barrowman-associates-timeseries-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only a single tool, there is no possibility of confusion between tool purposes. The analyze_series tool has a clear, specific mandate to detect and explain changepoints.
Naming Consistency5/5The sole tool follows a clear verb_noun convention (analyze_series). No conflicting naming styles exist since there is only one tool.
Tool Count2/5A single tool feels too few for a server branded as a timeseries MCP. The broader domain typically requires multiple operations, making the tool count significantly under-scoped.
Completeness2/5The server offers only changepoint analysis and lacks supporting operations such as data retrieval, transformation, or lifecycle management. This creates significant gaps for workflows that require more than the single analysis.
Average 3.8/5 across 1 of 1 tools scored.
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
- Behavior4/5
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. It explains the input format (points as sorted [timestamp, value] pairs) and the output shape (anomaly report with per-changepoint audit trail), and hints at budget constraints affecting the analysis. This goes beyond a mere restatement of the tool name, though it does not discuss side effects like budget deduction or error behavior.
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 compact and front-loaded with the core purpose, followed by a necessary input format detail and a clear statement of the return value. Every sentence adds useful information, and there is no filler or repetition of the tool name.
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, lack of annotations, and absent output schema, the description covers the main function, the points format, and the return type, but it leaves important contextual gaps unexplained: what 'spend budget' means in concrete terms, how severity is determined, and the exact structure of the anomaly report. It is viable but not fully complete for an agent making correct invocation decisions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate for undocumented parameters. It explicitly explains the 'points' parameter and loosely ties 'metric' to 'fleet metric', but it provides no real semantics for 'tenant_id' or 'month', leaving two parameters underdocumented. This partial compensation is not sufficient for a four-parameter tool with no inline schema descriptions.
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 uses a specific action phrase 'Detect changepoints in a fleet metric' and details what makes this tool distinct: explaining the most severe changepoints subject to a spend budget. It names the resource (fleet metric) and the expected output (anomaly report with audit trail), so an agent can understand the tool's core 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 Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for changepoint detection within a cost-aware budget context, but it does not explicitly state when to choose this tool over alternatives or mention when not to use it. With no sibling tools listed, some of this pressure is relieved, but clear usage context such as prerequisites or complementary workflows is missing.
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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