Cellar
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool has a distinct role: cel_act for executing actions, cel_perceive for continuous perception, cel_see for reading screen state, and cel_think for cognitive planning. Descriptions clarify boundaries despite some perceptual overlap.
Naming Consistency5/5All tool names follow the consistent pattern 'cel_verb' (act, perceive, see, think), using lowercase with underscores throughout. No deviations or mixed conventions.
Tool Count4/5With only 4 tools, the set is compact but each encapsulates many sub-operations via parameters and modes. The count is slightly low but appropriate for the server's focused domain of screen automation and perception.
Completeness4/5The tools cover perception, action, and cognitive planning comprehensively for UI automation. Minor gaps exist (e.g., no explicit system-level operations), but core workflows are well supported and no obvious dead ends.
Average 4.1/5 across 4 of 4 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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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?
The description details many capabilities but fails to disclose what happens on invocation without arguments. No annotations are provided to clarify behavior.
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?
The description is excessively long and poorly structured, lacking front-loading. It lists many sub-functions without clear organization.
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?
Given the complexity of the described features and the lack of parameters or output schema, the description is incomplete for an agent to know how to effectively use the tool.
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 input schema has no parameters, so schema-description coverage is 100%. No parameter semantics are needed, baseline score of 3 applies.
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 lists many sub-operations but does not state what the tool does when invoked with no parameters. The purpose is vague and ambiguous.
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 includes efficiency guidance preferring cel_see+cel_act in some cases, but does not clarify how to invoke any of the listed sub-operations since the tool takes no parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes each action type, mentions deterministic spreadsheet actions, batching with default delay, and warns about stale-state cascading failures. Side effects (UI mutation) are implied, and no contradictions exist.
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 lengthy but well-structured with bullet points and sections. It front-loads purpose and general guidance. Some redundancy exists (e.g., repeating 'prefer'), but overall it is organized and earn its detail for the variety of actions.
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?
No output schema is provided, and the description does not explain what the tool returns. Additionally, the input schema is empty, creating a mismatch with the description that implies parameters. The missing return value and schema inconsistency reduce completeness.
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?
Input schema has zero parameters, so baseline is 4 per instructions. The description adds substantial meaning by detailing all action types and their required coordinates, target_ref, element_id, etc., far beyond the empty schema.
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 that the tool executes actions on the screen including mouse clicks, keyboard input, accessibility actions, drag & drop, and direct value setting. It also distinguishes itself from siblings by advising to use cel_see first, making its purpose distinct and specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Extensive guidelines are provided: always use cel_see first, prefer set_value over type for form filling, prefer ax_action over click for buttons/checkboxes, and detailed recommendations for each action type. Batching and re-observing instructions are also given, offering clear when-to-use and when-not-to-use guidance.
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 are present, so the description carries full burden. It discloses that the tool is always-on, maintains a background mental model, uses event streams, accessibility refreshes, and optional screenshots. It explains each mode's behavior and side effects (e.g., feed waits for screen settle, diffs model). Minor ambiguity about whether feedback modifies state, but overall highly transparent.
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 somewhat lengthy but well-organized with a clear mode list and important constraints upfront. Every sentence adds information, though some details could be tightened. Front-loading the singleton note is effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, no output schema, and no annotations, the description covers all essential information: purpose, modes, constraints, sibling differentiation, and behavioral model. It is fully adequate for an agent to select and invoke the tool correctly.
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?
The input schema has zero parameters (100% documented by schema), so baseline is 4. The description adds value by explaining the modes which act as sub-operations, but no parameter details are needed.
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 tool is an 'always-on perception engine' that maintains a mental model, and explicitly lists all modes (start, read, feed, etc.) with specific verbs and resources. It effectively distinguishes from siblings like cel_see by noting that 'watch' mode is unavailable during active session.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use each mode, including when not to use certain modes (e.g., 'cel_see watch mode is unavailable during an active session'). It also highlights the singleton constraint, aiding the agent in choosing this tool appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description provides rich behavioral details: default timeout for wait_for_element (10s), requirement for wait_for_idle (2 consecutive stable polls), 18 event types for watch, CDP limits (50 text_blocks, etc.), and conflict note about cel_perceive. This goes far beyond simple annotations.
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 well-organized with clear sections (Screen Context, Element Inspection, Browser, Observation Recall, Waiting & Watching, Limits). Each sentence adds value, providing necessary detail without redundancy. Front-loaded with purpose.
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 no output schema, the description lists categories of returned data but does not fully specify output structure. However, it covers key aspects like limits and sub-function behaviors. It feels complete for a read tool, though a more structured output spec would be even better.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters in the input schema, and the description compensates by thoroughly explaining all the tool's sub-functions (Screen Context, Element Inspection, etc.). According to guidelines, 0 params = baseline 4; this description exceeds that with detailed breakdown of capabilities.
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?
Description explicitly states 'Read and observe the current screen state' and lists many capabilities. It distinguishes from siblings by saying 'Always use this BEFORE acting', making clear this is the observation tool while cel_act is for actions and cel_perceive for perception.
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 gives clear usage advice: 'Always use this BEFORE acting'. It also notes a limitation (watch unavailable during cel_perceive session). However, it does not explicitly state when not to use or provide direct comparison with cel_perceive.
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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