vision-memory-mcp

by putervision

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.7.21
Disambiguation2/5
Several tools have overlapping purposes: compare_states and get_visual_diff both return visual differences between two states; analyze_screenshot and recall_memory both accept screenshots and return state information; export_visual_trajectories and export_joint_trajectories both export trajectory data; save_visual_snapshot and export_snapshot both create snapshot archives. This creates ambiguity in tool selection.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., list_visual_specs, analyze_screenshot, recall_memory). The only outlier is 'app_version', which is a noun phrase rather than an action. Minor inconsistency.
Tool Count2/5
At 24 tools, the set feels bloated. Several tools could be consolidated (compare_states/get_visual_diff, export_visual_trajectories/export_joint_trajectories, save_visual_snapshot/export_snapshot), and batch_analyze_screenshots is just a convenience wrapper. The count exceeds the recommended range and doesn't justify itself.
Completeness3/5
The tool set covers core visual memory operations (ingest, search, compare, snapshot, export, forget, navigate), but has gaps: no direct way to list all visual states, no update/delete for visual specs, and create_visual_blocker depends on an external server (state-memory-mcp) for the actual logging, creating a dead end. Overall coverage is decent but not comprehensive.
Average 3.7/5 across 24 of 24 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 33 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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?
Annotations are all false, providing no behavioral safety profile, so the description carries the burden. It does not disclose side effects, whether this is a read or write operation, required permissions, rate limits, or any impact on state, leaving the agent without critical behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that is easy to read, but it is under-specified. It earns its place but does not provide enough substance for a batch tool with a nested schema, making it concise at the cost of informativeness.
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?
For a tool accepting up to 20 screenshot items with optional tags, accessibility trees, and multiple output modes, the description is too thin. It does not explain the return format, processing behavior, or expected use cases, and there is no output schema to clarify results.
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 only 33% (only 'items' has a description). The tool description adds no parameter details; it merely hints at 'screenshots or file paths' without mapping to the schema's properties like 'screenshot', 'file_path', 'response_format', or 'git_branch'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a resource (screenshots/file paths) and a batch scope, but the verb 'process' is vague and doesn't specify what processing entails (e.g., analysis, ingestion). It differentiates from the singular sibling 'analyze_screenshot' only through the word 'batch', not through a clear action.
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 given on when to use this batch tool versus the singular 'analyze_screenshot' or other sibling tools. It does not provide context on batching criteria, performance considerations, or when one approach is preferable.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds context about the content (visual transitions + workflow events correlated by session/trace ID) but does not detail output format or behavior limits, which is acceptable given the annotations.
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, dense sentence that conveys the core functionality without wasted words. It is concise but somewhat jargon-heavy, so it earns a 4 for being efficient yet slightly less accessible than a clearer two-sentence version.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description gives the essential purpose and scope. However, it lacks information about the returned data format and does not explicitly differentiate from the sibling export tool, leaving some gap in completeness for an agent to correctly invoke it in context.
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 coverage is 100% with clear descriptions for both 'limit' and 'trace_id'. The description contributes little beyond the schema, merely referencing session/trace correlation. Baseline 3 is appropriate since the schema carries the parameter documentation burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool exports 'unified interleaved visual state transitions and workflow graph events,' specifying the verb and resource. It hints at a combined scope but does not explicitly distinguish from the sibling 'export_visual_trajectories,' so it falls short of a 5.
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 provides no guidance on when to use this tool versus alternatives like 'export_visual_trajectories.' It describes what it does but not the context or conditions for choosing it, leaving the agent to infer usage.
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?
Annotations already indicate this is a non-read-only, non-idempotent, non-destructive operation. The description adds the source format ('exported archive JSON object') but does not disclose what happens to existing snapshots during restoration, leaving behavioral ambiguity about 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence that clearly communicates the core action and input. There is no filler, and the structure is front-loaded with the action verb.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description gives the essential action and input format. However, it omits any mention of return value, side effects, or relationship to existing snapshots, making it minimally complete but with clear gaps.
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?
