Workato Dev MCP
Server Quality Checklist
Latest release: v1.2.0
- Disambiguation4/5
Most tools target distinct resource+action pairs, and descriptions clearly separate similar pairs like list_mcp_servers vs list_api_collections or query_lookup_table vs query_data_table. A few pairs (list_mcp_server_tools vs list_api_endpoints) could be confused at a glance, but the descriptions resolve the ambiguity.
Naming Consistency4/5The overwhelming majority follow a consistent verb_noun underscore pattern (get_, create_, list_, update_, delete_, etc.). Minor exceptions like 'whoami' and 'workato_recipe_tips' break the pattern but are still readable and memorable.
Tool Count1/5With 56 tools, this far exceeds the 50+ threshold defined as an extreme mismatch. Even for a broad platform covering recipes, MCP servers, API Platform, data tables, and RLCM, the sheer number creates a heavy cognitive load and suggests the server is over-scoped.
Completeness4/5The tool set provides excellent lifecycle coverage across multiple domains: full CRUD for recipes and MCP servers, data table record operations, API endpoint activation, export/import, linting, and scaffolding. Minor gaps exist (no API endpoint create/delete, no collection update/delete) but core workflows have no dead ends.
Average 4/5 across 56 of 56 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
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 does not mention side effects, permissions, whether the recipe is validated, or what happens on success (e.g., returns a recipe ID). It only gives a minimal statement of the action, which is not sufficient for a mutating tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short (two sentences) and packs useful parameter details, but the first sentence 'Create a recipe' is a tautology that wastes space. The second sentence is a run-on that could be better structured with clearer separation of parameter definitions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a create operation with no output schema and no annotations, this description is incomplete. It explains some parameter semantics but lacks information about return values, error conditions, prerequisites, or post-creation behavior. The tool has four required parameters, and one (name) is completely undocumented in the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds semantic meaning beyond the schema for three of the four required parameters: code is described as 'the code tree,' config as 'connection config array,' and folder_id as 'a string.' This helps the agent understand what values to provide. However, the required 'name' parameter is not mentioned at all, leaving a gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Create a recipe,' which is a clear verb+resource pair, but it essentially restates the tool name without adding extra context about what a recipe is or how this creation differs from other 'create' tools in the list (e.g., create_mcp_server, create_api_collection). The additional parameter hints (code, config, folder_id) provide some implicit scope but do not explicitly distinguish this tool from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives like update_recipe, copy_recipe, or scaffold_recipe. There is no mention of prerequisites, such as needing an existing folder, nor any indication of when creation is appropriate. The usage is implied only by the tool's name.
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, the description must carry the full burden of behavioral disclosure. It states 'List recent jobs' but fails to explain what 'recent' means, whether there is a default limit, pagination behavior, or any side effects. The return fields are listed but overall behavior is under-specified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the action, scope, and expected output fields with no redundant words. It earns its place exceptionally well.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no output schema, the description gives a reasonable overview and lists return fields. However, it lacks parameter explanations, default behavior details, and any usage guidance, leaving gaps that matter for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description implicitly references recipe_id via 'for a recipe', but does not explain the limit parameter or elaborate on how recipe_id is used. It provides minimal added meaning beyond the schema, and fails to compensate for the low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and resource ('jobs') with a clear scoping phrase ('for a recipe'), and lists the returned fields (id, status, started_at, title). This clearly distinguishes it from siblings like get_job or list_recipe_versions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 vs alternatives like get_job or list_recipe_versions. The description implies its use for listing recent jobs, but lacks explicit context, prerequisites, or exclusions.
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 full burden. It only states the basic action and does not disclose any side effects, prerequisites, error conditions, or whether the operation is destructive. This is a significant gap 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loaded with the action. It is appropriately sized for a simple tool, though slightly more detail could be useful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no annotations and no output schema, the description is minimal. It does not explain return values, error handling, or relationship to list_folders or other folder operations. Context is insufficient for robust agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% coverage, so description must compensate. It adds meaning to parent_id ('optionally under a parent folder id') but says little about 'name' beyond the obvious. Partially compensates for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a folder and optionally under a parent folder id, which is a specific verb+resource. This distinguishes it from sibling tools like create_mcp_server or create_recipe.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the name and description: to create a folder. However, no explicit when-to-use or alternatives are provided. Context signals show sibling creation tools, but the description gives no comparative guidance.
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?
The verb 'List' implies a read-only operation, and the description mentions optional filtering behavior. However, with no annotations, it does not explicitly disclose whether the tool is safe from side effects, nor does it describe pagination or access scoping.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that conveys the core purpose and optional filters without redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list tool with no output schema and no annotations, the description covers the basic listing action and filters but omits return format, pagination, and filter combination behavior. It is minimally viable but leaves safety-related gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description enumerates the three filters and provides type hints for `running` (bool) and `name_contains` (substring), which are absent from the schema (0% coverage). It adds some semantic meaning but leaves ambiguous how `folder_id` is matched and what `running=true` specifically filters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the operation (listing) and resource (recipes), and specifies optional filters. It distinguishes from `get_recipe` which fetches a single recipe, but does not explicitly differentiate from other list-type siblings like `list_recipe_versions`.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 such as `get_recipe` or `list_recipe_versions`. It does not mention preconditions, scoping, or typical use cases, leaving the agent to infer applicability.
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?
