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sh1rokovs

timetta-mcp

by sh1rokovs
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
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: listing entities, retrieving schema for a specific entity, and querying data. No overlap or ambiguity.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern in snake_case: get_entity_schema, list_entities, query_odata. The pattern is uniform and predictable.

    Tool Count4/5

    Three tools is minimal but fits the focused scope of exploring and querying Timetta OData. It is not overly sparse for a read-only adapter; could potentially include more but earns its place.

    Completeness4/5

    The tool set covers the essential read operations: listing available entities, understanding their schema, and querying with rich OData semantics. For a read-only interface, there are no obvious gaps; write operations are likely out of scope.

  • Average 4.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 50 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

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  • 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 exist, so the description carries the burden. It is adequate (read-only listing) but does not explicitly confirm read-only or discuss auth or side effects.

    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?

    Single sentence, no filler, clear and front-loaded. Every word adds value.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Tool is simple with output schema provided. Description fully captures the necessary context for an AI agent to understand the tool's purpose.

    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?

    With zero parameters, baseline is 4. The description adds no parameter info, which is acceptable since none exist.

    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 states the specific verb 'List' and resource 'queryable Timetta OData entities (EntitySet names)', clearly distinguishing it from siblings like get_entity_schema and query_odata.

    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 discovering available entities but does not explicitly state when to use versus alternatives or provide contextual exclusions.

    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 are provided, so the description carries the full burden. It discloses return format (JSON array or error), default top (50), cap (200), and pagination via skip, which is sufficient for a query 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?

    The description is concise (a few sentences) and well-structured, with an example embedded. Every sentence adds value without redundancy.

    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 complexity (7 parameters, output schema present but not shown), the description covers key aspects: entity, filtering, selection, expansion, ordering, top cap, pagination. It may omit some edge cases but is largely complete.

    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?

    With 0% schema coverage, the description adds substantial value by explaining OData semantics, omitting the '$' prefix, providing an example, and detailing the top default and cap. This compensates for the lack of schema descriptions.

    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 'Query a Timetta OData entity' with a specific verb and resource, and distinguishes from sibling tools (get_entity_schema, list_entities) by focusing on data retrieval.

    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 provides a clear usage pattern with OData semantics and an example, but does not explicitly state when not to use this tool or mention alternative tools for different scenarios.

    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, but description clearly indicates a read operation (get schema). Does not mention any destructive behavior, auth, or rate limits, but none expected. Could add 'This is a read-only operation' for extra clarity.

    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: first defines purpose, second provides usage guidance. Front-loaded, no unnecessary words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Tool has output schema (not shown), so no need to explain return values. Description covers purpose, usage, and parameter semantics fully for a simple 1-param tool with well-known siblings.

    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?

    Schema has 0% coverage for parameter 'entity', but description explains it is for one entity and gives examples ('Users', 'TimeEntries'), adding essential context beyond the schema.

    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 states verb 'Get' and resource 'fields, types and navigation properties' for a Timetta entity, with examples 'Users', 'TimeEntries'. Clearly distinguishes from siblings: list_entities lists entities, query_odata queries data.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicitly says 'Call this before query_odata to learn real field names.' Provides clear when-to-use and implied when-not-to (for listing or querying). No exclusions needed.

    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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