Payman AI Documentation MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
The tools have overlapping purposes that could cause confusion, particularly between get-documentation and search-documentation (both retrieve documentation content) and between get-sdk-help and solve-problem (both address integration issues). However, the descriptions provide some differentiation by specifying topics vs. search and SDK usage vs. general issues, which helps mitigate misselection.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with hyphens (e.g., get-code-examples, search-documentation). There are no deviations in naming conventions, making the set predictable and readable throughout.
Tool Count5/5
With 5 tools, the server is well-scoped for its purpose of providing PaymanAI documentation and integration support. Each tool appears to serve a distinct role in the workflow, and the count is neither too sparse nor excessive for the domain.
Completeness4/5
The tool surface covers key documentation and integration support needs, including code examples, documentation retrieval, SDK help, search, and problem-solving. A minor gap exists in lacking tools for updating or managing documentation (e.g., edit or feedback functions), but agents can likely work around this for the server's stated purpose.
Average 2.9/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'Get help' but doesn't describe what form this help takes (e.g., textual advice, code snippets, links), whether it requires authentication, any rate limits, or potential side effects. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.
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, clear sentence that efficiently states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool, though it could be more front-loaded with specific details about the type of help provided. There's no wasted text, making it concise.
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's complexity (2 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what the tool returns (e.g., help text, solutions, error codes), how to interpret results, or any behavioral nuances. For a tool that likely provides varied output based on input, more context is needed to guide effective use.
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 ('problem' and 'sdk') with descriptions and enum values. The description doesn't add any meaning beyond what the schema provides, such as examples of problem descriptions or guidance on SDK selection. However, with high schema coverage, a baseline score of 3 is appropriate as the description doesn't need to compensate.
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 states the tool 'Get help with common PaymanAI integration issues' which provides a general purpose (assistance with integration problems) but is vague about what specific help it provides. It doesn't specify whether it returns solutions, troubleshooting steps, or diagnostic information, nor does it clearly distinguish from sibling tools like 'get-sdk-help' or 'search-documentation' which might offer similar assistance.
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 'get-sdk-help' or 'search-documentation'. It mentions 'common PaymanAI integration issues' which implies a context but doesn't specify prerequisites, exclusions, or when other tools might be more appropriate. The agent must infer usage from the tool name and description 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states it 'gets' documentation, implying a read-only operation, but doesn't specify if this requires authentication, returns structured data or raw text, or has any rate limits. The description is minimal and lacks crucial operational 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 a single, clear sentence with no wasted words. It's appropriately sized for a simple tool and front-loads the core purpose effectively.
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 and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., documentation text, links, or structured data), nor does it address authentication needs or error handling. For a tool with one parameter but unknown behavioral traits, this leaves significant 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?
Schema description coverage is 100%, with the parameter 'topic' fully documented in the schema including its enum values. The description adds no additional parameter semantics beyond what the schema provides, such as explaining the topic categories or usage examples. Baseline 3 is appropriate given the schema does the heavy lifting.
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 verb 'Get' and resource 'PaymanAI documentation on a specific topic', making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'search-documentation' or 'get-code-examples', which likely serve related but different purposes.
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 'search-documentation' or 'get-code-examples'. It mentions retrieving documentation on a 'specific topic', but doesn't clarify if this is for known topics only (as indicated by the enum) versus broader searches.
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 states the tool 'Get help' but doesn't clarify what form this help takes (e.g., documentation snippets, troubleshooting advice, API references), whether it requires authentication, rate limits, or error handling. For a tool with no annotation coverage, this is a significant gap in 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, efficient sentence: 'Get help with Node.js or Python SDK usage'. It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple tool. Every part of the sentence earns its place by specifying the action and target.
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's complexity (2 required parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what kind of help is returned (e.g., text, links, code), how results are formatted, or any behavioral traits. Without annotations or output schema, the description should provide more context to guide the agent effectively, but it falls short.
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, with clear parameter descriptions in the schema itself (e.g., 'Which SDK you need help with' for 'sdk'). The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage tips. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the heavy lifting.
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 as 'Get help with Node.js or Python SDK usage', which is a specific verb ('Get help') with resources ('Node.js or Python SDK usage'). However, it doesn't explicitly differentiate from sibling tools like 'get-code-examples' or 'get-documentation', which might provide overlapping functionality. The purpose is clear but lacks sibling distinction.
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 'get-code-examples' or 'search-documentation'. It implies usage for SDK help but doesn't specify contexts, prerequisites, or exclusions. This leaves the agent without clear direction on tool selection among similar siblings.
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. The description only states what the tool does ('Search through PaymanAI documentation') without adding any behavioral context such as how results are returned (e.g., relevance ranking, pagination), what happens with no matches, or any limitations (e.g., search scope, rate limits). It lacks details beyond the basic action.
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, efficient sentence: 'Search through PaymanAI documentation'. It's front-loaded with the core action and resource, with zero wasted words. This is appropriately sized for a simple tool, making it easy to understand at a glance.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., search results format, error handling) or provide behavioral context. For a search tool with no structured output information, the description should compensate by detailing expected outcomes, but it only states the basic action without addressing these 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 input schema has 100% description coverage, with the single parameter 'query' documented as 'Search term'. The description doesn't add any meaning beyond this, such as examples of effective queries or search syntax. Since schema coverage is high (>80%), the baseline score of 3 is appropriate, as the schema already provides adequate parameter information.
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 as 'Search through PaymanAI documentation', which includes a specific verb ('Search') and resource ('PaymanAI documentation'). However, it doesn't distinguish this tool from its sibling tools like 'get-documentation' or 'solve-problem', which might have overlapping functionality. The purpose is clear but lacks sibling differentiation.
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. With sibling tools like 'get-documentation' (which might retrieve specific documentation) and 'solve-problem' (which might handle troubleshooting), there's no indication of when this search function is preferred. The description offers no context, exclusions, 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 states the tool retrieves code examples but doesn't describe behavioral traits like whether it returns formatted snippets, includes comments, handles errors, or has rate limits. For a tool with zero annotation coverage, this is a significant gap, as the agent lacks insight into how the tool behaves beyond its basic purpose.
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, efficient sentence that front-loads the core purpose without unnecessary details. Every word earns its place by specifying the tool's action, resources, and scope concisely, making it easy for an AI agent to parse quickly. There's no redundancy or wasted phrasing.
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 (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and parameters but lacks behavioral details and usage guidelines. Without annotations or an output schema, the agent must infer how results are returned and when to use the tool, leaving room for improvement in providing a more comprehensive 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 description adds minimal meaning beyond the input schema, which has 100% coverage. It mentions 'Node.js or Python' and 'PaymanAI integration,' hinting at the 'language' and 'feature' parameters, but doesn't elaborate on parameter semantics like valid feature types or example formats. With high schema coverage, the baseline is 3, as the schema already documents parameters well, and the description provides only marginal 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 tool's purpose: 'Get Node.js or Python code examples for PaymanAI integration.' It specifies the verb ('Get'), resource ('code examples'), and scope ('PaymanAI integration'), distinguishing it from sibling tools like 'get-documentation' or 'search-documentation' that likely handle broader documentation. However, it doesn't explicitly differentiate from 'get-sdk-help' or 'solve-problem', which might overlap in providing code assistance.
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. It doesn't mention prerequisites, such as needing specific PaymanAI features, or compare it to siblings like 'get-sdk-help' (which might offer SDK-specific examples) or 'solve-problem' (which could provide troubleshooting code). The context is implied but not explicit, leaving gaps for an AI 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.

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