The single parameter archive_json is fully described in the schema as 'JSON string of exported SnapshotArchive'. The description reiterates 'exported archive JSON object' but adds no additional semantic detail beyond what the schema provides, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Restore' with a clear resource 'visual memory snapshot' and source 'exported archive JSON object'. It distinguishes from siblings like save_visual_snapshot or export_snapshot by implying an inverse relationship, though it does not explicitly name alternatives.
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 alternatives like export_snapshot or save_visual_snapshot. The description only states what the tool does, not the context in which it should be chosen.
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?
Annotations already declare destructiveHint=true, so the agent knows it's destructive. The description adds context about the types of mutations that can be undone (state ingestion vs transition edge addition), which is useful. However, it doesn't disclose what happens to the underlying data after undo, or whether the operation is reversible (though idempotentHint=false implies it isn't idempotent).
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 a single, compact sentence with no wasted words. It front-loads the core action and resource, making it easy to parse. It could be expanded with usage guidance, but as written, it is appropriately concise for its content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description is adequate but leaves out important details like the default behavior when 'type' is not provided, what constitutes the 'last' mutation, and how this tool differs from similar siblings. Annotations cover the safety profile, and the schema covers parameters, but the missing usage context lowers the completeness.
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?
The schema covers the 'type' parameter with a description and enum values, reaching 100% coverage. The description aligns with the enum but adds no new meaning beyond what the schema already states. It doesn't explain the behavior when type is omitted, which would be valuable additional context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (undo) and the resource (last visual state ingestion or transition edge addition). It is specific enough to distinguish from most siblings, though it doesn't explicitly differentiate from similar tools like forget_state or restore_snapshot.
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 vs alternatives. The description doesn't mention any conditions, prerequisites, or limitations (e.g., what happens if no mutations have been made, or whether type is required). This is a significant gap given the number of sibling tools.
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?
Annotations indicate readOnlyHint=false and destructiveHint=false but don't explicitly describe side effects. The description adds useful context by mentioning that it generates a new memory entry if no cache match, disclosing a write behavior. It doesn't cover rates, permissions, or failure modes, but the caching disclosure is valuable.
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 a single, dense sentence that front-loads the main actions (ingest, check cache, return details) and includes the key side effect. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description conveys the core function but lacks important context for a tool with 10 optional parameters: it doesn't specify that a screenshot or file path is required in practice, nor what 'details of the state' entails. It also doesn't distinguish from sibling tools like batch_analyze_screenshots, leaving some ambiguity for an agent.
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 explains all 10 parameters. The description mentions 'screenshot or file path' which maps to screenshot and file_path parameters but adds no extra semantics beyond what the schema provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool ingests a screenshot or file path, checks the visual state cache, and returns state details. It mentions cache and memory generation, which differentiates it from siblings like save_visual_snapshot. However, the phrase 'details of the state' is somewhat vague and could be more explicit about the output.
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 provides no guidance on when to use this tool versus alternatives like batch_analyze_screenshots or save_visual_snapshot. There are no exclusions or mention of prerequisites, making it unclear how it fits among the many sibling tools.
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?
Annotations already declare the tool as read-only, idempotent, and non-destructive, so the description doesn't need to repeat that. It adds value by enumerating the specific metrics returned, but doesn't disclose other behavioral traits like time windows, aggregation scope, or output format. This is acceptable given the annotation coverage.
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 a single sentence with no filler words. It front-loads the action and immediately enumerates the queried metrics, earning every word.
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?
For a parameterless metrics-retrieval tool, the description is reasonably complete: it names all three metric categories returned. Without an output schema, it still conveys what the caller will receive, though it omits edge cases like empty results or units.
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 tool has zero parameters, so the schema fully covers parameter semantics (trivially). The description adds no parameter-specific information because there is nothing to add. Baseline for zero-parameter tools is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool queries specific metrics: cache-hit ratio, average visual similarity scores, and token-savings estimates. It uses a specific verb ('query') and resource ('metrics'), and though it doesn't explicitly distinguish from siblings, the content makes its purpose clear.
