Xano MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific resources and actions in the Xano ecosystem. For example, 'add-api' vs 'browse-apis' vs 'get-api-spec' handle different aspects of API management, and table-related tools similarly cover distinct operations like creation, listing, and schema editing.
Naming Consistency5/5
All tools follow a consistent verb-noun pattern with hyphen separation, such as 'add-api', 'browse-apis', 'create-api-group', and 'edit-table-schema'. This predictable naming convention makes it easy for agents to understand and select the appropriate tool based on the desired action and resource.
Tool Count5/5
With 9 tools, the server is well-scoped for managing APIs and tables in Xano. Each tool serves a clear purpose, covering essential operations like creation, listing, editing, and specification retrieval without unnecessary bloat or redundancy.
Completeness4/5
The tool set provides strong coverage for API and table management, including CRUD-like operations (e.g., create, list, edit). A minor gap exists in the lack of explicit delete or update tools for APIs and API groups, but agents can likely work around this using existing editing or creation tools as needed.
Average 2.9/5 across 9 of 9 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 this is a creation operation ('Add a new API'), implying mutation, but doesn't cover critical aspects like required permissions, whether the operation is idempotent, error conditions, or what happens on success (e.g., returns an API ID). For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 words. Every part of the sentence ('Add a new API to an API group') directly contributes to understanding the tool's function, with zero waste or redundancy.
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 complexity of a mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (e.g., an API ID or confirmation), error handling, or side effects. While the schema covers parameters well, the overall context for safe and effective use is lacking, especially for a tool that modifies system state.
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%, with all parameters well-documented in the schema itself (e.g., 'apigroup_id' as 'ID of the API group to add the API to'). The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting. However, it doesn't compensate for any gaps since there are none.
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 ('Add') and resource ('API to an API group'), making the purpose immediately understandable. It distinguishes from sibling tools like 'create-api-group' (which creates groups rather than adding APIs to them) and 'browse-apis' (which reads rather than creates). However, it doesn't specify what constitutes an 'API' in this context beyond the parameters listed in the schema.
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 (e.g., needing an existing API group), exclusions, or comparisons to siblings like 'create-api-group' for when no group exists. The agent must infer usage solely from the tool name and parameters.
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 this is a creation operation ('Add a new table'), implying it's a write/mutation tool, but doesn't mention permissions required, whether it's idempotent, what happens on duplicate names, or what the response contains. For a database mutation tool with zero 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 that states exactly what the tool does without unnecessary words. It's appropriately sized and front-loaded with the core functionality. Every word earns its place in this minimal but complete statement of purpose.
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 database table creation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after table creation, what permissions are needed, whether there are rate limits, or what the typical response format would be. The description alone doesn't provide enough context for an agent to use this tool effectively in production scenarios.
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 detailed documentation for all 3 parameters (name, description, schema). The description doesn't add any parameter information beyond what's already in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
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 ('Add') and resource ('new table to the Xano database'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'edit-table-schema' or 'create-api-group', which could involve similar database operations. The description is specific 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 'edit-table-schema' for modifying existing tables or 'list-tables' for viewing tables. There's no mention of prerequisites, constraints, or typical use cases, leaving the agent without contextual usage information.
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 the full burden of behavioral disclosure. It mentions browsing APIs but doesn't specify whether this is a read-only operation, if it requires authentication, what the output format looks like, or any rate limits. This leaves significant gaps for an agent to understand the tool's 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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, 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 complexity of 6 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain the return values, error conditions, or behavioral traits, leaving the agent with incomplete information to use the tool effectively.
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, so all parameters are documented in the schema. The description doesn't add any additional meaning beyond what's in the schema, such as explaining how pagination works or what the search term filters. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('browse') and resource ('APIs in a specific API group'), making the purpose evident. However, it doesn't distinguish this tool from sibling tools like 'list-api-groups' or 'get-api-spec', which could cause confusion about when to use each.
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 'list-api-groups' or 'get-api-spec'. The description only states what it does without context about prerequisites, exclusions, or comparisons to sibling tools.
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 the full burden of behavioral disclosure. It states the tool creates something, implying a write operation, but doesn't cover critical aspects like authentication requirements, rate limits, error handling, or whether the creation is irreversible. This leaves significant gaps for a mutation tool.
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 directly states the tool's purpose without any fluff or redundancy. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 mutation tool with no annotations and no output schema, the description is insufficient. It doesn't address behavioral traits like side effects, response format, or error conditions, leaving the agent with incomplete context to use the tool effectively in complex scenarios.
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 all 6 parameters thoroughly. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or default behaviors, but doesn't need to compensate for gaps, resulting in a baseline score.
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 ('Create') and resource ('new API group in the Xano workspace'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'add-api' or 'list-api-groups', which would require explicit comparison to achieve a perfect score.
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 'add-api' or 'browse-apis'. It lacks context about prerequisites, such as workspace permissions or whether this is for initial setup versus ongoing management, leaving the agent with minimal usage direction.
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 the tool edits schema but doesn't mention critical traits like whether it's destructive (e.g., data loss when removing columns), permission requirements, rate limits, or error handling. For a mutation tool with complex operations, this is a significant gap in transparency, scoring low due to missing essential 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 that front-loads the core purpose ('edit the schema of an existing table') and specifies operations in parentheses. There's zero waste or redundancy, making it highly concise and well-structured for quick comprehension.