Annotations are absent, so the description carries the full burden of behavioral disclosure. It only states the operation and property format, but does not disclose whether properties are merged or replaced, whether the operation is idempotent, or any side effects or permissions required.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact—two sentences, no filler—and both sentences contribute useful information: the tool's purpose and the input format. It is appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 param, no output schema, no annotations), the description covers the basic purpose and parameter format. However, it omits behavioral details like upsert semantics (merge vs. replace) and any side effects, which creates slight invocation ambiguity but is acceptable for a minimal tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema merely defines properties_json as a string with no description. The description adds context by specifying it is a JSON object string in the format {name: value}, but stops short of providing examples or clarifying the exact structure of name/value pairs, leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with 'Create/update account properties', using a specific verb and resource. It also differentiates itself from the sibling read tool 'get_properties' by indicating a mutation operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided regarding when to use this tool versus alternatives. The description does not mention any prerequisites, exclusions, or context for when an upsert should be preferred over other property-related operations.
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?
No annotations are provided, so the description must carry the behavioral disclosure burden. It at least implies a read-only listing operation and specifies the returned fields, but it does not mention pagination, authorization requirements, or error behavior. This is minimal but acceptable for a simple no-parameter tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler. It front-loads the action and resource, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple no-parameter tool with no output schema, the description is reasonably complete: it states the operation and expected fields. However, it lacks explicit use-case context or exclusions relative to sibling tools, so it is adequate but not rich.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is empty, so there are no parameter semantics to explain. The baseline for 0 params is 4, and the description usefully adds the output field list (id, name, schema).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
Description uses a specific verb ('List') and resource ('lookup tables'), and it clearly states the output fields (id, name, schema). It does not explicitly distinguish this from sibling list_data_tables, but the tool name and field list make the purpose clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives like list_data_tables or query_lookup_table. It only states what the tool does, leaving the agent to infer appropriate usage context.
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, the description carries the full burden. 'List' clearly implies a read-only operation, and the description adds the return fields, which is helpful. However, it does not disclose potential behaviors like pagination (the limit parameter), ordering of versions, or error handling, leaving some transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded: it states the action, the resource, the key output fields, and the purpose. Every word contributes meaning, with no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool, the description covers the core function and output fields, but it omits parameter semantics, ordering behavior, and any pagination details. Without an output schema or annotations, the description is adequate but not fully complete, leaving some gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no descriptions for either parameter, and the description does not explain any parameters. Recipe_id is inferable from the tool name, but 'limit' is completely unexplained, and with 0% schema description coverage, the agent is left without guidance on how to use the limit parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') with a clear resource ('a recipe's version history') and explicitly names the return fields (version_no, author, comment). It distinguishes itself from sibling tools like get_recipe or get_recipe_steps by focusing solely on version history, and the mention of 'tracking/rollback awareness' adds contextual purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for tracking/rollback awareness, but it does not explicitly state when to use this tool versus alternatives or list any exclusions. There is no mention of alternative tools or specific conditions for use, so the guidance is implied rather than explicit.
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, the description carries full burden. It states 'Read rows' but does not clarify that 'id' refers to table_id, nor does it disclose behavior around the 'limit' parameter, return format, or error handling. The phrase 'by id' is ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence with no filler, front-loaded with the verb 'Read'. It efficiently conveys the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema or annotations, the description should explain return values and parameter effects, but it only partially covers the purpose. The missing explanation of 'limit' and the ambiguous 'by id' leave gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description only hints at 'id' without explicitly naming table_id. The 'limit' parameter is completely omitted, leaving its purpose and usage unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Read' with a clear resource 'lookup table' and the parameter 'id', distinguishing it from siblings like list_lookup_tables (lists tables) and add_lookup_table_row (adds rows).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a clear example use case ('to inspect captured query logs') that implies when to use the tool, but does not explicitly mention alternative tools or when not to use it. The context is clear but exclusions are absent.
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?
No annotations are provided, so the description carries the transparency burden. It discloses that start-time validation errors appear in the tool's response, which is useful behavioral context. However, it omits other important details like whether the action is idempotent, what prerequisites (e.g., recipe must exist or be stopped) exist, and what a successful start returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences that immediately state the action and then add a valuable behavioral note about validation errors. Every word earns its place; there is no fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no output schema, no annotations), the description covers the core purpose and a key behavior. However, it lacks details about success/failure return values, state prerequisites, and side effects, which would be needed for a fully unambiguous invocation. It is adequate but not exceptionally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one required parameter, recipe_id, with 0% description coverage. The tool description does not mention this parameter at all, leaving the schema to stand alone. Although the parameter name is somewhat self-explanatory, the description adds no additional meaning, such as how to find the recipe_id or any constraints on its value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Start (activate)') and clear resource ('a recipe'), making the tool's purpose unambiguous. It distinctively contrasts with sibling tools like stop_recipe and update_recipe by naming the activation action and adding the unique 'start-time validation errors' behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (to activate a recipe and to read validation errors), but it does not explicitly state when not to use it or mention alternatives such as stop_recipe or get_recipe. The usage context is only suggested through the phrase 'Start-time validation errors surface here,' without clear 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, the description carries full responsibility for behavioral disclosure. It does mention 'server-side' and 'preserves step `as` ids', which are useful details, but it does not disclose whether the original is changed, what permissions are needed, or what the output/return value is.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that states the core action and a key detail. Every word earns its place; there is no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (2 params, no output schema, no annotations), and the description gives the essential operation. However, it omits return value, error conditions, and any side-effect caveats, making it adequate but not fully complete for an agent to invoke without ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Parameter schema has 0% coverage, so the description must compensate. It does by clarifying that recipe_id identifies the recipe to copy and folder_id is the destination folder. However, it adds no further constraints or nuances about the parameters beyond what the schema types already indicate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'copy' with resource 'recipe' and destination 'folder', clearly distinguishing it from create_recipe, update_recipe, and delete_recipe. The parenthetical about preserving step `as` ids adds specific behavioral meaning, further clarifying its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: when you need a server-side duplicate of a recipe into a folder. However, it provides no explicit when-to-use guidance, exclusions, or alternatives, such as comparing with create_recipe or noting prerequisites.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose a key limitation: 'endpoints themselves are added in the UI — there is no create-endpoint Developer API.' This is valuable context. However, it doesn't mention what output/return value to expect, permissions, idempotency, or side effects beyond creation, so the burden is only partially met.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, with the main purpose front-loaded and a separate note for the limitation. Every word earns its place; no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple create tool with only two parameters and no output schema, the description is fairly complete: it defines the purpose, the required inputs, and an important non-obvious limitation about endpoint creation. However, it doesn't state what is returned (e.g., an ID) or what happens after creation, which would be helpful for an agent to chain subsequent calls.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description only repeats 'Needs name + project_id', which is already shown in the schema's required array. With 0% schema description coverage, the description should explain what project_id refers to or what name should be, but it does not add any semantic meaning beyond the parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Create an API collection' with a specific verb and resource, and adds context that it can later be exposed as a Workato MCP server. This distinguishes it from sibling tools like create_mcp_server (which creates the server itself) and list_api_collections (which lists existing collections).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Needs name + project_id', which implies these are required, and includes an explicit note that endpoints are added in the UI, not via a Developer API. This tells the agent when not to use this tool (to create endpoints) and implies the intended use case is collection creation only. It doesn't explicitly name alternatives like create_mcp_server, but the MCP server reference suggests a workflow.
