Skip to main content
Glama
OneCuriousLearner

TAPD Data Fetcher

by OneCuriousLearner
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

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: one fetches defect data (bugs) and the other fetches requirement data (stories) from TAPD. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the data type needed.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'get_tapd_' prefix followed by the data type (bugs, stories). The naming is predictable and adheres to snake_case throughout, providing clear and uniform identification.

    Tool Count2/5

    With only 2 tools, the server feels thin for a data fetcher scope. While bugs and stories are core entities, typical TAPD operations might include updates, searches, or other entity types (e.g., tasks, releases), suggesting an incomplete toolset that could limit agent workflows.

    Completeness2/5

    The server only provides read operations for two entity types (bugs and stories), lacking CRUD coverage (e.g., create, update, delete) and other common TAPD functionalities like filtering, sorting, or accessing additional resources. This creates significant gaps for comprehensive data management.

  • Average 3.1/5 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.

  • 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

  • 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. It mentions pagination support and the return format ('格式化后的需求数据JSON字符串'), but fails to disclose critical behavioral traits such as authentication requirements, rate limits, error handling, or what '指定项目' (specified projects) means operationally. For a data retrieval tool with zero annotation coverage, this leaves significant gaps.

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

    Conciseness3/5

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

    The description is brief (two sentences) but could be more front-loaded. The first sentence states the purpose and pagination support, while the second describes the return format. However, the structure isn't optimal - the return format information might be better integrated or placed in an output schema. It's concise but not perfectly structured.

    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 absence of annotations and output schema, the description is incomplete. While it covers the basic purpose and mentions pagination and return format, it lacks crucial context about authentication, error conditions, how projects are specified, and differentiation from the sibling bug tool. For a data retrieval tool in what appears to be a TAPD integration, this leaves too many unanswered questions.

    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 appropriately doesn't discuss parameters, maintaining focus on the tool's purpose and behavior. This meets the baseline expectation for tools without parameters.

    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: '获取TAPD平台指定项目的需求数据' (get requirement data for specified projects on TAPD platform). It includes a specific verb ('获取' - get) and resource ('需求数据' - requirement data). However, it doesn't explicitly differentiate from its sibling tool 'get_tapd_bugs', which likely retrieves bug data rather than requirement data.

    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 minimal usage guidance. It mentions support for pagination ('支持分页'), which suggests when to use this tool for large datasets, but offers no explicit guidance on when to use this versus the sibling 'get_tapd_bugs' tool, nor any prerequisites or alternative scenarios. The guidance is insufficient for proper tool selection.

    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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It adds some context: it mentions pagination support and specifies the return type as a formatted JSON string. However, it doesn't disclose important behavioral traits like authentication requirements, rate limits, error handling, or what '指定项目' means in practice. The description doesn't contradict annotations (none exist), but provides only basic operational context.

    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 concise with two sentences that each add value: the first states the core purpose and mentions pagination, the second specifies the return format. It's front-loaded with the main functionality. There's minimal waste, though the Chinese/English mix could be slightly cleaner.

    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 the tool has no parameters (simple complexity) and no output schema, the description provides basic completeness: purpose, pagination hint, and return format. However, for a tool interacting with an external platform (TAPD), it should ideally mention authentication requirements or data scope limitations. Without annotations and with no output schema, the description does the minimum viable job but leaves gaps about operational constraints.

    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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to explain parameters, and it appropriately doesn't attempt to describe nonexistent parameters. The mention of '指定项目' (specified projects) might imply some parameterization, but since the schema explicitly has no properties, this doesn't create confusion.

    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: '获取TAPD平台指定项目的缺陷数据' (get defect data for specified projects on TAPD platform). It includes a specific verb ('获取' - get) and resource ('缺陷数据' - defect data), and distinguishes from the sibling tool 'get_tapd_stories' by focusing on bugs rather than stories. However, it doesn't specify what '指定项目' (specified projects) means since there are no parameters.

    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 minimal usage guidance: it mentions support for pagination but doesn't explain when to use this tool versus the sibling 'get_tapd_stories' (which presumably gets story data). There's no explicit when/when-not guidance or alternative tool recommendations. The mention of pagination is helpful but insufficient for comprehensive guidelines.

    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

MCPAgentRE MCP server

Copy to your README.md:

Score Badge

MCPAgentRE 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/OneCuriousLearner/MCPAgentRE'

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