Woow n8n MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds value beyond the annotations (readOnlyHint, idempotentHint) by disclosing token estimates for each detail level (minimal ~200 tokens, standard ~1-2K, full ~3-8K) and stating that modes return different content (e.g., docs = markdown documentation). No contradiction with annotations is present, and the extra context helps an agent anticipate response size and content.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences, front-loading the main purpose and then listing details. Every sentence contributes useful information. The minor error ('format' vs 'mode') detracts slightly but the structure is efficient overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (9 parameters, multiple modes), the description covers the main modes and detail levels but omits explanations of version-related modes (versions, compare, breaking, migrations) and parameters like includeExamples, includeTypeInfo, maxPropertyResults, toVersion, fromVersion. The schema covers these, but the description could provide more high-level guidance for a complex tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and includes detailed parameter descriptions. The description adds token counts for detail levels and groups modes concisely, but also repeats information already in the schema. The confusing 'format' reference slightly reduces clarity. Overall, the description provides marginal extra value over the schema, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets node info with progressive detail levels and multiple modes, making the purpose well-defined. However, it introduces an inconsistency by saying 'Use format='docs'' when the parameter is actually named 'mode', which could confuse an agent. Still, the overall purpose is distinct from siblings like search_nodes or tools_documentation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description fails to provide explicit guidance on when to use this tool versus sibling tools (e.g., tools_documentation, search_nodes). It does not mention when-not to use it or suggest alternatives. The information on modes and detail levels helps with intra-tool choices but not tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds value beyond the annotations by explaining the mode parameter's effect on response size, which the annotations (readOnlyHint and idempotentHint) do not cover. It does not contradict the annotations; rather, it complements them. The behavior of controlling output detail is clearly described.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two short sentences that front-load the purpose and then explain the mode parameter. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 parameters, no output schema, no nested objects), the description is sufficiently complete. It covers the core retrieval purpose and the key parameter. However, it could optionally mention that the output is a workflow template JSON, but since mode 'full' implies 'complete workflow', it is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 extra meaning beyond the schema—it only summarizes the mode options briefly ('nodes_only (minimal), structure (nodes+connections), full (complete workflow)') but does not provide additional context about templateId beyond what the schema already states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'template by ID'. It succinctly distinguishes the tool from siblings like search_templates by specifying retrieval via ID, and it also describes the mode parameter that tailors the response size, which is a key differentiator from other retrieval tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit context for when to use this tool (when you have a template ID) and how to adjust the response size using mode. However, it does not explicitly state when not to use it or provide alternatives, though the sibling tool names (e.g., search_templates) imply that this is for direct ID lookup, not searching.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds behavioral context: max 20 results, default limit, behavior of includeExamples and includeOperations (token cost per result). This exceeds the burden given the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: three sentences that front-load the purpose and immediately provide actionable examples and constraints. Every sentence earns its place without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema and 6 parameters, the description is complete: it covers query usage, results limit, and the two boolean options. The context signals show high schema coverage and no nested objects, so the description needs to do little else. The sibling differentiation is implicit but sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so parameters are already well-documented. The description adds value by explaining the query parameter further (use quotes for exact phrase) and clarifying includeExamples and includeOperations behaviors beyond their schema descriptions. A small lift above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches n8n nodes by keyword with optional real-world examples. It distinguishes itself from siblings like search_templates or get_node by focusing on node search with template config examples, and from get_node by offering operations inclusion to save round-trips.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly guides usage via example queries and mentions the trade-off of includeOperations (saves a round-trip to get_node). However, it does not explicitly state when to use this tool over siblings like search_templates or get_node, nor when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the tool is safe and idempotent. The description adds behavioral clarity by detailing the five distinct search modes and their parameters (e.g., 'patterns' mode uses 2700+ templates for summaries), without contradicting annotations. The explanation of 'patterns' as lightweight summaries is extra context beyond annotations, but it lacks details on pagination behavior or result format, keeping it from a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently enumerates all five modes and their purposes without redundancy. Every clause adds value (e.g., 'lightweight workflow pattern summaries mined from 2700+ templates'), and it's front-loaded with the main action ('Search templates with multiple modes').