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 full burden. It discloses that include_code=true returns the parsed code tree + config, which is useful behavioral context. However, it does not note error behavior, permissions, or what the default response is when include_code is false or omitted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with the core action. The include_code clarification adds value without redundancy. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool, the description covers the primary action and key optional behavior. However, it lacks explanation of the default return shape and error conditions, which would be important since there's no output schema to clarify responses.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must add parameter meaning. It documents the effect of include_code=true, but not the false case or recipe_id semantics. The parameter names are somewhat self-explanatory, but the description only partially compensates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Fetch a recipe' — a specific verb and resource. It distinguishes from siblings like list_recipes (which lists) and get_recipe_steps (which retrieves steps), and the include_code behavior further differentiates it as the source of truth for editing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving a single recipe and include_code=true for accessing the code tree, but it doesn't explicitly contrast with alternatives like list_recipes for summaries or get_recipe_steps for steps-only. No exclusion criteria are given.
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, the description carries the burden for behavior. It discloses the privilege requirement and that the result is 'full details' including mcp_url. However, it does not explain what other fields constitute 'full details', nor address error cases or return format, which is a moderate gap for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose, and every word contributes meaning. No filler or repetition of schema/annotation data.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter get tool, the description covers what it does, the input key, a key part of the output, and a needed privilege. It is not exhaustive (e.g., no full return-field list), but completeness is adequate given the low complexity and absence of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains that 'handle' is the lookup key, which adds some meaning over the bare schema. Yet it does not define what a handle is or specify any format constraints, providing only minimal added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and identifies the resource ('one AI Hub MCP server's full details') along with a scope ('by handle'). It also highlights a unique output detail ('incl. mcp_url') and clearly distinguishes this single-item fetch from siblings like list_mcp_servers or get_mcp_server_policies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states a prerequisite ('Needs the MCP privilege') but does not explicitly contrast with alternatives such as list_mcp_servers for enumerating servers. The use case (fetching one server by handle) is implied, but no explicit 'when to use' or 'when not to use' guidance is provided.
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, the description carries the full burden for behavioral disclosure. It only provides the verb 'List' and a definition of the resource; it does not disclose return format, pagination, ordering, permission requirements, or any potential side effects. This is a significant gap for a tool with no 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the verb and resource, and uses a dash to add a concise explanation. Every word contributes to understanding the tool's purpose, with no wasted content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no parameters and no output schema, the description is mostly complete. It states what is listed and clarifies the resource type. However, it does not mention the output shape or any permission/scope constraints, which would be helpful but are not critical given the low complexity. Overall, it is adequate and useful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description adds contextual meaning by explaining what an API Platform collection is, but there are no parameter details to provide. This is appropriate for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List API Platform collections' with a specific verb and resource. The appended clause 'each is an API product that can be exposed as a Workato MCP server' distinguishes this from the sibling list_mcp_servers, which lists MCP servers themselves. This makes the purpose unambiguous and well-differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies its usage (listing API Platform collections) but does not explicitly state when to prefer it over alternatives like list_mcp_servers or create_api_collection. There is no mention of exclusions or when-not-to-use. Thus it provides clear context but no explicit guidance.
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, the description must carry the transparency burden. It states the return fields (id, name) and implies a read-only operation, but does not disclose pagination behavior, authentication requirements, or whether all accessible tables are returned. This is minimal but credible for a simple list operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that omits all unnecessary words. It front-loads the action and resource, making it highly scannable and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no parameters and no output schema, the description is nearly complete. It explicitly mentions the return content, but lacks contextual details like the scope of data tables listed (e.g., workspace-wide vs. user-specific) and any pagination or limit information. Still, the simplicity of the tool keeps this gap modest.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the input schema is empty with 100% coverage. Since there are no parameters to explain, the description needs to add nothing, and the baseline for 0 params is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') with a clear resource ('Workato Data Tables') and even specifies the output fields ('id, name'). This distinguishes it from sibling list tools like list_recipes and list_connections, making the purpose immediately clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 such as query_data_table or other list tools. There is no mention of use cases, prerequisites, or explicit exclusions, leaving the agent to infer from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states the read-only nature (listing) and the optional parent filtering, which is helpful context. But it omits details about pagination, sorting, recursive behavior, error conditions, or response format, leaving some behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the action and resource, with no redundant or filler words. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list operation with one optional parameter and no output schema, the description provides enough information to invoke the tool correctly in most cases. It could be improved with response details, but the low complexity makes the tool relatively complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter (parent_id) with 0% description coverage. The description compensates by mentioning the parameter and its optionality, but it does not explain the expected value type beyond the schema's 'string' or any constraints like whether it is a numeric ID or path. This is adequate for a single optional parameter but not particularly rich.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb+resource pattern: 'List folders, optionally under a parent folder id.' It clearly distinguishes this tool from siblings like list_folder_assets and create_folder by specifying the exact resource being listed and the optional parent scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for listing folders, optionally filtered by parent_id, which provides clear context for using it. However, it does not explicitly mention when to prefer this tool over alternatives or any exclusions, so the guidance is implied rather than explicit.
