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Mobai-read

SciTrace MCP Server

by Mobai-read
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    build_trace records a new reasoning step, while query_trace retrieves existing steps. Their purposes are clearly distinct, with no overlap in functionality.

    Naming Consistency5/5

    Both tools follow the consistent verb_noun pattern (build_trace, query_trace), making their actions predictable and easily understood.

    Tool Count3/5

    With only 2 tools, the set is minimal, but each tool is essential for the server's stated purpose of recording and querying reasoning traces. This falls at the borderline of being too thin.

    Completeness4/5

    The server covers the core write (build_trace) and read (query_trace) operations for reasoning traces. Missing update/delete functionality is a minor gap, but acceptable for an append-only trace logging context.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 18 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.

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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

  • 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 of behavioral disclosure. It states that the tool 'records' a step but does not disclose side effects such as whether the step is appended, overwritten, persisted immediately, or whether an existing trace is required. It also omits any mention of return values or failure modes, providing minimal insight beyond the input schema.

    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 with no filler content. It front-loads the core action ('Record a reasoning step') and then efficiently summarizes the optional fields, making it appropriately sized and well-structured.

    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?

    The description adequately covers the tool's write behavior and input structure for basic use, but it leaves gaps regarding the lifecycle of a trace (e.g., whether the trace must already exist) and the relationship to the sibling query_trace. No output schema exists, so the description should provide more context about what happens after recording, which it does not.

    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?

    The input schema provides descriptions for all six parameters, achieving 100% coverage. The description adds some context by mentioning that parent_id builds a reasoning DAG and artifacts are file paths, but these largely mirror the schema descriptions. Since the schema handles the parameter semantics well, the baseline score of 3 is appropriate.

    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 the specific verb 'Record' with the resource 'a reasoning step from the agent's execution', and explicitly enumerates the step types and structural elements (parent_id, artifacts). This clearly differentiates it from the sibling query_trace, which is a read-oriented tool.

    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 gives context ('from the agent's execution') implying when the tool should be used, but it lacks explicit 'when not to use' guidance or any mention of the sibling query_trace as an alternative. No exclusions or prerequisites are stated, so the usage guidance is only implicit.

    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?

    The description discloses key behaviors: results are ordered by most recent first and limited by the limit parameter. However, it does not explicitly state that the operation is read-only or that no modifications occur, and there are no annotations to fall back on. The description gives basic insight but not comprehensive disclosure.

    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 short sentences, each serving a clear purpose: purpose, filters, and result behavior. No unnecessary words or redundant information.

    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 query tool with three optional parameters and no output schema, the description covers the purpose, filtering options, ordering, and limit. It lacks explicit information about response format or state effects, but given the simplicity, it is adequately complete.

    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?

    The schema already provides 100% coverage with clear descriptions of each parameter. The description adds the nuance that filters can be combined ('and/or') and reiterates the limit behavior, but does not introduce new semantic meaning 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?

    The description uses the specific verb 'Query' with resource 'historical reasoning steps', clearly stating the operation. It also distinguishes from sibling 'build_trace' by implying a retrieval vs creation distinction, so purpose is unambiguous.

    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 provides context for querying but does not explicitly state when to use this tool versus 'build_trace' or any alternative. The intent is implied by the verb 'query' and the mention of 'historical steps', but no explicit guidance is given.

    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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