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ByteAsk

byteask-embedded-docs

Official
by ByteAsk
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Python
Hybrid

Server Quality Checklist

83%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct, non-overlapping purpose: search_docs for searching, get_context for expanding search results, and request_document for adding new documents. No two tools could be confused.

    Naming Consistency5/5

    All tool names follow a clear verb_noun pattern with underscores (search_docs, get_context, request_document), consistently using imperative verbs and descriptive nouns.

    Tool Count5/5

    With only 3 tools, the server is well-scoped for its domain. Each tool is essential and justified, covering the primary operations without unnecessary bloat or gaps.

    Completeness5/5

    The tool set covers the full workflow: searching for information, retrieving full context from search results, and requesting new documents when missing. No obvious gaps for the stated purpose of an embedded docs corpus.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 8 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

  • 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",
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  ]
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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. The description does not disclose behavioral traits such as whether the operation is read-only, if it requires specific permissions, or any rate limits. The verb 'expand' implies retrieval but is not explicit about side effects or safety.

    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 extremely concise with two clear sentences plus param explanations. It is front-loaded with the main purpose, and every sentence adds value. No waste.

    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 only two simple parameters and the presence of an output schema, the description is complete. It explains the tool's purpose, how to use it, and the role of each parameter. The output schema covers return values.

    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 description coverage is 0%, so the description fully compensates. It explains that result_id is from a search_docs hit, and that effort is an internal diagnostic tag clients should leave unset. This adds critical meaning beyond the schema's type/title.

    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 it expands a previous search hit to its full verbatim section in markdown. It distinguishes from siblings by specifying it operates on a search hit's result_id, not on documents or raw search.

    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 explains that result_id comes from search_docs, and warns clients not to set effort. This gives clear context on when to use it after search_docs, but does not explicitly compare to sibling tools or state 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?

    No annotations are provided, so the description carries full burden. It discloses that the tool is a request (not immediate), that it's reviewed, and typical turnaround time (24 hours). It does not mention authentication or rate limits but covers the core behavior well.

    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?

    Three sentences, front-loaded with purpose. Every sentence adds value: what it does, when to use, what to pass, and outcome. No 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 output schema exists (not shown), explanation of return values is unnecessary. The description covers the action, context, and outcome. However, it omits mention of the effort parameter and could clarify that the request is for documents not already in the corpus.

    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 coverage is 0%, so description must compensate. It specifies that the 'request' parameter should be a single string containing document details. However, it does not describe the 'effort' parameter, which may lead to confusion. The instruction 'Pass ONE string' could be misinterpreted as ignoring the second parameter.

    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's purpose: requesting a document to be added to the corpus. It distinguishes from sibling search_docs by explicitly stating this tool does not search. The verb 'request' and resource 'document to be added' are specific.

    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 tells when to use (when search_docs returns no confident match for specific materials) and when not to (use search_docs for searching). Also provides guidance on what to include in the request string and mentions SLA (24 hours).

    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?

    Despite no annotations, the description fully discloses behavior: returns verbatim evidence, never fabricates, returns 'no confident match' on miss, and notes it is faster than web search. This provides complete transparency for an AI agent.

    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 lengthy but well-structured and front-loaded with key purpose and usage guidelines. Each sentence adds value, though some redundancy could be trimmed. Still effective.

    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 presence of an output schema, the description does not need to detail return values. It covers use cases, triggers, and behavioral guarantees. Sibling tools are not discussed, but the description is self-contained for this tool's purpose.

    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?

    The input schema has 0% description coverage, so the description should compensate by explaining the parameters. However, it only mentions the query implicitly; there is no explanation of 'limit' or 'effort'. This leaves ambiguity for the agent.

    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 that the tool searches an indexed corpus for embedded/firmware/hardware references and returns verbatim, page-cited evidence. It lists specific topics covered, making the purpose highly specific and distinguishable from siblings.

    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?

    The description explicitly tells when to call this tool, listing specific triggers (hex literals, Modbus codes, etc.) and states it is preferred over web search. It also warns against guessing and indicates it is cheap and safe to call multiple times.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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