BrianKnows MCP Server
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: 'agent' is for chatting with an AI agent, 'ping' is for health checking the API server, and 'search' is for querying a knowledge engine. The descriptions make it easy to differentiate these functions, eliminating any risk of misselection.
Naming Consistency3/5The naming is mixed in style: 'agent' and 'ping' are simple verbs, while 'search' is a verb that could fit a pattern, but there is no consistent verb_noun structure or uniform convention. However, the names are readable and straightforward, avoiding chaotic or confusing formats.
Tool Count3/5With only 3 tools, the count feels thin for a server named 'BrianKnows MCP Server', which suggests a broader knowledge or interaction domain. While the tools cover basic operations (chat, health check, search), it may lack depth for more complex workflows, making it borderline in scope.
Completeness2/5The tool surface has significant gaps for a knowledge-oriented server: there is no way to create, update, or manage knowledge entries, and the 'agent' tool lacks clarity on its capabilities beyond chatting. This incompleteness could lead to agent failures when trying to perform comprehensive tasks in the domain.
Average 3/5 across 3 of 3 tools scored. Lowest: 2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Chat with Brian agent' suggests an interactive conversation, but it doesn't disclose whether this is a read-only operation, what authentication might be required, rate limits, or what kind of responses to expect. The description provides minimal 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.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise with just three words, this is under-specification rather than effective conciseness. The description doesn't earn its place by providing meaningful information - it's too brief to be helpful. A single sentence with actual content would be more appropriate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters, no annotations, and no output schema, the description is completely inadequate. It doesn't explain what the tool returns, what 'Brian agent' refers to, or how this differs from standard chat interactions. The description fails to compensate for the lack of structured metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage, so all parameters are documented in the structured schema. The description adds no additional parameter semantics beyond what's already in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Chat with Brian agent' is a tautology that essentially restates the tool name 'agent' without specifying what the tool actually does. It doesn't distinguish this tool from its siblings (ping, search) and provides no specific verb+resource combination that clarifies the tool's function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does 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 its siblings (ping, search) or any alternatives. There's no mention of appropriate contexts, prerequisites, or exclusions for using this tool.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Search using Brian's knowledge engine' implies a read-only operation but doesn't specify what kind of results to expect, whether there are rate limits, authentication requirements, or any constraints on the search. The description is too minimal to provide adequate behavioral context for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just one sentence containing 5 words. It's front-loaded with the core functionality and wastes no words. This is an appropriate level of conciseness for a simple search tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what kind of results to expect, the format of returns, whether there are limitations on query complexity, or how results are ranked. The minimal description leaves too many questions unanswered for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters (query and kb) with descriptions and enum values for kb. The description doesn't add any additional meaning beyond what's in the schema - it doesn't explain what 'Brian's knowledge engine' means in relation to the parameters or provide usage examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a search operation using a specific knowledge engine (Brian's knowledge engine), which provides a specific verb+resource combination. However, it doesn't distinguish this tool from its siblings (agent, ping) since they appear to be unrelated tools rather than alternative search methods.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. There's no mention of when this search tool is appropriate versus other search methods, nor any context about when not to use it. The sibling tools appear unrelated, so no explicit comparison is made.
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 full burden. It discloses the behavioral trait of checking server liveness, which is useful context, but does not mention response format, timeout behavior, or error handling. For a zero-parameter tool with no annotations, this is adequate but leaves gaps in operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function with zero waste. It is front-loaded and appropriately sized for a simple tool, making it easy to understand without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is complete enough for its purpose. It clearly defines what the tool does, though it could benefit from slight elaboration on expected outcomes or usage scenarios to enhance agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters and 100% schema description coverage, the baseline is 4. The description does not need to add parameter semantics, as there are no parameters to document, and it appropriately focuses on the tool's purpose without redundant information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Check if') and target resource ('Brian API server is alive'), distinguishing it from siblings like 'agent' and 'search' by focusing on health/availability testing rather than data operations. It uses precise language that conveys the exact function without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (verifying server status), but does not explicitly state when to use this tool versus alternatives or provide exclusions. It suggests a diagnostic purpose, which gives clear context, but lacks explicit guidance on scenarios like pre-operation checks or troubleshooting steps.
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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