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 burden of disclosing behavior. It does mention the output fields (id, name, root folder), which is useful, but it does not explicitly state read-only behavior, pagination, or scope (e.g., all projects vs. accessible ones). For a simple list tool, this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, immediately front-loaded with the action and resource. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (no params, no output schema), the description is reasonably complete. It tells the user exactly what fields will be returned. Minor gaps like pagination or authentication are not addressed, but they are less critical for a straightforward list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is fully covered. The baseline for 0 params is 4, and the description correctly indicates the output structure without needing to explain any inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List'), the resource ('Workato projects'), and the returned fields ('id, name, root folder'). This is specific and distinguishes it from sibling tools like list_folders or list_recipes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 merely states what it does, without context or exclusions. Users are left to infer that this is the tool for listing projects.
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?
Discloses the critical irreversible nature and permanency, which is essential given there are no annotations. It does not detail potential side effects on dependent resources or API-level authorization, but the core destructive behavior is clearly conveyed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded, every word contributes. The warning about irreversibility is concise and impactful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter delete operation, the description covers the key context: irreversible action and need for user confirmation. It could mention whether a running recipe can be deleted or return behavior, but these are not essential given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage and the tool description never mentions recipe_id. While the parameter name is self-explanatory, the description adds no semantic value beyond the schema, which itself provides no guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the verb (delete), resource (recipe), and the permanency of the action. This distinguishes it from sibling tools like start_recipe, stop_recipe, and copy_recipe.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a clear usage prerequisite: confirm with the user first. However, it does not explicitly contrast with alternatives like stop_recipe or update_recipe, nor does it describe scenarios where deletion is inappropriate.
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 clearly states that the endpoint is deactivated, MCP clients can no longer call it, and the operation mutates a live MCP server's tool surface, plus the caution to confirm with the user. No annotation contradiction exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the purpose and effect, and contains no wasted words. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple mutation tool with no annotations, no output schema, and a single obvious parameter, the description adequately covers purpose, effect, and the user-confirmation requirement. It could mention re-enabling or the result, but the description is sufficiently complete given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not mention api_endpoint_id at all. Since the description must compensate for missing schema documentation but fails to do so, it adds no meaning beyond what the parameter name itself implies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Deactivate' and identifies the resource as an 'API-Platform endpoint,' clearly stating that it turns a tool OFF so MCP clients can no longer call it. This distinguishes it from the sibling tool enable_api_endpoint, which would perform the opposite action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for use by noting that it mutates a live MCP server's tool surface and instructs to confirm with the user first. However, it does not explicitly mention alternative tools (e.g., enable_api_endpoint for reactivation), so it lacks explicit exclusions or named alternatives.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It notes the output is 'compact' and limited to selected fields, offering some behavioral context. But it does not describe error handling, prerequisites, or side effects, though this appears to be a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no redundant words. The parenthetical field list is dense and useful, earning its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with one parameter and no output schema, the description covers the purpose and return fields reasonably well. It lacks explicit error behavior or output tree structure details, but the given information is sufficient for a basic mental model.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes a single required integer 'recipe_id' with no description, and schema description coverage is 0%. The tool description adds no parameter semantics, relying entirely on the self-explanatory parameter name. With low schema coverage, the description should compensate but does not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description uses a specific verb 'Return' and names the resource as a 'compact step tree' for a recipe, explicitly listing output fields (keyword/provider/name/as). This clearly distinguishes it from sibling tool get_recipe by focusing on structure inspection rather than full recipe details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for quick structure inspection' signals the appropriate context for use. However, it does not explicitly mention alternatives or exclusions, such as when to use get_recipe instead, though sibling tools are present.
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, the description carries the burden of behavioral disclosure. It states what the tool shows (method, path, active flag, backing recipe), which is helpful for a list operation. However, it does not explicitly mention read-only behavior, permissions, or potential errors, leaving some behavioral aspects implicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with two sentences that front-load the core purpose and output fields, followed by a practical prerequisite. Every word adds value, and it avoids unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no output schema), and the description explains the output fields (method, path, active flag, backing recipe) enough to set expectations. It also includes ordering guidance. It could be more complete by clarifying the meaning of 'active flag' or error behavior, but for a basic list tool, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There is only one parameter and schema description coverage is 0%, so the description must compensate. It does not directly explain 'api_collection_id' beyond implying it identifies a collection via 'collection's' and the prerequisite to list collections first. This is minimal compensation for a parameter that is essential to the tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action ('List'), the resource ('an API collection's endpoints'), and the purpose ('the TOOLS an API-Platform-backed Workato MCP server exposes'). It also distinguishes from sibling tools by specifying the fields shown (method, path, active flag, backing recipe) and referencing the prerequisite 'list_api_collections'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance by instructing 'Run list_api_collections first,' which clarifies the required ordering and indirectly indicates when this tool is appropriate. It does not mention exclusions or alternative tools, but the sequencing guidance is clear and useful.