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 information is provided about when to use this tool versus alternatives. There are no usage scenarios, prerequisites, or exclusions, leaving the agent to infer appropriateness from the metric names alone.
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 annotations already indicating a non-read-only, non-idempotent mutation, the description adds that it updates transition statistics but does not elaborate on side effects like whether previous statistics are overwritten or if there are permission requirements.
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 sentence that front-loads the core function. No filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 8 parameters and no output schema. The description gives the gist but omits return value behavior and contextual prerequisites. Schema descriptions mitigate the param gap, but the overall completeness is average.
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 coverage is 88%, so parameters are mostly self-descriptive. The description does not add additional parameter context beyond what the schema provides.
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 a specific action (log a transition) and resource (between two visual states), and mentions it updates statistics. This distinguishes it from sibling tools like list_visual_specs or analyze_screenshot.
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 on when to use this tool vs alternatives. It does not mention prerequisites, context, or when logging an outcome is appropriate.
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?
Annotations already indicate non-readonly, non-idempotent, and non-destructive behavior, and the description does not contradict them. However, it adds no additional behavioral detail such as whether duplicate names overwrite or error, or what the response looks like.
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 a single, well-structured sentence that front-loads the action and resource, containing no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core purpose and the annotations cover safety aspects, but it lacks guidance on usage context, conflict behavior, and return values. This makes it adequate but incomplete for an agent to fully leverage the tool.
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%, with both 'name' and 'description' explained. The description itself adds no parameter-specific meaning, so the baseline of 3 applies.
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 ('Saves') and resource ('current visual memory states') to clearly define what the tool does. It distinguishes the action of creating a named checkpoint snapshot from related operations like export or restore.
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 alternatives. There is no mention of suitable contexts, exclusions, or related tools like export_snapshot or restore_snapshot.
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?
Annotations already indicate this is a mutating operation (readOnlyHint: false, idempotentHint: false, destructiveHint: false), and the description adds the context of setting a baseline. However, it does not disclose important behavioral details such as whether setting a spec overwrites an existing one, whether file_path and screenshot are mutually exclusive, or what happens after setting. With annotations present, the description provides some added value but not rich behavioral context.
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 a single, concise sentence that front-loads the main action ('Set') and contains no filler. Every word contributes meaning, and it is immediately scannable. This is an exemplary level of conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple setter tool with only three parameters and a complete parameter schema, the description covers the basic purpose but lacks additional context such as how the baseline is used, whether it replaces existing baselines, or what the expected outcome is (though no output schema exists). It is adequate but leaves some gaps for an agent to fully understand the tool's role in the larger workflow.
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?
The input schema has 100% description coverage for all three parameters (name, file_path, screenshot), so the baseline is 3. The description itself does not mention any parameters or add extra meaning beyond the schema, so it neither improves nor detracts from what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Set a screenshot or mockup design as a Visual Spec baseline') and its purpose ('for UI compliance testing'). It uses a specific verb and resource, making the tool's role understandable. However, it does not explicitly differentiate from sibling tools like 'save_visual_snapshot' or 'create_visual_blocker', so it stops short of a 5.
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 in the context of UI compliance testing, giving some context about when to use it. However, it does not provide explicit guidance on when to choose this tool over alternatives (e.g., 'save_visual_snapshot') or any exclusions. The 'for UI compliance testing' phrase is the only usage signal, making it borderline between implied and clear 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?
Annotations already declare readOnlyHint=true and idempotentHint=true, which describe the safety profile. The description adds no extra behavioral context (e.g., output delivery, rate limits, or side effects). It does not contradict annotations, but also does not enrich them.
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 a single, front-loaded sentence that directly states the tool's purpose. There is no wasted wording or redundancy, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an export tool with no output schema, the description does not clarify what the exported data looks like or how it will be delivered (e.g., a file path, download URL, or inline payload). It provides adequate context for the tool's purpose but lacks important details about the export result.
