nteract/semiotic

by nteract

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: diagnoseConfig for configuration validation, getSchema for component schema retrieval, renderChart for SVG chart generation, reportIssue for GitHub issue creation, and suggestChart for chart type recommendations. There is no overlap or ambiguity in their functions.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern (e.g., diagnoseConfig, getSchema, renderChart, reportIssue, suggestChart), with all tools using camelCase. However, the pattern is slightly inconsistent as some tools use verb+noun (e.g., renderChart) while others use verb+noun combinations that are less standard (e.g., diagnoseConfig, reportIssue), but overall it remains readable and predictable.
Tool Count5/5
With 5 tools, this server is well-scoped for its purpose of chart configuration and rendering in Semiotic. Each tool serves a specific role in the workflow, from diagnosis and schema retrieval to rendering and issue reporting, making the count appropriate and efficient.
Completeness4/5
The tool surface covers the core chart lifecycle: diagnosis, schema exploration, rendering, issue reporting, and chart type suggestion. Minor gaps exist, such as the lack of tools for updating or deleting chart configurations, but these are not critical for the server's stated purpose, and agents can work around them effectively.
Average 3.8/5 across 5 of 5 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- 3 of 3 community issues answered or closed in the last 6 months
- 659 commits in the last 12 weeks
- Last stable release on August 20, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
This server has been verified by its author.
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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 full burden. It discloses that the tool returns 'SVG string or validation errors' and lists available components, which adds some behavioral context. However, it lacks critical details: whether this is a read-only operation, if it has side effects, performance characteristics, authentication needs, or rate limits. For a tool with no annotations, this is insufficient.
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 appropriately sized with two sentences: one stating the purpose and return values, another listing components. It's front-loaded with the core functionality. However, the component list is lengthy and could be summarized more efficiently (e.g., 'Available components include LineChart, BarChart, and 20+ others').
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 no annotations, no output schema, and a tool with 2 parameters (including a complex nested object 'props'), the description is incomplete. It doesn't explain the structure of 'props', provide example inputs, describe error handling, or detail the SVG output format. For a visualization tool with nested parameters, this leaves significant gaps for an AI 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 baseline is 3. The description adds minimal value beyond the schema: it lists specific component examples (e.g., 'AreaChart', 'BarChart') which slightly clarifies the 'component' parameter, but doesn't explain 'props' object structure or provide usage examples. It doesn't compensate for any gaps since coverage is already high.
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's purpose: 'Render a Semiotic chart to static SVG' with specific verb ('Render') and resource ('Semiotic chart'). It distinguishes from siblings by specifying chart rendering rather than configuration diagnosis, schema retrieval, issue reporting, or chart suggestion. However, it doesn't explicitly differentiate from 'suggestChart' which might be related.
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 context by listing available components, suggesting this tool is for chart visualization. However, it provides no explicit guidance on when to use this tool versus alternatives like 'suggestChart' or other siblings. No prerequisites, exclusions, or comparative context 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?
With no annotations provided, the description carries the full burden. It discloses that the tool returns 'ranked recommendations with example props,' which adds useful behavioral context about the output format. However, it lacks details on error handling, rate limits, authentication needs, or performance characteristics, leaving gaps for a tool with no 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 front-loaded with the core purpose, followed by parameter guidance and output information in just two sentences. Every sentence earns its place by conveying essential information without redundancy or fluff, making it highly efficient and well-structured.
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 no annotations and no output schema, the description is moderately complete. It covers the purpose, basic parameter usage, and output format ('ranked recommendations with example props'), but it lacks details on error cases, response structure, or advanced usage scenarios, which would be helpful for a tool with no structured output documentation.
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 fully documents both parameters (data and intent). The description adds minimal value by mentioning the data sample size ('1-5 sample objects') and the optional intent to narrow suggestions, but it does not provide additional syntax, format, or usage details 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 the specific action ('Recommend Semiotic chart types') and resource ('for a given data sample'), distinguishing it from sibling tools like renderChart (which renders) or getSchema (which retrieves schema). It specifies the exact verb and scope without being vague or tautological.
