Skip to main content
Glama
iamfiro

Parcel Tracking MCP Server

by iamfiro
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

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 searches for carriers by name, while the other tracks parcel deliveries. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (search-carrier and tracking-delivery), using hyphens for separation. The naming is predictable and readable throughout the set.

    Tool Count2/5

    With only 2 tools, the server feels thin for a parcel tracking domain. A typical tracking system would include more operations, such as creating shipments, updating statuses, or handling multiple carriers, making this count insufficient for comprehensive coverage.

    Completeness2/5

    The toolset is severely incomplete for parcel tracking. It lacks essential CRUD operations like creating or updating shipments, retrieving carrier details beyond search, and managing delivery events. This will likely cause agent failures in common tracking workflows.

  • Average 3.3/5 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 the full burden of behavioral disclosure. While it mentions 'supports fuzzy typos', it doesn't describe what 'fuzzy' means operationally, whether there are rate limits, authentication requirements, error conditions, or what the response format looks like. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.

    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 one sentence with zero wasted words. It's front-loaded with the core purpose and efficiently adds the key behavioral detail about fuzzy matching in parentheses.

    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 lack of annotations and output schema, the description should do more to compensate. While the purpose is clear, it doesn't describe the return format, error conditions, or operational constraints. For a search tool that presumably returns results, the absence of output information is a significant 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%, so the schema already fully documents both parameters. The description adds minimal value by mentioning 'keyword' and 'fuzzy typos', but doesn't provide additional syntax, format details, or examples beyond what the schema descriptions already state about case-insensitivity and typos.

    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 'Search carriers by name keyword' with the specific functionality of 'supports fuzzy typos'. It uses a specific verb ('Search') and resource ('carriers'), but doesn't explicitly differentiate from the sibling tool 'tracking-delivery' which appears to have a different function.

    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 tool 'tracking-delivery' or any other search methods, nor does it provide context about when this fuzzy search is preferred over exact matching or other filtering approaches.

    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 states the tool's function but does not disclose behavioral traits such as authentication requirements, rate limits, error handling, or what the output might look like (e.g., tracking status details). This leaves significant gaps for an agent to understand how to invoke it effectively.

    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 directly states the tool's purpose without unnecessary words. It is front-loaded and every part earns its place, making it highly concise and well-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 tool's complexity (tracking with potential carrier detection) and lack of annotations and output schema, the description is incomplete. It does not cover behavioral aspects, output format, or error cases, which are crucial for an agent to use the tool correctly in real-world scenarios.

    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 both parameters thoroughly. The description does not add any meaning beyond what the schema provides (e.g., it doesn't explain parameter interactions or provide examples). Baseline 3 is appropriate as the schema does the heavy lifting.

    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 specific action ('track') and resource ('parcel delivery') with the service provider ('via 17TRACK'), making the purpose explicit. It distinguishes from the sibling tool 'search-carrier' by focusing on tracking rather than carrier lookup.

    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 implies usage context (tracking parcels with 17TRACK) but does not explicitly state when to use this tool versus alternatives like the sibling 'search-carrier'. It provides clear functional intent but lacks explicit comparison or exclusion guidance.

    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

parcel-tracking-mcp MCP server

Copy to your README.md:

Score Badge

parcel-tracking-mcp 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/iamfiro/parcel-tracking-mcp'

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