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 coverage is 100%, with all three parameters having descriptions in the schema itself. The description adds no additional meaning beyond what the schema already provides. The enum for 'format' is well-defined in 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 specifies a clear verb ('Export'), a concrete resource ('multimodal visual state transition trajectories'), and the intended purpose ('for local model fine-tuning'). This distinguishes it from sibling tools like export_joint_trajectories, which likely exports a different type of trajectory data.
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 phrase 'for local model fine-tuning' implies the primary use case, but there is no explicit guidance on when to use this tool versus alternatives such as export_joint_trajectories or export_snapshot. No exclusions or alternative tools are mentioned.
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?
Annotations already indicate the tool is read-only, idempotent, and non-destructive, so the description does not need to restate these. However, it adds minimal behavioral context beyond purpose—no mention of result ordering, pagination, or how the 'fast'/'semantic'/'thorough' strategies affect behavior. The description is accurate but does not enrich the annotation-provided safety profile.
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 a single, tightly worded sentence that immediately conveys the tool's purpose and primary input methods. There is no redundancy or filler; it earns its place by being clear and succinct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (8 optional parameters, no output schema), the description is functional but thin. It does not explain what a 'visual state memory' is, what the return payload looks like, or how to choose among strategies. The annotations and schema cover safety and parameter details, but the description leaves gaps around expected output and usage nuances.
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?
The input schema has 100% description coverage for all 8 parameters, so the baseline is 3. The description mentions three of the search modalities (screenshot, text query, accessibility tree), but does not add semantics beyond what the schema already provides for parameters like 'limit' or 'response_format'. It does not clarify how 'strategy' selection impacts results.
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's function: searching visual state memory using three distinct input modalities (screenshot image, text query, accessibility tree). This specific verb+resource combination distinguishes it from sibling tools like 'analyze_screenshot' or 'list_visual_specs'.
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 when to use the tool (when you need to search visual memory) but provides no explicit guidance on when not to use it or which sibling tool might be a better alternative. For instance, it doesn't mention that 'list_visual_specs' could be used for listing specs or that 'compare_states' might be better for comparing states.
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?
Annotations already convey read-only, idempotent, non-destructive behavior. The description adds that the comparison is both visual and structural, producing key-level JSON diffs, which clarifies the output format and the dual nature of the comparison.
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 a single sentence of 12 words, placing the verb and resource first. It is concise with no redundancy.
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 tool's simplicity (3 parameters, no nested objects) and annotations, the description is mostly sufficient. However, the absence of an output schema means the description should clarify the return format further, especially what 'visually' entails.
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?
Two parameters have basic descriptions in the schema, but the response_format parameter is not described beyond its enum. The description does not add meaning to the parameters, leaving the distinction between 'compact' and 'full' unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool compares two states, with the specific scope 'visually and structurally' and mentions key-level JSON diffs, which distinguishes it from generic 'diff' tools. However, it does not explicitly name sibling tools or differentiate itself from diff_visual_snapshots, so it stops short of full clarity.
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 the tool is for comparing two states, but provides no explicit guidance on when to use it vs alternatives like diff_visual_snapshots or get_visual_diff. There are no exclusions or alternative suggestions, leaving the agent to infer the appropriate context.
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?
Annotations already cover the safe read-only/idempotent nature, and the description adds valuable behavioral context about the output format (a standalone JSON archive with states, transitions, and metadata). However, it does not address error cases or size limitations.
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 a single, well-structured sentence that front-loads the verb and resource, with no filler or unnecessary detail.
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?
For a simple tool with one parameter and good annotations, the description explains the essential purpose and output content. It could additionally mention behavior for nonexistent snapshots, but the current description is sufficiently complete for a basic export operation.
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 coverage is 100%, including a clear description of the 'name' parameter. The description's use of 'named' adds no new meaning beyond the schema, so it does not go above the baseline for well-documented schemas.
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 action ('Export'), the resource ('a named visual snapshot'), and the resulting output ('a full standalone JSON archive containing states, transitions, and metadata'). This distinguishes it from sibling tools like diff_visual_snapshots and export_visual_trajectories.
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 explicit guidance is provided on when to use this tool versus alternatives. The description implies exporting for external use, but it does not mention when to prefer it over restore_snapshot or export_visual_trajectories.