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 by mentioning 'Optionally pass intent to narrow suggestions,' but it does not explicitly state when to use this tool versus alternatives like renderChart or diagnoseConfig. There is no guidance on prerequisites, exclusions, or specific contexts, leaving usage somewhat ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it performs diagnosis (analysis without mutation), returns a human-readable diagnostic report (output format), and provides actionable fixes (practical guidance). However, it doesn't mention error handling, performance, or authentication needs, leaving some gaps.
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 front-loaded with the core purpose, followed by specific problem examples and output details in a single, efficient sentence. Every part adds value without redundancy, making it highly concise and well-structured.
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 complexity (diagnosing configurations with nested objects) and no output schema, the description adequately covers the purpose, behavior, and output format. However, it could be more complete by specifying error cases or limitations, though it compensates well with clear problem examples and actionable output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., examples of 'component' and 'props'), resulting in a baseline score of 3 where the schema does the heavy lifting.
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 specific action ('diagnose') and resource ('Semiotic chart configuration'), listing concrete problem types like 'empty data, bad dimensions, missing accessors, wrong data shape'. It distinguishes from siblings like 'renderChart' (visualization) and 'suggestChart' (recommendation) by focusing on validation and debugging.
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 a chart configuration might have issues, but doesn't explicitly state when to use this tool versus alternatives like 'getSchema' (for schema inspection) or 'reportIssue' (for reporting problems). It provides some context through the problem examples but lacks explicit guidance on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates a URL and returns it to the user, which implies a read-only operation that doesn't modify data. However, it doesn't mention authentication requirements, rate limits, or what happens if the GitHub repository is inaccessible. The description adds some context about what to include in the body for rendering bugs, but lacks comprehensive behavioral details.
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 perfectly concise and well-structured in two sentences. The first sentence states the core purpose and return value. The second sentence provides specific usage guidance for a common scenario. Every word earns its place with zero wasted text.
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 tool with 3 parameters, 100% schema coverage, and no output schema, the description is reasonably complete. It explains what the tool does, when to use it, and provides specific guidance for rendering bugs. The main gap is the lack of output information - while it mentions 'Returns a URL,' it doesn't describe the URL format or what happens if generation fails. Given the good schema coverage, this is mostly adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond the schema: it mentions that the body should include 'component name, props summary, and any diagnoseConfig output' for rendering bugs, which provides some semantic context for the body parameter. However, it doesn't add significant meaning beyond what's already in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate a GitHub issue URL for Semiotic bug reports or feature requests.' It specifies the exact action (generate URL) and resource (GitHub issue), and distinguishes it from sibling tools like diagnoseConfig or renderChart by focusing on issue reporting rather than diagnostics or rendering.
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 this tool: 'For Semiotic bug reports or feature requests.' It gives specific guidance for rendering bugs: 'include the component name, props summary, and any diagnoseConfig output in the body.' However, it doesn't explicitly state when NOT to use it or name alternatives among 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it returns prop schemas or lists components, and it specifies that components marked [renderable] are usable with 'renderChart'. However, it doesn't mention potential errors, rate limits, or authentication needs, leaving some behavioral aspects uncovered.
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 front-loaded with the core purpose, followed by usage instructions and a note about sibling tools. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
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 low complexity (1 optional parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and a key behavioral note about [renderable] components. However, it lacks details on output format or error handling, which could be useful for an agent, though not strictly required given the 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?
The schema description coverage is 100%, so the schema already documents the single parameter. The description adds minimal value beyond the schema by reiterating that the component parameter can be omitted to list all, but it doesn't provide additional syntax, format details, or examples beyond what's in the schema. This meets the baseline 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 clearly states the tool's purpose with specific verbs ('Return the prop schema', 'list all available components') and distinguishes it from siblings by mentioning that components marked [renderable] can be passed to 'renderChart'. It explicitly identifies the resource ('Semiotic chart component') and differentiates between two modes of operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: pass a component name to get its props, or omit it to list all components. It also distinguishes from alternatives by noting that [renderable] components can be used with 'renderChart', clarifying the relationship with a sibling tool.
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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