jensenify-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have completely distinct purposes: one provides literary wisdom for engineering decisions, while the other tracks compute spending progress. There is no overlap or ambiguity between them.
Naming Consistency3/5The naming is mixed: 'consult_the_canon' follows a verb_noun pattern, but 'jensen_status' uses a proper noun prefix with a noun. While readable, they lack a consistent convention across the set.
Tool Count2/5With only 2 tools, the server feels thin for its apparent scope of combining literary consultation with compute tracking. This limited set may not fully cover the domain's potential workflows or user needs.
Completeness2/5The server's domain appears to blend engineering wisdom and compute management, but there are significant gaps: no tools for applying wisdom, adjusting compute settings, or integrating the two concepts. The surface is incomplete for meaningful agent interaction.
Average 3.3/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed 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
This repository is licensed under MIT License.
This repository includes a README.md file.
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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 'real-time progress bar' and 'personalized recommendations' which give some behavioral context, but doesn't address critical aspects like whether this tool makes changes to any system, requires authentication, has rate limits, or what happens when invoked. The description is too vague about actual behavior beyond surface-level output.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences that each serve a purpose: stating the tool's function, describing its outputs, and providing motivational context. It's front-loaded with the core functionality. The motivational quote at the end could be considered slightly extraneous but doesn't significantly detract from the overall efficiency.
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 no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the output looks like (beyond mentioning 'progress bar' and 'recommendations'), doesn't address error conditions, and provides minimal behavioral context. The motivational statement doesn't add functional completeness. Given the lack of structured data, the description should do more heavy lifting.
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 fully documents both parameters. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain how these parameters affect the progress calculation or recommendations. The baseline of 3 is appropriate when the schema does all the parameter documentation work.
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's purpose: tracking progress toward a specific compute spend goal ($250k/year) with a progress bar and recommendations. It uses specific verbs ('track', 'displays') and identifies the resource (compute utilization). However, it doesn't explicitly differentiate from the sibling tool 'consult_the_canon', which prevents a perfect score.
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. It mentions 'personalized recommendations' but doesn't specify what triggers those recommendations or when this tool should be used instead of the sibling tool. There's no mention of prerequisites, frequency of use, or exclusion criteria.
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 mentions that the tool 'loads the complete text of all canonical works in your current spending tier,' hinting at tier-based access, which is useful behavioral context. However, it lacks details on rate limits, authentication needs, or what happens if the tier doesn't include certain works. The description doesn't contradict any annotations since none exist.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by operational details and a recommendation. Every sentence adds value, including the motivational quote, without unnecessary fluff. It's efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (consulting literature for engineering insights) and lack of annotations or output schema, the description is somewhat complete but has gaps. It explains the purpose and usage well but doesn't detail the return format, error handling, or how 'relevant wisdom' is determined. For a tool without structured output, more context on results would be helpful.
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 ('question' and 'include_full_texts') well. The description doesn't add specific parameter semantics beyond what's in the schema, such as examples or deeper context. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't need to heavily.
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's purpose: 'Consult the great works of Western literature for engineering wisdom' and 'returns relevant wisdom from each.' It specifies the resource (Western literature) and the action (consult for wisdom). However, it doesn't explicitly differentiate from the sibling tool 'jensen_status,' which could be related but isn't described here.
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 provides clear usage context: 'Recommended before every technical decision' and includes a supporting quote about reducing bugs. This gives strong guidance on when to use it. However, it doesn't mention when NOT to use it or explicitly compare it to the sibling tool 'jensen_status,' which might offer alternative advice.
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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