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hailampy123

solid-knowledge-ai

by hailampy123
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools are distinct in purpose: search_kb returns semantic search results over sources, while ask provides a grounded, cited answer through an agent. There is some overlap in that both retrieve information from the same knowledge base, but the descriptions clearly differentiate a low-level search from a high-level Q&A interaction, making misselection unlikely.

    Naming Consistency4/5

    Both tool names use lowercase snake_case and are verb-based. 'search_kb' follows a verb_noun pattern while 'ask' is a bare verb, creating a minor inconsistency, but the pattern is still simple and predictable given the small set.

    Tool Count3/5

    With only two tools, the server feels thin but not unreasonable. Search and ask cover the core knowledge-access functions, though additional tools like listing sources or managing documents might be expected in a fuller knowledge-management server.

    Completeness4/5

    The tool surface covers the core domain of querying an ingested knowledge base: search_kb for retrieval and ask for synthesized answers. Minor gaps exist, such as no way to enumerate available sources or inspect document metadata, but these are not critical for the stated purpose.

  • Average 3.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 23 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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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 present, so the description carries the full burden of disclosing behavior. It reveals that the tool searches over ingested documents and accepts an optional source_type, but it does not disclose result limits, relevance behavior, authentication needs, or any side effects. For a read/search tool this is a notable but not severe gap.

    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 tight sentence followed by a compact optional-parameter note. It is front-loaded with the core action and resource, and every word adds information. No filler or redundancy.

    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?

    For a simple two-parameter search tool, the description covers the essential invocation surface, and an output schema likely documents return values. However, it omits any comparison with 'ask' and does not explain result behavior or limitations, leaving the agent to infer when this tool is appropriate.

    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 does add meaning to source_type by enumerating allowed values ('pdf|md|web'), but it leaves the main 'query' parameter semantically undefined beyond the schema's bare type declaration. The compensation is only partial.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states a specific action ('Semantic search') on a clear resource ('ingested documents') and lists the optional source_type filter. It does not explicitly name the sibling tool 'ask' as the alternative, so some differentiation is left to inference, but the operation is unmistakable.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to prefer search_kb over the sibling tool 'ask', nor any exclusions or prerequisites. Usage context is only implied by the phrase 'semantic search', which is not enough to route an agent reliably.

    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 must carry behavior disclosure; it does say outputs are grounded and cited, a meaningful trait. However, it does not mention limitations, confidence, citation format, or whether the agent can refuse/ask follow-ups, leaving the behavioral profile thin.

    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 short sentences, with the core instruction and return behavior front-loaded. No filler or redundant restatement of the schema.

    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 one-parameter tool with an output schema, the description covers the key behavior and return characteristic. It is mostly complete, though the lack of sibling differentiation and parameter detail keeps it from being fully self-sufficient.

    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 schema has a single required `question` string with no field description, and the description does not elaborate on expected question format, length, or scope. The parameter name is self-explanatory, but the description adds no semantic detail to compensate for 0% schema coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    States a specific action ('Ask') on a distinct resource ('self-reflective knowledge agent') and its output ('grounded, cited answer'). It does not explicitly compare itself to search_kb, but the resource and output type give enough differentiation for a general sense.

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

    Usage Guidelines2/5

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

    No explicit guidance on when to ask versus using search_kb, nor any exclusions or conditions. The name and description imply Q&A usage, but the agent is not told when to choose this tool over the sibling.

    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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