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sophiabanda

zendesk-mcp

by sophiabanda

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: finding similar tickets, gathering customer context, assessing solutions by version, and summarizing daily work. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (e.g., search_similar_tickets, get_customer_context), making them predictable and easy to navigate.

    Tool Count5/5

    Four tools are appropriate for a focused support assistant server. Each tool addresses a specific need without superfluous or missing functionalities.

    Completeness2/5

    The tool set covers information retrieval and analysis but lacks core actions like creating, updating, or commenting on tickets, which are essential for full ticket lifecycle management. This creates dead ends for agents needing to take action.

  • Average 4.1/5 across 4 of 4 tools scored.

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

    • No community issues in the last 6 months
    • 12 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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?

    No annotations provided. The description implies a read-only search, but does not disclose specific behaviors (e.g., what happens if no solutions found, or performance characteristics). Adequate but could be improved.

    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?

    Two sentences with no wasted words. The first sentence explains the action, the second explains the use case. Highly concise and front-loaded.

    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?

    No output schema is provided, yet the description does not explain what the tool returns (e.g., list of solutions with applicability flags). The description covers purpose and usage but lacks output details and edge-case behavior, making it 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 67%, meaning two of three parameters have descriptions. The tool description does not add extra meaning beyond the schema; for example, topK has no description in the schema and is not elaborated in the description. Baseline 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 clearly states the verb (search/check) and resource (past solutions/workarounds, product version). It distinguishes from siblings like search_similar_tickets by focusing on version applicability.

    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?

    Explicitly states when to use: 'figure out if a known fix should already cover the customer's version, or if they need to upgrade.' No exclusions or alternatives are mentioned, but 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.

  • Behavior3/5

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

    No annotations are provided, so the description must cover behavioral traits. It states the tool uses semantic search over ticket history plus keyword search, implying a read-only operation. However, it lacks details on rate limits, ordering of results, or what 'similar' means. The description is adequate but could be more informative.

    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?

    Description is only two sentences: the first explains the tool's action and method, the second gives usage guidance. There is no unnecessary information, and every sentence serves a clear purpose.

    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 tool has two parameters fully described in the schema, no output schema, and no annotations. The description explains the purpose and usage, but lacks details on the output format (e.g., what fields are returned, how similarity is scored) and more behavioral context. While functional, it is not 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?

    Schema description coverage is 100%, so baseline is 3. The description adds an example for the `issue` parameter ('PDF export hangs on large files') but does not provide additional semantics beyond what the schema already offers. Both parameters are well-documented in the schema, so the description does not need to add much.

    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?

    Description clearly states the tool finds past Zendesk tickets similar to a described issue using semantic search and keyword search. The verb 'find' is specific, the resource is clearly identified as past Zendesk tickets, and the description distinguishes it from sibling tools that focus on customer context, solution assessment, or daily work summarization.

    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?

    Description explicitly says to use this tool 'before triaging a new ticket to check for precedent,' providing a clear usage context. While it does not mention when not to use it or alternative tools, the provided context is sufficient for an AI agent to understand when this tool is appropriate.

    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?

    Discloses the tool gathers org, tickets, and issues—a read-only operation. Without annotations, description carries burden; no side effects or limitations (e.g., data freshness, rate limits) are mentioned, but basic behavioral context is provided.

    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?

    Two sentences, zero waste. First sentence states purpose, second gives usage guidance. 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 no output schema, the description adequately explains output: org, past tickets, prior issues. Could specify format or additional details, but sufficient for understanding the tool's value.

    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 has 100% coverage with basic descriptions. The description adds value by differentiating the two parameters: organization for whole account, requesterEmail for single contact, and gives an example ('Anthology').

    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 pulls together customer information including org, tickets, and issues. It distinguishes from sibling tools like search_similar_tickets by focusing on customer context rather than similarity search.

    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?

    Explicitly recommends using this before responding to a ticket to understand history. Explains when to use each parameter (email vs. organization). Does not explicitly state when not to use or name alternatives.

    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 provided, so description carries full burden. Describes what the summary includes (tickets touched, solved, open, high-priority) and notes assignee defaults. Omits output format, but is acceptable for a read-only summary 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?

    Two concise sentences: first states purpose, second gives usage context. No redundancy or fluff.

    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?

    With no annotations or output schema, the description covers purpose, key content, and usage context. Could mention output type, but overall complete for a simple summary tool.

    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 coverage is 100% and already includes default descriptions. The tool description adds no additional meaning beyond what is in the schema, so 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?

    Clearly states verb (summarize) and resource (Zendesk activity for a day). Distinct from sibling tools like search_similar_tickets which are different purposes.

    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?

    Explicitly recommends when to use ('end of day or during standup prep'). No explicit exclusions or alternatives, but context is clear given distinct sibling purposes.

    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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