CSL-Core

Server Quality Checklist

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Latest release: v0.4.0
Disambiguation5/5
Each tool targets a distinct activity on CSL policies: generating a scaffold, explaining in Markdown, simulating against inputs, verifying with Z3, verifying with TLA+, and analyzing the state space. The descriptions clearly differentiate them, especially verify_policy vs tla_verify by specifying different verification scopes (logical consistency vs temporal safety).
Naming Consistency4/5
Most tools follow a verb_noun pattern (explain_policy, scaffold_policy, simulate_policy, verify_policy), but tla_verify and universe_info deviate: tla_verify uses a proper noun prefix, and universe_info is noun_noun. This minor inconsistency prevents a perfect score.
Tool Count5/5
With 6 tools, the server is well-scoped for a CSL policy toolkit. It covers creation, explanation, simulation, logical verification, temporal verification, and state-space analysis without being over- or under-populated.
Completeness5/5
The tool surface covers the essential policy lifecycle: generate (scaffold), understand (explain, universe_info), test (simulate), verify (verify_policy, tla_verify). No obvious missing functionality like editing or compilation, as verification already includes IR compilation.
Average 4.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 3 of 3 community issues answered or closed in the last 6 months
- 5 commits in the last 12 weeks
- Last stable release on April 5, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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 carries full burden. It explains the four-stage analysis and that it returns actionable errors, but does not disclose whether the tool is read-only, synchronous, or has any side effects. The description is adequate but not exhaustive.
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 concise, front-loading the primary purpose in the first sentence. The four-stage analysis is listed efficiently, and every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter, the presence of an output schema (implied by context), and the detailed stage breakdown, the description covers all necessary aspects for an agent to use the tool correctly. Return values are not required due to output schema.
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?
Schema coverage is 0%, so the description compensates well by specifying 'csl_content: The complete CSL policy source code as a string.' This adds meaningful context beyond the schema's type-only definition, though format details could be added.
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 states the tool verifies a CSL policy for logical consistency using Z3, a specific verb+resource combination. It outlines four stages and distinguishes the tool from siblings (explain, scaffold, simulate, tla_verify) by focusing on formal verification.
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?
No guidance is provided on when to use this tool versus siblings like explain_policy or simulate_policy. There is no mention of prerequisites, limitations, or alternatives, leaving the agent to infer usage context.
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, the description carries full burden. It explains the output (ready-to-edit .csl template) and non-destructive nature, but does not explicitly confirm idempotency or absence of side effects.
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?
Well-structured with front-loaded purpose, followed by output description, common patterns, variable types, and parameters. Slightly verbose but each section adds value.
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?
Covers purpose, parameters, output, and provides usage examples. Given complexity (3 params, no annotations, but output schema exists), the description is sufficiently complete for an AI agent.
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?
Schema coverage is 0%, so description compensates well. Provides examples and clarifies each parameter: domain_name and description get context, variables is described as 'optional comma-separated variable hints' with examples.
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 clearly states 'Generate a CSL policy scaffold from a description' with specific verb, resource, and scope. It distinguishes from siblings like explain_policy and verify_policy by emphasizing scaffold creation.
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?
Includes common CSL patterns and variable types but does not explicitly state when to use this tool over alternatives, such as for creating new policies versus modifying or verifying existing ones.
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; the description carries the full burden. It discloses the tool does not compile or verify and returns a Markdown summary, but omits behavioral traits like idempotency, side effects, or permissions. This is adequate but not comprehensive.
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 concise with two sentences plus an args section. It is front-loaded with the main action and includes necessary details without any fluff.
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 presence of an output schema, the description does not need to detail return values. It lists what the tool shows (domain, variables, constraints, config) and the parameter is well explained. Missing minor context like error handling, but overall complete.
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 only parameter, csl_content, is described as 'The complete CSL policy source code as a string,' which adds meaning beyond the schema's type and title. Since schema description coverage is 0%, the description effectively compensates.
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 states it parses a CSL policy and returns a structured Markdown summary. The verb 'parse' is specific and distinguishes it from sibling tools, especially by explicitly excluding compilation or verification.
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 explicitly says 'Does NOT compile or verify — use verify_policy for that,' providing clear guidance on when not to use and pointing to an alternative. However, it does not mention when to use other siblings like simulate_policy or scaffold_policy.
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?
Without annotations, the description discloses the compilation and runtime guard steps, the return format, and the non-blocking behavior of dry_run. It lacks details on error handling but is generally transparent about the tool's operation.
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 well-structured with a clear opening statement, a brief explanation of the process, and a bulleted list of arguments. Each sentence adds value, though some redundancy could be trimmed for further conciseness.
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 three parameters, no annotations, and an existing output schema (which may cover return details), the description provides sufficient context: the tool's purpose, batch support, dry run, and parameter definitions. It does not cover error scenarios but is complete for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by precisely explaining each parameter: csl_content as 'complete CSL policy source code', context_json as 'JSON object or array', and dry_run as 'evaluates but never blocks'. This adds significant 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 clearly states the verb 'simulate' and the resource 'CSL policy against JSON inputs', and specifies the output 'ALLOWED or BLOCKED with full violation details'. It effectively distinguishes from siblings like 'explain_policy' and 'verify_policy' by focusing on simulation and batch testing.
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 for testing policies before deployment and mentions shadow testing via dry_run, but does not explicitly state when to use this tool versus alternatives like verify_policy or explain_policy. No exclusions are given.
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?
No annotations exist, so description carries full burden. It discloses exhaustive state-space exploration, returns counterexamples, fix suggestions, and a mock option. However, it doesn't mention potential long runtime or resource consumption, which are important for a verification tool.
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 well-structured: one-liner, detailed explanation, return summary, usage guidance, then parameter details. It's slightly long but every sentence adds value. Could be condensed slightly, but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (formal verification) and that an output schema exists, the description covers purpose, usage, parameter details, return values, and contrasts with alternatives. No obvious gaps; it is self-contained enough for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description provides clear, meaningful semantics for all three parameters: csl_content (complete source code), timeout (TLC subprocess timeout), use_mock (fallback to Python BFS). This fully compensates for the missing schema 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 clearly states the tool performs TLA+ formal verification (TLC model checking) on a CSL policy, and contrasts it with Z3-based verification via verify_policy. The verb 'verifies' and resource 'CSL policy' are specific, differentiating it from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use this tool vs. verify_policy: 'Use verify_policy for quick Z3 consistency checks. Use tla_verify when you need exhaustive temporal verification.' No ambiguity.
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 full burden. It lists what the tool returns (variables, domains, constraints, etc.) and implies a read-only analysis. However, it does not explicitly state no side effects or potential costs, leaving a minor gap.
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 well-structured with a clear purpose statement, bullet-pointed outputs, usage context, and parameter definition. It is slightly lengthy but each part adds value, earning a high score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description adequately explains input semantics, high-level outputs, and when to use the tool. It covers prerequisites and implications for verifying CSL policies, providing a complete picture for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, but the description provides full semantic meaning for the sole parameter 'csl_content', stating it must be the complete CSL policy source code as a string.
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 states the tool analyzes the state space 'universe' of a CSL policy, which is a specific verb and resource. It distinguishes from siblings like 'explain_policy' and 'tla_verify' by focusing on structural analysis of the state space.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: for understanding the universe, planning experiments, and estimating verification cost before running 'tla_verify'. This provides clear guidance on usage context and alternatives.
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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