envspeak

by Patrizzos

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools are clearly separated by purpose: manifest summarizes, impact previews, diff compares, and trace enumerates. The only real overlap is between resolve_variable and trace_variable, since both explain precedence and 'what wins,' though one targets the final value and the other targets all definition/read locations.
Naming Consistency3/5
resolve_variable and trace_variable follow a clean verb_noun pattern, but config_manifest and impact_preview are noun compounds, and diff_environments uses a terse verb. The names are readable and descriptive, but the naming convention is not uniform across the set.
Tool Count5/5
Five tools is well-scoped for a read-only environment/config analysis server. Each tool covers a distinct analytical need without redundancy or bloat, and the count feels intentional rather than thin or excessive.
Completeness5/5
The set covers the full analysis workflow: resolving a single variable, tracing all usages, generating a project-wide manifest, predicting change impact, and diffing environments. No obvious missing operation is needed for the stated domain.
Average 3.6/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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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}
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden. It adds one meaningful behavior (higher-precedence shielding) but does not disclose whether the tool is read-only, error behavior (e.g., variable not found), or what the output is (e.g., list of affected items). This is a clear gap, but the precedence detail provides partial transparency.
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 entire description is a single, tight sentence that front-loads the verb 'Predicts' and packs in the core purpose plus a behavioral nuance. There is no redundant phrasing or filler—every word earns its place.
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?
Despite the tool's moderate complexity (impact prediction across environments with precedence), the description omits critical context: it does not explain how parameters like 'files' and 'projectRoot' influence behavior, nor what the return value looks like. An agent would struggle to construct a correct call without additional inference.
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 coverage is only 40%, so the description must compensate for poorly documented parameters like 'files', 'variable', and 'projectRoot'. It only indirectly references 'file' and 'variable' and does not clarify the purpose of 'files' (plural) or why 'projectRoot' is needed. This fails to add meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Predicts') and identifies the exact resource (environments/services/workloads affected by a variable change). It also adds a distinguishing nuance about precedence shielding, which differentiates it from siblings like resolve_variable or trace_variable without naming them.
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 states what the tool does but gives no guidance on when to use it versus alternatives. It does not mention conditions, exclusions, or situations where another sibling tool would be more appropriate. This leaves the agent without direction for tool selection.
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?
With no annotations provided, the description carries the full burden. It discloses the tool's output nature (compressed summary, sources, risk hotspots) and hints at performance ('load once per session'), suggesting it is a read-only aggregation operation. However, it does not explicitly state whether it modifies state, whether it performs file I/O that might have side effects, or any rate limits. The description is informative but not fully transparent about behavioral boundaries.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that packs a lot of relevant information: purpose, content, and usage guidance. It front-loads the core purpose ('Compressed, project-wide summary') and then adds specifics. It is concise and well-structured, though it could be slightly less dense. No waste.
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?
Given the absence of annotations, output schema, and parameter descriptions, the description must provide extensive context. It explains what the tool returns and gives a usage hint, but it omits essential details: what files are expected (input semantics), how projectRoot and maxVariables affect behavior, what the output format is, and when to use this versus other siblings beyond resolve_variable. The description is insufficient for an agent to confidently invoke this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the input schema provides no explanations for the three parameters. The description does not mention any of them (files, projectRoot, maxVariables). An agent has no clue from the description what values to provide or how the parameters affect the tool's behavior. The description completely fails to compensate for the schema gap, making parameter semantics severely deficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the tool's purpose: producing a compressed, project-wide summary of config variables, including sources, domains, and risk hotspots. It explicitly distinguishes itself from sibling resolve_variable by positioning as a session-level alternative, making it unambiguous what the tool does and when it differs from at least one sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Load once per session instead of repeated resolve_variable calls.' This tells an agent when to prefer this tool over a specific sibling. However, it does not mention other siblings (trace_variable, impact_preview, diff_environments) or provide exclusions for when not to use this tool, so it's not fully comprehensive.
