ocular

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have clear, distinct purposes (OCR, UI analysis, chart analysis, etc.), but analyze_image serves as a general fallback and could overlap with specialized tools like analyze_ui_screenshot or analyze_chart_image if an agent picks poorly. The descriptions are specific enough to guide selection in most cases.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case: analyze_image, extract_text_from_image, compare_ui_screenshots, etc. This consistency makes it easy to predict what a tool does based on its name.
Tool Count5/5
With 8 tools, the set is well-scoped for an image analysis server. Each tool serves a distinct purpose, and the count is neither too sparse nor overwhelming.
Completeness5/5
The tool set covers a broad range of image analysis needs for coding agents: general analysis, OCR, UI analysis, error screenshots, table extraction, chart analysis, and image comparison. The inclusion of an upload session tool addresses local file handling, filling the only potential gap.
Average 3.4/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 20 commits in the last 12 weeks
- Last stable release on August 12, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses only that the tool extracts visible table data into structured formats, but it omits important behavioral traits such as limitations on table complexity, return value structure, or how the tool handles corrupt/malformed images. The qualifier 'visible' hints at a constraint (ignoring hidden data), which is useful, but overall transparency is minimal.
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, front-loaded sentence with no fluff, which is syntactically concise. However, given the tool's five parameters and the need to disambiguate from siblings, the brevity leaves important gaps. Still, it earns a solid score for being clear and direct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With five parameters, no annotations, and no output schema, the description needs to carry substantial context. It only states the core extraction behavior and output formats, failing to cover the meaning of the 'task' parameter, the file upload workflow, return data shape, or any limitations. The tool is simple enough that minimal context might suffice, but the missing task semantics and lack of alternative guidance make it incomplete.
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 60%, so the description must compensate for undocumented parameters. The description mentions output formats, matching the output_format enum, but it does not explain the critical 'task' parameter at all, nor does it clarify the trade-offs between file_id and image_base64 beyond what is already in the schema. The free-form 'task' parameter remains entirely ambiguous.
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 ('Extract') and resource ('visible table data from an image'), clearly distinguishing this tool from siblings like extract_text_from_image (which extracts raw text) and analyze_chart_image (which analyzes chart data). It also names the three output formats, making the tool's purpose concrete.
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 gives no guidance on when to use this tool versus alternatives such as extract_text_from_image or analyze_chart_image. It does not state prerequisites (e.g., uploading the image first via create_upload_session) or explicitly exclude non-table images, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only restates the purpose and gives no information about output format, return values, or side effects. Even though 'analyze' implies a read-only operation, the absence of any safety or behavior details leaves the agent uninformed.
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 action and object. Every word earns its place with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description should provide more context about what the tool returns and how to invoke it. It offers only a one-line purpose and is far from complete for a tool with six parameters.
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?
The description adds no parameter information beyond the schema. Schema coverage is 50%, leaving 'task', 'page_hint', and 'framework_hint' entirely undocumented. The description fails to compensate for these gaps, especially the critical 'task' parameter.
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 analyzes UI/design mockup screenshots for frontend implementation work, using a specific verb and resource. It distinguishes from sibling tools by focusing on frontend implementation, though it could more explicitly contrast with generic 'analyze_image'.
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 frontend implementation but does not provide explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned, even though siblings like 'analyze_image' exist and could overlap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, and description does not state that the operation is read-only, nor any side effects or limitations beyond noting approximate values; the burden is on the description and it falls short.
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?
Single sentence, front-loaded with the action, no filler words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite a 5-parameter tool with no output schema or annotations, the description is just one vague sentence; it fails to explain the role of the task parameter, the two image input modes, or the relationship to sibling tools.
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 covers file_id, mime_type, and image_base64 with descriptions, but the task and chart_hint parameters are undocumented; description adds no parameter semantics beyond what 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 uses the specific verb 'analyze' with the resource 'chart image' and enumerates concrete outputs (labels, trends, approximate values, limitations), making it clearly distinct from generic image analysis or text extraction siblings.
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 choose this tool over siblings like analyze_image or extract_text_from_image; missing when-to-use or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only promises 'structured JSON', which conflicts with the output_format parameter allowing markdown or text, and it does not mention whether the operation is read-only, any auth needs, or how the image is supplied.
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 sentence, front-loaded with the core purpose, and no filler. It is an appropriate size for the limited information provided.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With five optional parameters, no output schema, no annotations, and a family of specialized sibling tools, the description is too thin. It leaves unclear when to prefer this tool over analyze_ui_screenshot or analyze_chart_image, what the JSON structure looks like, and how output_format changes returned data.