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 (mutation with 6 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It lacks behavioral context (e.g., safety, permissions), usage guidelines, and output expectations. For a schema-editing tool that could impact data integrity, this minimal description fails to provide sufficient context for safe and 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 input schema fully documents all 6 parameters. The description adds no parameter-specific semantics beyond implying operations (add, remove, modify columns), which aligns with the 'operation' enum but doesn't provide additional syntax or format details. Baseline 3 is appropriate as the schema does the heavy lifting, and the description doesn't compensate with extra insights.
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 ('edit') and resource ('schema of an existing table'), specifying the operations (add, remove, or modify columns). It distinguishes from siblings like 'add-table' or 'get-table-schema' by focusing on schema modification rather than creation or retrieval. However, it doesn't explicitly differentiate from potential overlapping tools like 'update-table' if they exist, keeping it at 4 instead of 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. It doesn't mention prerequisites (e.g., table must exist), exclusions (e.g., cannot edit system tables), or compare with sibling tools like 'add-table' for new tables or 'get-table-schema' for viewing. This lack of context leaves the agent guessing about appropriate usage scenarios.
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. It states 'browse' which implies a read-only operation, but doesn't disclose behavioral traits like whether it requires authentication, rate limits, or what happens if the table doesn't exist. This is a significant gap for a tool with no 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, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration.
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 output looks like (e.g., structure of schema information), behavioral constraints, or how it fits with sibling tools. For a tool with 2 parameters and no structured safety hints, this leaves too many 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%, so the schema already documents both parameters thoroughly. The description doesn't add any meaning beyond what's in the schema, such as explaining the implications of choosing 'markdown' vs 'json' format. Baseline 3 is appropriate when 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 ('browse') and resource ('schema of a table'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'edit-table-schema' or 'get-api-spec', which might have overlapping functionality or context.
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. With siblings like 'edit-table-schema' and 'list-tables', the description lacks context on prerequisites, such as needing a table ID from 'list-tables', or when to choose this over other schema-related tools.
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 the full burden of behavioral disclosure. It only states 'browse all API groups' without mentioning pagination behavior, rate limits, authentication requirements, or what happens when no results are found. For a listing tool with 5 parameters, this leaves significant behavioral aspects unexplained.
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 states the core purpose without any wasted words. It's appropriately sized and front-loaded, 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?
For a listing tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how results are structured, or provide any context about the API groups being listed. The agent would need to guess about the response format and behavior.
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, providing clear documentation for all 5 parameters. The description doesn't add any meaningful parameter semantics beyond what's already in the schema, so it meets the baseline for high schema coverage without compensating value.
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 ('browse') and resource ('all API groups in the Xano workspace'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'browse-apis' or 'list-tables', which would be needed for a perfect score.
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 'browse-apis' or 'list-tables'. It lacks any context about prerequisites, when it's appropriate, or what distinguishes it from similar listing tools in the sibling set.
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 the full burden of behavioral disclosure. It mentions the conversion to 'minified markdown format', which adds some context about output behavior, but fails to address critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, or error handling. This leaves significant gaps for a tool that fetches and processes data.
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 action ('get and convert') without unnecessary words. Every part earns its place by specifying the resource and output format, 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.
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 (fetching and converting API specs), no annotations, and no output schema, the description is adequate but incomplete. It covers the basic purpose and output format but lacks details on behavioral traits, error cases, or return structure, which are needed for full contextual understanding in this 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 description coverage is 100%, so the schema fully documents both parameters (apigroup_id and format). The description adds minimal value by implying the format parameter's purpose ('convert... to a minified markdown format'), but doesn't provide additional semantics beyond what the schema already states (e.g., details on 'minified' or conversion process). 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.
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 with specific verbs ('get and convert') and resource ('Swagger specification for an API group'), distinguishing it from siblings like 'list-api-groups' or 'browse-apis'. However, it doesn't explicitly differentiate from potential similar tools like 'get-table-schema' in terms of resource type, keeping it at 4 instead of 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. It doesn't mention prerequisites (e.g., needing an existing API group), exclusions, or comparisons to siblings like 'browse-apis' or 'list-api-groups', leaving the agent with minimal context for selection.
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 but only states the action ('browse') without detailing behavior. It doesn't disclose if this is read-only, paginated, returns metadata or full data, requires permissions, or has rate limits—critical for a list operation in a workspace 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 that front-loads the core purpose ('browse all tables') without wasted words. It's appropriately sized for a simple tool with no parameters, making it easy to parse quickly.
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 with no annotations, no output schema, and siblings like 'get-table-schema', the description is incomplete. It doesn't explain what 'browse' entails (e.g., list names, metadata, or full schemas), return format, or how it fits into the broader table management workflow, leaving gaps for agent usage.
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 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description appropriately doesn't mention parameters, aligning with the schema. Baseline is 4 for zero parameters, as it avoids unnecessary detail.
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 ('browse') and resource ('all tables in the Xano workspace'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'browse-apis' or 'get-table-schema', which would require more specific language about scope or output format.
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, compare to siblings like 'browse-apis' for APIs versus tables, or specify use cases like initial exploration versus detailed schema inspection with 'get-table-schema'.
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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