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?
Annotations already communicate the read-only, idempotent, non-destructive nature. The description adds what the returned data contains (states and transitions) but provides no additional behavioral context such as result ordering, scale, or required preconditions, offering only marginal value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, starts with a verb, and is free of redundant wording. Every word contributes to understanding the tool's purpose.
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?
With no output schema, the description does specify the general content of the return (a list of recent/frequent states and transitions). However, it omits details about response_format values and whether the output structure is a flat list or grouped, leaving some ambiguity for a tool of this simplicity.
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 67%, leaving response_format undocumented. The tool description does not explain any parameter meanings or clarify the 'compact' vs 'full' distinction, so it adds no value beyond the existing schema descriptions for include_recent and include_frequent.
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 'Fetch' and names the resource 'aggregated visual context', also specifying what it includes ('recent/frequent states and active transitions'). This clearly distinguishes it from sibling tools like get_navigation_paths or list_visual_specs.
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 when to use the tool (when session context is needed) but offers no explicit guidance on when not to use it or which alternatives to choose. No mention is made of sibling tools like recall_memory or get_navigation_paths as alternatives.
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?
Annotations already declare the tool read-only, idempotent, and non-destructive, so the description need not repeat those. It adds value by revealing the calculation method (dHash diff and region deltas), which gives insight into the tool's behavioral output. It does not describe the return format, but given the strong annotation coverage, this is acceptable.
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 a single, well-structured sentence that starts with the verb and immediately specifies the tool's function. Every word contributes meaning, with no redundant phrases or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description is responsible for explaining what the result looks like. It mentions 'dHash diff and region deltas' but does not specify the return type or structure, which could leave an agent uncertain about how to use the output. Params are simple and annotations cover safety, but the missing output contract creates a clear gap.
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?
The input schema already provides clear descriptions for both parameters (baseline and target state IDs) with 100% coverage. The description only says 'between two visual states' without adding new meaning about how the parameters relate or what format they should take, so it does not elevate understanding 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 ('Calculate') and names the exact resource ('perceptual dHash diff and region deltas between two visual states'), making it clear what the tool does and distinguishing it from generic comparison tools. This directly differentiates it from siblings like compare_states or diff_visual_snapshots by specifying the algorithm and output type.
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 does not explicitly state when to use this tool over alternatives, nor does it list any exclusions. However, the specific mention of 'perceptual dHash' implies its niche, so the usage context is inferred rather than stated. There is no guidance about scenarios where this tool is preferred over similar siblings.
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well-covered. The description adds that the screenshot must be 'live captured' and the baseline 'registered,' but doesn't disclose behavior like what happens if the baseline is missing or how failures are reported. With annotations covering the main safety aspects, this is adequate.
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 a single sentence, front-loaded with the action and object. Every word earns its place with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters, no output schema, and simple annotations, the description provides the core purpose but not enough for a fully complete picture. It doesn't mention return values or expected outcome (e.g., pass/fail), which is significant for a verification tool. However, the tool's simplicity and schema coverage keep it at a mid-range score.
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% with each parameter already described. The description adds no additional parameter-level meaning, so the baseline of 3 applies.
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 ('Verify') with a clear object ('live captured UI screenshot') and resource ('registered Visual Spec baseline'). It distinctly differentiates from sibling tools like set_visual_spec (which creates baselines) and diff_visual_snapshots (which compares two snapshots).
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 clearly conveys the context of use: verification of a screenshot against a baseline. However, it does not explicitly mention alternatives or when not to use this tool, so it misses the top score but provides clear contextual 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?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is known. The description adds value by specifying the types of changes detected (additions, deletions, visual drift regressions), but does not mention prerequisites or output behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the action and result, with no redundant words. It earns its place by being compact and informative.
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?
With two fully described parameters, clear annotations, and a focused purpose, the description is largely sufficient. However, it does not describe the return format or any error conditions, which would be useful given no output schema exists.
