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SpectraSynq

mcp-nexar

by SpectraSynq
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation4/5

    Each tool serves a distinct purpose: exact MPN lookup, free-text search, and datasheet URL retrieval. The potential confusion between part_by_mpn and best_datasheet_url is mitigated by clear descriptions of differing outputs.

    Naming Consistency2/5

    Tool names lack a consistent pattern. 'parts_search' follows a noun-verb structure, while 'part_by_mpn' uses a prepositional phrase and 'best_datasheet_url' is an adjective-noun combination. The inconsistent naming style makes the set feel ad hoc.

    Tool Count5/5

    With only 3 tools, the server is well-scoped for a focused part-search and datasheet service. Each tool is essential, and the count falls comfortably within the ideal range.

    Completeness4/5

    The server covers the core functions of part search (exact and free-text) and datasheet retrieval. Minor gaps exist, such as no explicit endpoint for part specifications or availability, but these are likely covered by the part_by_mpn response, making the surface adequate.

  • Average 3.6/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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 Apache 2.0.

  • 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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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

  • 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 merely states the search action but does not mention any return format, result ordering, pagination, rate limits, or authentication requirements. The behavioral traits beyond the basic action are not disclosed.

    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 effectively conveys the core purpose. There is no redundant or unnecessary information, and the key information is front-loaded.

    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 schema parameter descriptions, the description is too sparse to provide a complete understanding. It does not cover usage scenarios, parameter semantics fully, or any behavioral caveats. Although an output schema exists, the missing usage and parameter details leave the tool inadequately documented.

    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%, so the description must compensate. It clarifies that 'q' is the free text query, but the 'limit' parameter is not described at all; its default of 3 is present in the schema but without explanation of its meaning or effect. Thus, the description only partially explains the parameters.

    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 action ('Search') and the resource ('Nexar/Octopart') with a specific scope ('parts matching free text'). It distinguishes from sibling tools like 'part_by_mpn' (which searches by MPN) and 'best_datasheet_url' (which retrieves a datasheet URL).

    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 when to use the tool: when you have free text to search for parts. However, it does not explicitly say when not to use it or mention alternatives, such as using 'part_by_mpn' for exact part numbers. There is no explicit exclusion or alternative guidance.

    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?

    No annotations are provided, so the description carries the full burden. It discloses that the tool returns the 'first' bestDatasheet URL, implying an ordering or ranking behavior. However, it does not mention potential errors, rate limits, or side effects, though the read-only nature is implied by 'Return'.

    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 with no wasted words. It conveys the essential information efficiently.

    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 tool with one parameter and an output schema present, the description is adequate. It clearly states the return value (first bestDatasheet URL). It lacks edge-case behavior (e.g., no match found), but given the output schema and low complexity, this is a minor 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 0%, so the description must compensate. It does by explaining that the parameter mpn is a 'manufacturer part number', which is a key semantic. However, it does not provide format examples or constraints beyond that.

    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 a specific verb ('Return') and resource ('bestDatasheet URL') for a given manufacturer part number, which distinguishes it from siblings like part_by_mpn (likely returns part details) and parts_search (likely searches parts).

    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: when you need a datasheet URL for a specific MPN. However, it does not explicitly mention when to use this tool versus alternatives like part_by_mpn, nor does it provide any exclusions or prerequisites.

    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 must carry the full burden. It only states the basic search action and does not disclose any behavioral traits such as return format, error handling, or access requirements.

    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 with no redundant information, achieving maximum conciseness.

    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 is simple (one parameter) and an output schema exists, so return values need not be described. The description sufficiently covers the core function, though it lacks some behavioral depth and usage guidance.

    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 schema provides no description for 'mpn', but the description's phrase 'manufacturer part number' clarifies the parameter's meaning. For a single self-explanatory parameter, this is adequate compensation for the 0% 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 states a specific verb ('Search') and resource ('Nexar/Octopart') with a clear modifier ('exact manufacturer part number'), distinguishing it from sibling tools like parts_search and best_datasheet_url.

    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 exact MPN lookups but does not explicitly state when to use this tool versus alternatives (e.g., 'use parts_search for partial matches'). No exclusions or alternatives are mentioned.

    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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