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joehaddad2000

Claude Todo MCP Server

by joehaddad2000
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: TodoRead is for retrieving the task list, while TodoWrite is for replacing it entirely. There is no overlap or ambiguity between these operations.

    Naming Consistency5/5

    Both tools follow a consistent TodoVerb naming pattern (TodoRead and TodoWrite), using the same prefix and clear action verbs. This makes their functions immediately understandable and predictable.

    Tool Count2/5

    With only two tools, the server is severely limited for a todo management domain. It lacks essential operations like creating, updating, or deleting individual tasks, making it impractical for typical todo workflows.

    Completeness1/5

    The tool surface is severely incomplete. It only supports reading and full replacement of the task list, missing critical CRUD operations such as add_todo, update_todo, delete_todo, or mark_complete. This will cause frequent agent failures in managing tasks.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 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

  • 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 this is a 'complete replacement' operation, which implies destructive behavior, but doesn't explicitly warn about data loss or confirm this is a mutation. It mentions return values but doesn't describe error handling, permissions, or rate limits. For a mutation tool with zero annotation coverage, this is insufficient.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is appropriately sized and well-structured with clear sections. The first sentence states the core purpose, followed by detailed parameter documentation and return information. Every sentence adds value, though the return statement could be slightly more concise.

    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?

    Given this is a mutation tool with no annotations but with output schema (returns success status and count), the description covers parameters well but lacks behavioral context. It doesn't explain what 'complete replacement' means operationally, potential side effects, or error conditions. The output schema reduces need for return value details, but overall completeness is moderate.

    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?

    The schema has 0% description coverage, so the description must fully compensate. It provides comprehensive parameter semantics: explains 'todos' is a list, documents all nested fields (id, content, status, priority, metadata), specifies enum values for status and priority, and clarifies metadata is optional. This adds substantial meaning beyond the bare schema.

    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 tool's purpose: 'Update the entire task list (complete replacement).' It specifies the verb ('update'), resource ('task list'), and scope ('entire', 'complete replacement'). However, it doesn't explicitly differentiate from the sibling TodoRead tool, which prevents a score of 5.

    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. It doesn't mention the sibling TodoRead tool, prerequisites, or scenarios where this complete replacement approach is appropriate versus incremental updates. The agent receives no usage context.

    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. It discloses that the tool returns a list of todos with their current state, which adds some behavioral context beyond the basic purpose. However, it lacks details on permissions, rate limits, error handling, or whether the operation is safe (e.g., read-only). For a tool with no annotations, this is a significant gap in 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 extremely concise with two sentences that directly state the purpose and return value. It is front-loaded with the core function and wastes no words, making it easy for an AI agent to parse quickly. Every sentence earns its place by providing essential information.

    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 low complexity (0 parameters, simple read operation), an output schema exists (which covers return values), and no annotations, the description is reasonably complete. It specifies what the tool does and what it returns, which is sufficient for basic understanding. However, it could benefit from more behavioral details given the lack of annotations, but the output schema mitigates this gap.

    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?

    The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate. Baseline is 4 for zero parameters, as it avoids redundancy and focuses on the tool's purpose.

    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 tool's purpose as 'Read the current task list' (verb+resource), which is specific and unambiguous. It distinguishes from the sibling TodoWrite by focusing on reading rather than writing. However, it doesn't explicitly mention what distinguishes it from potential other read operations beyond the sibling context.

    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 description implies usage context through the phrase 'current task list,' suggesting this tool is for retrieving existing todos. It doesn't provide explicit guidance on when to use this versus alternatives (though the sibling TodoWrite is clearly for writing), nor does it mention any prerequisites or exclusions. Usage is implied but not explicitly stated.

    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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