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?
No annotations are provided, so the description carries the full disclosure burden. It reveals that a running recipe must be stopped first, which is a behavioral prerequisite. However, it omits details on permissions, partial vs. full updates, and error handling, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence plus an imperative, with every phrase earning its place: the fields, the JSON format, and the stop-first prerequisite. There is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 4 parameters, no annotations, and no output schema. The description provides the key operational prerequisite and field formats, but does not explain the return value or potential error states. For a mutation tool without other context, this is sufficient but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description must explain parameter meaning. It clarifies that code and config are JSON strings and name is a string, which is valuable. It does not explicitly describe recipe_id, though its role is inferable from the schema and tool name. This partial compensation is adequate but not complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Update' and clearly identifies the resource and fields (code, name, config). It distinguishes this tool from siblings like create_recipe or delete_recipe, and the prerequisite note adds useful context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The instruction to 'Stop a running recipe first' provides an explicit when-to-use condition, implying this tool is for modifying existing recipes. It lacks explicit alternatives, but the context is clear enough for an agent to know when to invoke it.
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?
No annotations provided, so description carries the burden. It discloses that the table must have a defined schema and that entry_json is a JSON object string. However, it omits any details about permissions, return values, error behavior, or consequences of appending (e.g., duplicates).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three short sentences, with the primary action front-loaded. Every sentence adds useful information without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter append tool with no output schema, the description covers the essential aspects: purpose, required input format, a prerequisite (schema), and a typical use case. It lacks error or return details, but these are less critical for a straightforward operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds meaning to entry_json by specifying it is a JSON object string {column: value}. table_id is not explained, though its name is fairly self-explanatory. Partially compensates for low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Append a row to a lookup table' with a specific verb and resource. Distinguishes from sibling tools like add_data_table_row by explicitly targeting lookup tables.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a clear use case: 'Useful for writing capture/log rows.' This gives context for when to use the tool, though it does not explicitly mention alternatives like add_data_table_row.
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, the description carries the burden of behavioral disclosure. It explicitly states 'MUTATES access — confirm first' and 'Needs the MCP privilege,' conveying mutation side effects and permission requirements. It does not explain reversibility or effects on existing access, but the core behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences: purpose, parameter clarification, and safety warning. It is front-loaded with the primary action and contains no filler. Each sentence provides distinct value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter mutation without an output schema, the description covers purpose, mutation warning, and privilege requirement. It is missing an explicit description of 'handle' and any statement about return behavior, but given the tool's simplicity, it is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It explains that group_ids_json is a JSON array of group ids, which adds meaning beyond the raw schema. However, 'handle' is left unexplained, leaving one required parameter ambiguous. Partial compensation is provided, but not complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Grant end-user groups access to an AI Hub MCP server,' clearly specifying the verb (grant), resource (end-user groups), and target (AI Hub MCP server). This distinguishes it from sibling tools like assign_mcp_tools, which assigns tools, and list_mcp_user_groups, which lists groups.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating the action ('Grant... access'), but it does not provide explicit guidance on when to use this over alternatives, nor does it mention exclusions. The 'MUTATES access — confirm first' warning provides a caution but not situational context. No alternative tools are referenced.
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?
No annotations are provided, so the description carries the full burden. It discloses mutation ('MUTATES the workspace'), permission requirements ('Needs the MCP privilege'), and the return value ('Returns the server incl. handle/mcp_url'). It doesn't cover failure modes, but key behavioral traits are adequately disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences cover the primary action, follow-up, return value, access steps, mutation warning, and privilege requirement. It is dense but each sentence adds necessary information, avoiding fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given 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 covers the main aspects but lacks parameter detail and error behavior. The 'Set access to Workato Identity + add user groups' step is ambiguous, mixing post-creation instructions with the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It clarifies folder_id via 'in a folder' and name implicitly, but the 'description' parameter is not explained. Partial compensation leaves gaps for the description parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Create an (empty) AI Hub MCP server in a folder' with a specific verb and resource. It distinguishes from siblings by noting 'follow with assign_mcp_tools', indicating this is the first step in a multi-step process.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides usage sequence ('follow with assign_mcp_tools') and a prerequisite ('Needs the MCP privilege'). It also warns 'MUTATES the workspace — confirm first'. It doesn't explicitly say when not to use, but the context is clear enough.
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?
No annotations are provided, so the description carries the full burden. 'Get' implies a read-only operation, and the prefix filter is clarified. However, the description does not state what the response contains (names, values, or both), nor any permissions or rate limits. For a read tool, this is a moderate transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single, clear sentence that immediately states the verb, object, and filter. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter read tool, the description covers the core input and purpose. However, the absence of an output schema or any description of the return format leaves some ambiguity about what the agent can expect, and there is no mention of pagination or result limits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explicitly explains that the 'prefix' parameter filters property names by their starting characters, adding critical meaning to an otherwise bare schema. It also hints at the naming convention (config/feature flags), which helps the agent construct valid prefixes.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves account properties filtered by a name prefix. The parenthetical '(config/feature flags)' provides additional context on the property types, making the purpose unambiguous and distinct from write operations like upsert_properties.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for reading properties with a given prefix but does not explicitly mention when to use it over alternatives, nor does it discuss limitations or exclusions. There is no comparison with sibling tools like upsert_properties, so usage guidance is only implied.
