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MCP Power - Knowledge Search Server

by wspotter
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one lists available datasets, and the other searches within them. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with the 'knowledge.' prefix: 'listDatasets' and 'search'. The naming is uniform and predictable, using camelCase consistently throughout the set.

    Tool Count2/5

    With only two tools, the server feels thin for a 'Knowledge Search Server' that might imply more operations like dataset management (e.g., create, update, delete) or advanced search options. The count is too low for the apparent scope, limiting functionality.

    Completeness2/5

    The toolset is severely incomplete for knowledge search operations. It lacks basic CRUD operations for datasets (e.g., create, update, delete) and other essential functions like document retrieval or metadata handling, which are likely needed in this domain.

  • Average 3.2/5 across 2 of 2 tools scored.

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

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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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the search method ('semantic similarity') but doesn't cover important aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what happens with invalid dataset IDs. The description is minimal and lacks behavioral context.

    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 - a single sentence that directly states the tool's purpose. There's zero wasted language, and it's front-loaded with the core functionality. Every word earns its place in this minimal description.

    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?

    For a search tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (document matches, scores, metadata), how results are ranked, error conditions, or provide any context about the knowledge dataset system. The description leaves too many questions unanswered for effective tool use.

    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 100%, so the schema already documents all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain parameter interactions, provide examples, or clarify concepts like 'semantic similarity' in relation to the query parameter.

    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 clearly states the tool's purpose: 'Search a knowledge dataset for relevant documents using semantic similarity'. It specifies the verb ('search'), resource ('knowledge dataset'), and method ('semantic similarity'), but doesn't explicitly differentiate from its sibling tool 'knowledge.listDatasets' beyond their different functions.

    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 use this tool versus alternatives. It doesn't mention the sibling tool 'knowledge.listDatasets' or any other search methods, nor does it specify prerequisites like needing a registered dataset ID before searching.

    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 carries full burden. It mentions the tool lists datasets but doesn't disclose behavioral traits like whether this is a read-only operation, if there are rate limits, what format the results come in, or if authentication is required. The description is minimal and lacks essential operational context.

    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, well-structured sentence that clearly states the tool's purpose without any wasted words. It's front-loaded with the core action and resource, making it highly efficient and easy to understand.

    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?

    Given the tool has 0 parameters and no output schema, the description adequately covers the basic purpose. However, without annotations or output schema, it lacks details on behavior, return format, or error handling. For a simple listing tool, this is minimally viable but leaves gaps in operational understanding.

    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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, making it efficient. Baseline for 0 parameters is 4, as it avoids unnecessary detail.

    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 clearly states the tool's purpose with a specific verb ('List') and resource ('knowledge datasets'), and specifies scope ('all registered' and 'available for searching'). However, it doesn't explicitly differentiate from its sibling tool 'knowledge.search' beyond implying this is a listing rather than searching operation.

    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 context through 'available for searching,' suggesting this tool should be used to discover datasets before performing searches. However, it doesn't provide explicit guidance on when to use this versus 'knowledge.search' or any prerequisites or exclusions.

    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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