like 'get_context' (which might retrieve a single context) or the save operations, missing full sibling differentiation.

    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 alternatives like 'get_context' for single context retrieval or the save tools for creation. It mentions filtering options but doesn't specify scenarios or prerequisites for usage.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool saves context with relationships, implying a write operation, but doesn't disclose critical behaviors like whether it overwrites existing context with the same ID, what permissions are required, error conditions, or response format. For a mutation tool with zero annotation coverage, this is a significant gap.

    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 front-loads the core purpose ('save project-specific context with relationships') with zero waste. Every word earns its place, making it appropriately sized for the tool's complexity.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (7 parameters, mutation operation, no output schema, and no annotations), the description is incomplete. It doesn't explain the return values, error handling, or behavioral nuances like how 'relationships' are enforced or what happens on duplicate IDs. For a save operation with rich parameters, more context is needed.

    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 7 parameters thoroughly. The description adds no additional meaning beyond implying relationships via 'parentContextId' and 'references', which is already clear from the schema. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra insights.

    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 action ('save') and resource ('project-specific context with relationships'), which is specific and actionable. It distinguishes from sibling 'save_conversation_context' by specifying 'project-specific' context, though it doesn't explicitly differentiate from 'get_context' or 'list_contexts' beyond the save vs. get/list distinction.

    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 alternatives like 'save_conversation_context' for conversation contexts or when to retrieve vs. save using 'get_context'/'list_contexts'. It lacks explicit when/when-not instructions or prerequisites, leaving usage context implied by the tool name alone.

    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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