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 behavioral burden. It conveys that the operation resolves variables and reports differences, implying a read-only comparison. However, it does not disclose output format, possible failure modes, performance implications, or whether any resolution side effects occur.
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 one dense, purposeful sentence with no filler. It front-loads the core verb and resource, states the comparison scope, and communicates the output concept without redundancy.
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?
With five parameters, nested objects, no output schema, and no annotations, this description leaves important gaps: what 'files' represents, what values the selectors may contain, what a 'difference' report looks like, and when this tool is preferable to resolve_variable or trace_variable. The core idea is present, but an agent would struggle to call it correctly without guessing.
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 coverage is 60%; variables and the two environment objects have descriptions, and the description reinforces that all discovered variables are included by default. However, the required 'files' parameter and 'projectRoot' have no schema descriptions and the description does not clarify their meaning, leaving a critical input unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific action ('Resolves every variable') and a distinct resource ('two different environment/service/workload selectors'), then states the comparison outcome ('reports what differs'). This clearly separates it from siblings like resolve_variable, which handles a single variable, and trace_variable, which traces rather than diffs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'under two different environment/service/workload selectors' gives clear context for when to invoke this tool: when an agent needs to compare resolved variables across two configurations. It does not explicitly exclude alternatives or name sibling tools, but the intended scenario is apparent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden, and it does a solid job: it reveals that the tool is a read/analysis operation that returns locations and precedence outcomes. It does not discuss edge cases or failure modes, but it clearly communicates the core observable behavior and that it is non-mutating.
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 entire description is one dense, front-loaded sentence with no filler. Every clause contributes meaning: what is listed, where it is searched, and what result is produced.
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?
The core purpose is clear, but the definition is incomplete for reliable invocation: there is no output schema, no annotations, no guidance on files/projectRoot semantics, and no differentiation from sibling tools. An agent would likely need to inspect the schema or guess to use it correctly.
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 explain parameters. It clarifies that 'variable' is the trace target, but it gives no meaning for 'files' or 'projectRoot'—what files should contain, whether they are required, or how projectRoot changes discovery. This is a significant gap for a schema with no inline descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Lists'), names the resource (variable definitions/reads), and defines scope across dotenv, compose, k8s, and code. It also conveys a distinct deliverable ('what wins in each discovered environment/service/workload context'), which separates it from siblings like resolve_variable or diff_environments.
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 usage context is implied rather than explicit: an agent can infer this is the tool to use when all definition/read locations and precedence are needed. However, it never states when not to use it or names alternative tools, leaving the agent to reason from sibling names.
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 behavioral burden. It does disclose important behavior: resolution spans dotenv, docker-compose, and Kubernetes, and precedence is evaluated. However, it does not state side-effect freedom, error behavior, missing-file handling, or any operational prerequisites, leaving notable gaps for a tool with no annotation support.
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?
One concise, front-loaded sentence delivers the core function and the key differentiator (which source wins). There is no filler or redundant restatement of the tool name.
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?
Given the moderate complexity of 7 parameters, full schema coverage, and no output schema, the description is largely sufficient: it states what the tool returns conceptually (evaluated value and winning source) and all parameters are otherwise documented. It could be stronger by noting how to handle missing variables or by connecting to sibling tools, but the essential selection and invocation context is present.
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 every parameter. The description adds conceptual context by naming the source types, which loosely ties to files/service/workload/container, but it does not add parameter-level detail beyond what the schema provides, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses a specific verb ('resolves') and identifies the exact resource: what a config/env variable evaluates to across dotenv, docker-compose, and Kubernetes, including which source wins. This clearly differentiates it from siblings by focusing on multi-source resolution and precedence, not tracing, manifest inspection, impact, or diffing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a clear usage context: use when you need the actual evaluated value of a variable across multiple config-source types and the winning source. It does not explicitly name alternatives or state when not to use it, so it stops short of the strongest guidance.
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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