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CaseyRo

mcp-timely

by CaseyRo
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.2

  • Disambiguation5/5

    Each tool serves a clearly distinct purpose: projects_overview provides project-level summaries, time_spent aggregates time data, and work_log returns raw entries. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent pattern of [noun]_[descriptor] (e.g., projects_overview, time_spent, work_log), making them predictable and easy to understand.

    Tool Count5/5

    Three tools are appropriate for a read-only time tracking reporting server, covering high-level overview, aggregated time, and detailed logs without unnecessary bloat.

    Completeness4/5

    The tools cover the main read operations for time tracking reporting, but lack write/update actions. For a reporting-focused server, this is acceptable, though a tool for client or user listing could enhance coverage.

  • Average 3.7/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 10 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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?

    Annotations already provide readOnlyHint, openWorldHint, idempotentHint; description adds date range format (YYYY-MM-DD) and notes about entries, but does not mention pagination, size limits, or other behavioral traits.

    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?

    Compact single sentence with a clarifying bracketed prefix, though the '[timely]' may be ambiguous; front-loaded with purpose.

    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?

    Description covers key output (entries with notes), references use cases (standup, diary, invoicing), and pairs well with existing annotations and output schema, making it mostly complete for a data retrieval tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema has 0% parameter description coverage. Description only indicates date range format but does not explain the 'since' and 'upto' parameters individually or their constraints.

    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 retrieves individual work log entries with notes for a date range, explicitly distinguishing from sibling tools like projects_overview and time_spent by calling it 'raw material' for standup, diary, and invoicing.

    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?

    Implies usage for raw activity entries, but no explicit guidance on when to use this versus siblings, nor when not to use it.

    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?

    Annotations already indicate read-only, idempotent behavior. The description adds value by disclosing that totals are computed server-side by Timely and that day buckets follow the account's timezone. This provides clarity beyond the annotations.

    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 concise, containing a few clear sentences. It front-loads the purpose and critical details without unnecessary words. The informal lead phrase 'Where did my time go?' adds personality but is still efficient.

    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 moderate complexity (3 params, output schema present), the description covers the main aspects: date range, grouping, timezone behavior, and server-side computation. It provides sufficient context for an agent to use the tool effectively.

    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 0%, so the description should compensate. It mentions the date range format (YYYY-MM-DD) and the group_by options, but does not elaborate on the meaning of 'since' and 'until' beyond the schema. Some value added but incomplete.

    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 computes server-side rollups for time spent, grouped by project/client/label/day, with billable split. It distinguishes from siblings by focusing on aggregated rollups rather than detailed logs or overviews.

    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 for aggregated time data, mentioning server-side rollups and grouping options. However, it does not explicitly state when to use this tool over siblings like work_log or projects_overview, leaving the agent to infer based on the 'rollup' nature.

    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?

    Annotations already declare readOnly, openWorld, and idempotent hints. The description adds behavioral details such as sorting by hours logged and specifying output fields (client, budget burn, unbilled amounts), providing additional context beyond 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 a single concise sentence that front-loads the purpose with '[timely] What's on my plate?' followed by a clear list of outputs. Every word is informative, with no wasted content.

    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 has one optional parameter and an output schema, the description covers the main purpose and output fields. However, it omits guidance on the include_inactive parameter, leaving a minor completeness gap.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, meaning the parameter include_inactive has no description in the schema. The tool description does not mention this parameter or explain when to set it to true, failing to compensate for the low schema coverage.

    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 returns every project with client, logged hours, budget burn, and unbilled amounts, sorted by hours logged. It specifies the resource (projects overview) and the action (list/retrieve), making it distinct from sibling tools like time_spent and work_log.

    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 through '[timely] What's on my plate?' suggesting a quick overview, but it does not explicitly provide when to use this tool versus alternatives or conditions when not to use it.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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