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%, and the parameter descriptions already explain file_id advantages, MIME handling, and output_format choices. The tool description adds no extra parameter semantics, so the baseline score of 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?
States 'Analyze a general image' with a specific verb and resource, and the word 'general' sets it apart from specialized siblings like extract_text_from_image or analyze_chart_image. However, it does not specify what kinds of analysis are performed or what the returned structured JSON contains.
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 'general image' implies broad use compared to specialized alternatives, providing implicit guidance. But there is no explicit when-to-use/when-not-to-use, no named alternative tools, and no mention of trade-offs like file_id versus image_base64 beyond the schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully disclose behavior; however, it only states that visual differences are returned without addressing side effects, limitations, output structure, or error conditions, leaving significant behavioral ambiguity.
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?
A single 11-word sentence with no filler. It is front-loaded and every word adds meaning, though it could be more explanatory without violating conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no output schema, and no annotations, the description is too sparse. It does not explain the return structure, expected input formats (beyond schema), or use cases, making it under-specified for an agent to use reliably.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 25% with task, page_hint, and framework_hint undescribed. The description itself adds no parameter semantics, and while the images property has a schema description, the tool description fails to explain the other three parameters.
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 'Compare' with the resource 'two UI screenshots' and specifies the output as 'implementation-useful visual differences,' clearly distinguishing it from sibling single-image analysis tools like analyze_ui_screenshot.
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 when comparing two screenshots but provides no explicit guidance on when to prefer this over analyze_ui_screenshot or diagnose_error_screenshot, and no exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action and omits important context like output format, layout preservation behavior, input method preferences, or limitations. This is insufficient for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence with no fluff. It efficiently communicates the core purpose and supported input types.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should explain expected outputs and behavioral nuances. It does not, leaving significant gaps for a tool with six parameters and two distinct input methods.
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 50%, and the description adds no parameter guidance. It doesn't explain language_hint, output_format, preserve_layout, or the decision between file_id and image_base64, which are key to correct usage.
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 ('Extract OCR text') and lists concrete input types (screenshots, documents, tables, terminal output, code images). It distinguishes itself from siblings like extract_table_from_image, which targets tabular data specifically, and analyze_image, which implies broader analysis.
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 use cases by enumerating various image types, but it does not explicitly state when to use this tool over alternatives. No mention of exclusions or sibling tools, such as directing table extraction to extract_table_from_image.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'Analyze,' which implies a read-only operation, but it does not describe return format, side effects, permissions, or limitations. This is minimal disclosure for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence that states the tool's purpose without any filler or repetition. It is front-loaded with the verb and resource, making it highly efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters and no output schema, the one-sentence description is insufficient. It does not explain what analysis is performed, how file_id relates to image_base64, or what the response contains. The description is complete for a simple tool but inadequate for this multi-parameter, no-output-schema tool.
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?
The description adds no meaning beyond the input schema. The schema documents 3 of 5 parameters (60%), leaving 'task' and 'project_context' entirely undocumented in both the description and schema. The description does not compensate for these gaps or clarify how the parameters relate to the analysis task.
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 'Analyze' with a specific resource: 'error, terminal, console, browser, or build failure screenshots.' This clearly scopes the tool and differentiates it from sibling tools like analyze_ui_screenshot or analyze_chart_image, which target different screenshot categories.
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 by enumerating the screenshot types this tool handles (error, terminal, console, browser, build failure), which effectively tells the agent when to use it. However, it does not explicitly state exclusions or name alternatives, so it stops short of a full 5.
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 the full burden. It discloses the stateless nature ('Stateless -- call once'), the expected HTTP interaction, and the requirement to verify the local file before upload. It also clarifies the return payload includes an upload_url and that the response yields a file_id.
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 under six sentences and front-loaded with the purpose. Each sentence adds distinct value: purpose, prerequisite verification, upload method, and statelessness.
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 one-parameter, no-output-schema tool, the description fully explains the workflow and what the caller will receive (upload_url, then file_id). It is self-contained for the intended local-file upload scenario.
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%; the single mime_type parameter already has a description and enum. The tool description adds no additional parameter semantics but also doesn't need to.
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 'returns upload instructions for the binary side channel' and explicitly positions it for analyzing local image files while avoiding base64 corruption. This distinguishes it from sibling vision-analysis tools, which analyze already-uploaded images.
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?
It provides explicit when-to-use guidance ('use this to analyze LOCAL image files') and detailed prerequisites and steps: verify the file with Bash, then PUT raw bytes with curl --data-binary (NOT base64), and pass the resulting file_id to any vision tool. It also mentions the tool is stateless and can be called once.
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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