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mitchallen

Synthetic Orders MCP Server

by mitchallen
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

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: check_target verifies API connectivity, preview_order generates a single order without sending, and send_orders generates and sends multiple orders. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (check_target, preview_order, send_orders). The naming is predictable and uniform.

    Tool Count5/5

    With 3 tools, the server is well-scoped for its purpose of generating and sending synthetic orders. Each tool serves a distinct function without redundancy or unnecessary bloat.

    Completeness5/5

    The tool set covers the essential workflow: checking the target, previewing an order, and sending orders. The ability to replay seeds in send_orders addresses the need for reproducibility, leaving no obvious gaps in the stated domain.

  • Average 4.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

  • 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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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 declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is clear. The description adds that the tool reports reachability and contents, but does not disclose details like response format, potential errors, or any side effects beyond what annotations already cover.

    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, front-loaded sentence that directly states the tool's purpose with no filler or redundancy. Every word contributes meaning.

    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?

    For a simple zero-parameter check tool with rich annotations and an output schema, the description adequately covers the tool's function. It clearly states what is reported (reachability and contents), though it does not elaborate on the meaning of 'what it holds' or offer guidance relative to sibling tools.

    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 zero parameters, so the baseline is 4. The description correctly avoids describing parameters that do not exist, and no additional parameter semantics are needed.

    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 uses a specific verb ('Report') and a clear resource ('the configured order API'), stating exactly what is checked: reachability and contents. This distinguishes it from siblings 'preview_order' and 'send_orders', which clearly perform different operations.

    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 using this tool to verify API availability and inspect its contents, which suggests a preflight check before operations like previewing or sending. However, it does not explicitly state when to use it versus the sibling tools or provide any exclusion criteria.

    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?

    Beyond the annotations, the description adds behavioral context: orders are POSTed, the result reports the seed, and replay is possible by passing the seed back. This clarifies side effects and error-path drilling, which is useful for an agent.

    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 two concise sentences and a follow-up sentence, all front-loaded with the primary action. No redundant text; every sentence adds operational value.

    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?

    The tool has an output schema, so return values likely are covered there. The description sufficiently covers the tool's purpose, mode distinctions, and replay behavior, making it complete for a low-complexity tool with three optional parameters.

    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?

    Schema description coverage is 0%, so the description must and does explain parameters: 'count' is the number of orders, 'mode' (seeded/simple) has usage semantics, and 'seed' is returned for replay. All three parameters are meaningfully covered in natural language.

    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 uses a specific verb 'Generate' and 'POST' with a clear resource ('orders') and destination ('configured order API'). It clearly distinguishes itself from sibling tools like check_target and preview_order, which likely handle other aspects of order workflow.

    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 explicit guidance on when to use each mode ('Use `seeded` for traffic that should be accepted and `simple` to drill the API's rejection path'). It does not explicitly mention when to use this tool over sibling tools, but the purpose is so distinct that no conflict arises.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    The description adds significant behavioral context beyond the annotations: it explains that 'seeded' samples the catalog while 'simple' invents fields, and that 'seed' reproduces an exact payload. This is useful, non-obvious information that helps the agent anticipate results. No contradiction with readOnlyHint or idempotentHint.

    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 three sentences, front-loaded with the core purpose, followed by concise parameter guidance. Each sentence adds necessary information without redundancy.

    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?

    Given the tool's simplicity, the presence of an output schema, and the annotations, the description is complete: it covers the operation's purpose, the mode behavior, and the seed parameter. No gaps remain for the agent to guess.

    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?

    Despite 0% schema description coverage, the description fully explains both parameters: 'mode' with its two enum values and their implications, and 'seed' for reproducing a payload. This provides meaning beyond the raw schema definitions.

    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 'Generate' and resource 'one order', with the key differentiator 'without sending it'. This clearly distinguishes it from sibling tools like send_orders, which actually sends orders.

    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 clear context for when to use the tool (previewing an order before sending) and even gives guidance on which mode to choose ('seeded' should be accepted, 'simple' should be rejected). It doesn't explicitly name sibling tools or state when not to use it, but the 'without sending it' contrast implies the alternative.

    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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