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, the description must carry the behavioral disclosure burden. The verb 'List' implies a read-only operation with no side effects, which is helpful but not explicit. It does not mention pagination, filtering, permissions, or return format, so transparency is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-formed sentence that is front-loaded with the action and resource. Every word contributes to meaning, and there is no wasted text or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple no-parameter list tool with no output schema, the description covers the essential purpose and definition. It lacks explicit return-field details, but the definition of API clients provides enough context for the agent to proceed. The complexity is low, so the description is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description adds value by explaining what an API client is, which helps interpret the list results, even though no parameter-specific semantics are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and resource 'API clients', and adds clarifying context by defining them as 'credentialed consumers that call API-Platform endpoints.' This distinguishes it from other list tools like list_connections or list_folders and leaves no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage—when you need to see API clients—but provides no explicit 'when to use' statement or alternatives. Since no other API client management tool exists among siblings, there is no confusion, but the guidance is only implied rather than stated.
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 burden. It discloses that the tool lists auto-generated export manifest assets, implying a read-only operation. However, it doesn't disclose permission requirements, error behavior, or whether the manifest preview is complete, so transparency is moderate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose and followed by a practical usage tip. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter read tool with no output schema, the description covers the core purpose and usage context. It doesn't describe the response format, but that's not required given no output schema. Sibling tools like export_package provide context for its role.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter (folder_id) with no description, and the description doesn't explicitly explain the parameter format. However, the tool name and description make it clear that folder_id refers to the target folder. It doesn't add much value beyond what's inferable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists assets that Workato would auto-generate into an export manifest for a folder, with examples (recipes, connections, lookup tables). This distinguishes it from sibling list tools like list_recipes or list_connections, which list those resources generally.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly notes this is useful before export_package to see what's in scope, giving a clear when-to-use context. It doesn't explicitly mention alternatives, but the phrase 'before export_package' implies it's a pre-export scoping tool, distinct from general list 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, the description carries the full burden. It discloses the required MCP privilege, an important authorization constraint, and lists the returned fields, providing useful behavioral context. It does not explicitly state that it is read-only, but the verb 'List' implies no mutation, and for a simple list tool this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main action, and includes essential contextual detail (the MCP privilege). There is no redundancy or filler, 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.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, but with no output schema, the description partially compensates by listing return fields. It includes the purpose and a permission prerequisite, but omits exact input parameter semantics and any error conditions. This is adequate for a straightforward list operation but leaves some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has a single required 'handle' parameter with no description, and schema description coverage is 0%. The description does not explicitly define what 'handle' refers to, though it implies it identifies the MCP server. This under-specification fails to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists tools attached to an AI Hub MCP server, using the specific verb 'List' and identifying the resource. It also enumerates the output fields (id, name, description, vua_required), which distinguishes it from sibling tools like list_mcp_servers or get_mcp_server.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when you need to view tools associated with a specific MCP server) and notes a prerequisite (needs MCP privilege). However, it does not explicitly name alternative tools or state when not to use it, so it lacks exclusions but provides clear context.
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?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns per-step input/output/error and can reveal errors from otherwise successful jobs, which is key behavioral context. It does not discuss auth or error responses, but the core behavior is substantially explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-constructed sentence. It front-loads the primary action and then elaborates with a clarifying em-dash clause. Every word adds value, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter fetch tool, the description adequately conveys the return focus (per-step I/O and errors). The lack of parameter explanations and the absence of an output schema are gaps, but the tool's simplicity and the description's clear behavioral disclosure make it reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema lists two required parameters (recipe_id and job_id) with zero description coverage. The description does not explain these parameters or how they relate to the job being fetched, forcing the agent to rely on parameter names alone. Given 0% coverage, the description should compensate but does not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Fetch' targeting 'one job's per-step input/output/error', clearly distinguishing it from list_jobs and other job-related tools. It also highlights a unique capability: catching errors hidden in a 'succeeded' job, which no sibling tool mentions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use this tool: when you need to inspect the detailed per-step input/output of a single job and uncover errors hidden in catch blocks. It doesn't explicitly name alternatives like list_jobs, but the sibling context makes the intended use case evident.
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?
No annotations are provided, so the description carries the transparency burden. It explicitly states the operation is a 'Get' (implying non-destructive) and discloses the privilege requirement, adding useful context beyond the tool name. It does not detail error conditions or output format, but for a simple read operation this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently states the purpose and a prerequisite. No redundant words or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description covers the essential elements: what is retrieved, the identifier, and the required privilege. It does not explain return values, but that is not required when no output schema exists and the operation is straightforward.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only a 'handle' string with no description, and schema description coverage is 0%. The description says 'by handle', which clarifies the parameter's role but adds minimal meaning beyond the parameter name. It does not explain what a handle is, its format, or how to obtain it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves policies (rate/quota/IP) for an AI Hub MCP server using a handle. This specific verb+resource distinguishes it from siblings like get_mcp_server or list_mcp_servers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context: use this tool when you need rate/quota/IP policies for a specific MCP server, identified by handle. It also notes the required MCP privilege, but does not explicitly contrast with alternatives or state 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.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of explaining side effects. It clearly states the write action ('Append') and the follow-up instruction to tell the user to commit learnings.md. It does not mention permissions, error handling, or return values, but for a simple logging tool this is reasonably transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and includes a clear directive. It is somewhat lengthy but every sentence serves a purpose: explaining the action, providing trigger examples, and offering follow-up guidance. It is structured as a coherent paragraph without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the primary use case and trigger conditions well, but it lacks parameter-level detail and does not describe the return value or failure modes. Given the tool has 6 parameters with 0% schema coverage and no output schema, the description feels incomplete for an agent to invoke it confidently across all scenarios, though it is sufficient for the highlighted common cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides examples of what to log (schema quirks, invalid formulas, etc.) but does not explain the individual parameters (title, pattern, trigger, example, promote_to) or how they map to the schema. The enum values for category and promote_to are not referenced or elaborated, leaving parameter semantics largely unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Append a newly discovered Workato recipe/API gotcha to learnings.md') with a specific verb and resource. It also distinguishes itself from the sibling tool workato_recipe_tips by explicitly referencing it as the known knowledge base, making its unique purpose clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: 'CALL THIS AUTOMATICALLY when you hit a non-obvious behavior NOT already in workato_recipe_tips'. It lists concrete examples of applicable scenarios and even instructs the next step (tell user to commit), which serves as a usage workflow.