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%, and the parameter descriptions ('Base snapshot name' and 'Target snapshot name to compare against') already clarify meaning. The tool description adds no additional parameter semantics 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 the specific verb 'Diff' with the direct object 'two snapshots' and states the outcome ('locate additions, deletions, or visual drift regressions'), clearly distinguishing it from sibling tools like 'save_visual_snapshot' or 'get_visual_diff' by its focus on snapshot comparison and drift detection.
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 comparing snapshots but does not explicitly state when to prefer this over alternatives like 'get_visual_diff' or 'compare_states'. There is no when-not guidance, so the agent must infer context from the snapshot-specific wording.
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds context about the 'historical' nature of the pathways and the flexibility of matching by description, but does not disclose performance characteristics, result limits, or potential edge cases (e.g., what happens if no path exists). Since annotations cover the primary safety concerns, a score of 3 is appropriate.
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 a single sentence, front-loaded with the key verb and resource, and contains no unnecessary words. It is efficiently structured and immediately understandable.
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 tool's moderate complexity, good annotations, and schema descriptions, the description is adequate for an agent to understand the tool's purpose and invoke it. It does not describe the return format (e.g., whether it returns a list of steps), but the tool name and description imply this, and the schema provides parameter details. The lack of an output schema is mitigated by the inherent clarity of the operation, so a score of 4 is warranted.
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 80% (4/5 params have descriptions), which exceeds the high-coverage threshold. The description adds minimal extra meaning beyond the schema: it reinforces that from_state_id corresponds to 'current state' and to_state_id/to_description correspond to 'target state.' This is helpful but not substantial enough to exceed the baseline of 3.
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's function: 'Trace historical pathways from current state to a target state or state matching description.' It uses a specific verb ('trace') and identifies the resource ('historical pathways' between UI states). This distinguishes it from siblings like compare_states (which compares two states) or get_session_context (which retrieves session context).
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 clear context for when to use the tool: finding a path from a current state to a target state, either by ID or description. However, it does not explicitly mention alternatives or exclusionary criteria (e.g., when to use predict_next_action or export_visual_trajectories instead). Thus it meets the 'clear context, no exclusions' level.
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?
Annotations already declare destructiveHint=true, and the description adds useful specifics by listing what gets purged (vector embeddings and perceptual hashes) and that it permanently removes from storage. This goes beyond the structural hints, though it does not mention irreversibility explicitly, which is implied by the annotation.
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 a single, tightly constructed sentence that front-loads the action ('Purge') and includes only essential details. Every word contributes value, with no fluff or repetition.
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?
For a simple destructive tool with one well-documented parameter and existing annotations (destructiveHint, etc.), the description is complete. It covers what action is taken, precisely what is affected, and the intended use case, making it sufficient for an agent to invoke correctly.
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 coverage is 100% for the single parameter 'state_id', which is already described as 'ID of visual state to purge'. The description does not add extra detail beyond calling it 'specific', so the schema carries the semantic weight, meriting the baseline score of 3.
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 ('Purge') and clearly identifies the resource ('a specific visual state, its vector embeddings, and perceptual hashes'). It also states the purpose ('privacy or memory reset'), which differentiates it from sibling tools like 'undo_last_visual_mutation' that imply reversible operations.
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 phrase 'for privacy or memory reset' implies a context for use, but the description does not explicitly say when to use this tool versus alternatives like 'undo_last_visual_mutation' or 'save_visual_snapshot'. There is no when-not-to-use guidance or naming of alternative tools.
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?
Annotations already declare the tool as read-only and idempotent. The description adds context that it only generates a payload and does not perform the actual logging, which is an important behavioral trait not captured by the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose, and every word earns its place. No fluff or redundancy.
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?
For a simple payload generator with no output schema, the description explains the tool's role and the follow-up step. It doesn't detail the payload structure, but the input schema plus the reference to add_node provide enough context for an agent to proceed.
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 all three parameters are already documented with descriptions. The tool description adds no parameter-specific semantics, falling at the baseline of 3 for high schema coverage.
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 ('generates') and resource ('structured visual blocker payload'), clearly distinguishing it from sibling tools that actually log, analyze, or modify visual states. It states exactly what the tool produces.