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 13 parameters, no output schema, and rich sibling context, the description is remarkably complete. It covers the core behavior (five search modes), when to use each, and hints at return differences (e.g., 'patterns' yields summaries). With readOnlyHint and idempotentHint annotations, no additional safety info is needed. The only minor gap is no explicit mention of result pagination, but the schema's offset/limit parameters suffice.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 parameters. The description adds value by grouping parameters by search mode (e.g., 'query' for keyword, 'nodeTypes' for by_nodes), providing context not in the schema. However, for parameters like 'category' and 'targetAudience', the description only restates what the schema says, offering no deeper semantics, so it doesn't fully reach a 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches templates with multiple modes, enumerates each mode with its specific use case (e.g., 'searchMode=keyword' for text search, 'by_nodes' to find templates using specific nodes), and distinguishes itself from siblings like search_nodes (which likely searches for nodes, not templates). The explicit listing of five search modes provides a precise scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use each mode (e.g., 'Use searchMode=keyword for text search, by_nodes to find templates using specific nodes'), which directly guides the AI agent in selecting the right approach. It implies that for template-related searches this tool is appropriate, while siblings like search_nodes are for node-level searches, providing clear separation of concerns.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows this is a safe, read-only operation. The description adds behavioral context beyond annotations: it explains that calling without parameters returns a 'quick start guide,' and that depth='full' provides 'comprehensive documentation.' This clarifies the different response modes. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each adding value: first sentence states the core purpose, second gives the default behavior, third explains the two optional parameters. It is front-loaded and contains no unnecessary words. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 optional params, no output schema), the description covers the main use cases well. It explains all invocation modes. However, it does not describe the format of the returned documentation (e.g., markdown, text), which would be helpful for an agent. Still, it is largely complete for a straightforward documentation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 mentions using the 'topic' parameter and 'depth="full"', but this mostly restates the schema's own descriptions. It adds minimal new meaning beyond what the schema already provides. Therefore, a score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 documentation for n8n MCP tools.' It distinguishes itself from siblings (which deal with templates, nodes, validation) by explicitly focusing on tool documentation. The different invocation modes (no params, topic, depth) are also outlined, making the purpose very specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: 'Call without parameters for quick start guide. Use topic parameter to get documentation for specific tools. Use depth="full" for comprehensive documentation.' This tells the agent exactly when to use each parameter combination. It does not mention when not to use the tool or alternatives, but the sibling tools are sufficiently different that no further exclusion is needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark it as readOnlyHint true and idempotentHint true, so the description has less burden. It adds mode parameters and example usage but does not detail return format or success/failure behavior, which is partially covered by the output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with a clear example, no fluff. Could be slightly more compact by integrating the example into the main statement, but it is efficient and front-loaded with purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (4 params, nested objects, output schema) and 100% schema coverage, the description covers the key behavior. The output schema handles return details, so no further explanation needed. The example adds practical context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description provides an example for nodeType and config that goes beyond the schema by showing a practical use case. This adds value, earning a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it validates n8n node configuration with a specific verb and resource. Provides examples of nodeType and config to distinguish it from sibling tools like 'search_nodes' or 'get_node'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly describes two validation modes ('full' for comprehensive, 'minimal' for quick checks) and gives usage example. Context suggests siblings like 'validate_workflow' exist but no direct 'when not to use' is stated, though mode distinction sufficiently guides selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint and idempotentHint, so the safety profile is clear. The description adds that it returns errors/warnings/fixes, which is useful but not extensive. Since annotations carry the behavioral burden, the description adds moderate value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose and scope. Every word adds value. No redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose and return type. An output schema exists, so return details are documented elsewhere. Slightly missing mention of the optional 'options' parameter, but the schema covers it. Overall adequate for a validation tool with rich structured data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed descriptions for each parameter. The description mentions the validation categories (structure, connections, expressions) which map to options, but does not add new meaning beyond what the schema already provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clear verb+resource: 'Full workflow validation' specifies the action and scope. Lists what is validated (structure, connections, expressions, AI tools) and what is returned (errors/warnings/fixes). Distinguishes from sibling 'validate_node' by being for the entire workflow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

States 'Essential before deploy' which implies when to use it, but does not explicitly mention when not to use it or alternatives like validate_node for single nodes. The context is clear but lacks exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.