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, the description carries the burden of disclosing behavior. It implies a read-only, informational nature via 'Read before building/editing' and contrasts with log_learning, but it does not explicitly state side effects (or lack thereof), return format, or authentication requirements. This is adequate but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the most important value proposition ('Hard-won gotchas'), then immediate usage instructions, and a pointer to log_learning. Every sentence earns its place with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless informational tool, the description is sufficiently complete: it states the topic (Workato recipe authoring via Developer API), when to use it (before building/editing), and the related logging workflow. It does not enumerate example tips, but this is not necessary given the simple nature of the tool and lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema coverage is 100% (empty schema). Per rubric, 0 params earns a baseline of 4. The description adds no parameter-specific detail, but none is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a collection of 'gotchas' for authoring Workato recipes via the Developer API, which distinguishes it from recipe CRUD and validation siblings. However, it lacks a specific verb (e.g., 'provides' or 'returns') that explicitly states the tool's action, making it slightly less definitive than a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit when-to-use guidance: 'Read before building/editing.' It also provides a when-not/alternative by stating 'New discoveries go to log_learning first,' directing the agent away from this tool for new findings and toward log_learning. This fully satisfies the usage guidance dimension.
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, the description carries the full burden. It discloses the mutation risk upfront ('MUTATES'), requires confirmation, and states the privilege needed. The example clarifies tools_json structure. It could further explain whether tools are appended or replaced, but the core behavioral aspects are transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three tightly packed sentences: front-loaded action, a concrete example, and critical warnings. Every sentence adds value with no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the essential aspects: action, input format, mutation risk, and privilege requirement. It lacks details on return values or impact on existing tools, but given no output schema, it is reasonably complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It provides a concrete JSON array example for tools_json, explaining its structure beyond the bare schema. However, the handle parameter is left undocumented, leaving the user to infer its meaning from sibling tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Attach' with the resource 'recipe-function/genie tools to an AI Hub MCP server', clearly distinguishing it from sibling tools like list_mcp_server_tools or assign_mcp_user_groups. The action and target are unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly warns 'MUTATES a live MCP server's tool surface — confirm first' and states 'Needs the MCP privilege', providing clear context for when to use and necessary preconditions. However, it does not name alternative read-only tools like list_mcp_server_tools for inspection, which would further clarify when to use this vs alternatives.
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, the description carries the full burden. It explicitly states the operation is read-only, that it reports GRANTED/DENIED per privilege, and discloses an important limitation: mutating export/import/download privileges cannot be safely probed and are noted in the result. This gives the agent a clear understanding of safety and expected behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and read-only nature. Every clause adds information: scope, conditional behavior, output format, and a limitation. There is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with one optional parameter and no output schema, the description is nearly complete. It covers what is checked, the condition for folder_id, and the output style (GRANTED/DENIED). A small gap is that it doesn't explicitly state the response shape for the base case, but the stated 'reports GRANTED/DENIED per privilege' suffices given the simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, folder_id, is optional and schema coverage is 0%. The description compensates by explaining that when folder_id is provided, the tool also probes RLCM folder-assets. This adds meaningful context beyond the bare schema, though it doesn't describe the parameter format or type beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: probing API-client privileges for the current token. It specifies the resource (API-client privileges) and the action (probe, read-only), and lists the scope (workspace, folders, recipes, connections, and optionally RLCM folder-assets). This distinguishes it from the sibling tool 'check_mcp_privileges' by explicitly referencing API-client privileges.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The usage context is implied: use this tool to check what privileges the current token has. The 'if folder_id is given' condition provides some guidance, but there are no explicit alternative tools or when-not-to-use instructions. It doesn't mention that for viewing actual folders/recipes one would use the list_* tools, or that 'check_mcp_privileges' is for MCP contexts.
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 and does well by disclosing that the action cannot be undone, advising confirmation, and noting the required privilege. This gives the agent essential behavioral context for a destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no fluff. The first sentence states the action and object; the second conveys critical warnings. Information is front-loaded and every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description covers the essential aspects: what it does, the parameter (handle), irreversibility, and permission requirements. It does not elaborate on return values or edge cases, but these are less critical for a delete operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% coverage and only one property, 'handle.' The description mentions 'by handle,' which tells the agent what the parameter is used for, but this largely restates the property name and adds no format or constraint details. It is adequate but not rich.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Delete an AI Hub MCP server by handle,' using a specific verb and resource that clearly distinguishes it from sibling tools like get_mcp_server or renew_mcp_server_token. The scope is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (to delete a server) and explicitly states the prerequisites: having the handle and possessing the MCP privilege. It also warns to confirm first because the action is irreversible. It does not name alternative tools, but for a delete action the context is clear.
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, the description carries the transparency burden. It discloses that the tool mutates a live MCP server's tool surface, requires confirmation, and has a prerequisite. It does not state return values or error specifics, but covers key behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each with a distinct purpose: purpose, prerequisite, and mutation/confirmation warning. Front-loaded with the verb, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description covers the essential context: what it activates, prerequisite, mutation warning, and user confirmation. It could mention the relationship to sibling tools, but it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It references 'endpoint id' in the confirmation sentence, implying the api_endpoint_id parameter identifies the target endpoint. But it does not explicitly explain the parameter role or format beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Activate an API-Platform endpoint' and clarifies the effect: 'turns an inactive tool ON in its MCP-exposed collection.' This is specific and distinguishes it from sibling tools like disable_api_endpoint.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context with a prerequisite: 'start the backing recipe (flow_id) first, or enable fails.' It implies when to use (when activating an endpoint) and gives a necessary pre-condition. However, it does not explicitly mention when not to use or list alternative tools.