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 the output should be used to call state-memory-mcp:add_node, giving a clear usage context. However, it doesn't mention when not to use it or alternative approaches, so it's a 4 rather than a 5.
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?
Annotations already indicate the read-only, idempotent, and non-destructive nature. The description adds behavioral context by explaining the prediction is based on 'transition success rates and goal alignment,' which goes beyond the annotations and illustrates how the tool operates.
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 a single, front-loaded sentence that directly conveys the tool's function without any redundant or filler content.
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 simple parameter set, rich annotations, and clear schema, the description provides enough context to invoke correctly. It does not describe the return format, but the tool's name and purpose make that less critical, and no output schema exists to require it.
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?
The input schema has 100% coverage with descriptive comments for each parameter. The description reinforces the goal-related purpose but adds no additional parameter-level semantics beyond what the schema already provides.
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's purpose: predict the best next UI action based on the current visual state, transition success rates, and goal alignment. It distinguishes itself from sibling tools like analyze_screenshot (analysis) and get_navigation_paths (path enumeration).
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 clear context for use: when you have a current visual state and need to decide the next action, optionally guided by a goal. It does not explicitly list alternatives or when not to use it, but the context is sufficient.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety. The description adds crucial behavior beyond annotations: polling with a timeout, which is not inferred from the annotations. No contradictions found.
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 a single, front-loaded sentence that states the core behavior and adds a motivational hint. Every word earns its place, and it is not padded with redundant information.
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?
With annotations and a fully described schema, the description covers the essential waiting behavior. It does not specify the return value or timeout error behavior, but this is a simple polling tool and the missing details are not critical for selection.
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 parameters are already well-documented. The description does not add meaning beyond what the schema provides, such as explaining how default values interact or return behavior. Baseline score of 3 applies.
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's verb ('poll') and resource ('target visual state ID'), with an explicit terminal condition ('exists in memory or timeout occurs'). This distinguishes it from sibling tools like recall_memory or compare_states, which do different operations.
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 'avoiding spinning agent loops' gives clear context that this tool is intended to safely wait for a state to appear, instead of an agent manually polling. However, it does not explicitly name alternatives or exclusions, so it lacks full when-to-use vs 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful context about project-wide scope and hash details, but does not disclose other behavioral aspects like return format or potential limitations. This matches the expected level when annotations cover safety.
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 a single, front-loaded sentence that directly states the action and scope. Every word adds value, with no redundancy or filler.
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?
For a no-parameter, read-only listing tool, the description fully covers what the tool does and what output to expect (baselines and perceptual hash details). Annotations and empty schema complete the picture, so no additional context is needed.
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 schema has zero parameters, so parameter semantics are inherently covered. The 0-parameter baseline is 4, and the description gives sufficient context about what is being listed without needing parameter specifics.
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 ('List'), clearly identifies the resource ('all registered Visual Spec baselines across the project'), and adds a detail about output ('their perceptual hash details'). This distinguishes it from sibling tools like set_visual_spec or verify_visual_spec.
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 makes the tool's enumerating purpose obvious, implying when to use it (to get an overview of all baselines). However, it does not explicitly mention alternatives or exclusions, such as using get_visual_diff for comparing specific specs.
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing the safe read-only nature. The description adds specific return fields (version, package name, MCP identifier, server information), which is valuable behavioral context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler. It front-loads the specific information returned and is immediately comprehensible.
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?
For a zero-parameter, read-only info tool with no output schema, the description fully covers the tool's behavior. It names all returned data categories and is complete within its scope.
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 tool has no parameters, and schema coverage is 100% vacuously. Per the baseline for 0-param tools, the description need not explain parameters, and it doesn't introduce any confusion.
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+resource: 'Get version, package name, MCP identifier, and server information of vision-memory-mcp.' It clearly distinguishes this tool from siblings by focusing on the MCP server's own metadata rather than visual or memory operations.
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 clear context that this is a diagnostic/metadata tool for the MCP server itself. While it doesn't explicitly mention alternatives or exclusions, the usage is self-evident given the tool's purpose and zero parameters.
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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