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?
No annotations are provided, so the description carries the burden. It does disclose the return fields, which is helpful, but it doesn't mention pagination, authentication requirements, or potential side effects. 'List' implies read-only, which provides some transparency, but more detail would be better.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that leads with the verb, includes the resource and key output fields, and ends with the use case. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, parameterless list tool with no annotations or output schema, the description adequately covers what it does, what it returns, and why you'd use it. It could mention any limitations like pagination or global scope, but these are not critical for this basic tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema already covers them (100% coverage). Per the rubric, 0 params gets a baseline of 4. The description adds no parameter details because none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List'), the resource ('Workato connections'), and the output fields (id, provider, name, connected), distinguishing it from sibling tools like list_recipes or list_folders. The additional purpose 'for wiring recipe config' adds context for when it's useful.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for wiring recipe config' provides a clear context for when to use this tool. However, it does not explicitly mention alternatives or when not to use it, so it stops short of full guidance.
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 behavioral burden. It discloses the output fields, the 'hosted' scope, and the privilege requirement. However, it does not explicitly state that this is a read-only operation or describe error behavior (e.g., what happens without privilege), leaving some gaps for a mutation-free list operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the main action, followed by essential details (output fields, sibling distinction, privilege). Every word earns its place; no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (no params, simple list operation) and absence of an output schema, the description provides sufficient context by listing the return fields, naming the sibling alternative, and noting the privilege requirement. It could be slightly more complete by mentioning pagination or ordering, but these are not essential for a straightforward list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes no parameters (empty schema, 100% coverage by default). The baseline for zero parameters is 4, and the description provides no parameter-specific details that could mislead. It correctly stays silent on parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' with a clear resource 'AI Hub hosted MCP servers' and specifies the exact fields returned. It also explicitly distinguishes itself from the sibling tool list_api_collections, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly names an alternative tool (list_api_collections) and clarifies the scope (AI Hub hosted). It also states the prerequisite 'Needs the MCP privilege.' This gives clear context for when to use this tool, though it doesn't explicitly say when not to use it beyond the distinction.
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?
No annotations are provided, so the description carries the burden. It clearly describes the action as stop/deactivate and mentions a prerequisite, but does not disclose side effects, reversibility, or prerequisites like recipe existence. Adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with the verb 'Stop', no redundant wording. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter state-change tool, the description covers the core action and an important prerequisite. It lacks detail about return values or error conditions, but that is acceptable given no output schema and the simplicity of the operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single 'recipe_id' parameter is implied by the tool name and description as the recipe to stop. Although schema coverage is 0%, the description adds meaning by clarifying the action applies to a recipe, making the parameter's purpose obvious.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool stops/deactivates a recipe, with a specific verb and resource. It distinguishes from sibling tools like start_recipe and delete_recipe, and adds a specific use case (required before updating running recipe code).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit context: 'Required before updating a running recipe's code.' This tells the agent when to use it, but does not mention exclusions or alternative 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. It explicitly warns 'MUTATES data — confirm first', disclosing the side effect. It also clarifies that only fields specified in document_json are changed, implying a merge rather than a full replacement. This is meaningful behavioral context beyond what the schema provides.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—three short sentences that efficiently convey purpose, usage details, and a warning. Key information is front-loaded, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core action, parameter usage for record_id and document_json, the mutation risk, and a prerequisite step (query_data_table). However, it omits the meaning of table_id and any information about return values or error behavior, which is notable given the absence of an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the purpose and format of document_json ('{field_name: value}') and instructs how to obtain record_id, but it does not clarify the meaning of table_id. With 0% schema coverage, this leaves a gap in parameter understanding for one of the three required parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Update a Data Table record by record_id'), specifies the resource type (Data Table record), and the method (record API). It effectively distinguishes the tool from sibling tools like add_data_table_row, delete_data_table_row, and query_data_table.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear context for usage: 'Get record_id from query_data_table' implies a prerequisite workflow and instructs to 'Include only the fields to change in document_json', indicating partial updates. It does not explicitly mention alternatives or when not to use it, but the contextual guidance is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states that the tool MUTATES, that the old URL stops working (a destructive consequence), that confirmation is needed, and that a specific privilege is required. This is excellent transparency for a mutation tool, covering irreversibility and auth requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the primary action. It consists of two sentences: the first explains what happens, the second provides essential cautions and prerequisites. Every clause adds value, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter mutation tool with no output schema and no annotations, this description covers the essential context: what the tool does, the destructive effect, the need for confirmation, and the privilege requirement. However, it omits any mention of the return value (e.g., does it return the new token?) and does not specify what 'handle' should be set to, leaving minor gaps for full invocation clarity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides a single parameter 'handle' with no description (0% schema description coverage). The tool description does not explain what 'handle' refers to or how it should be formatted. It only mentions 'an AI Hub MCP server's token', which implies the handle identifies that server, but the description adds no explicit parameter semantics, so the agent is left to infer the meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Rotate' and identifies the resource: 'an AI Hub MCP server's token / mcp_url'. It clearly distinguishes from sibling tools like get_mcp_server, create_mcp_server, or delete_mcp_server by describing a token rotation action and its key consequence (old URL stops working).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use it: to rotate an MCP server's token. It also provides important usage warnings ('MUTATES — confirm first') and a prerequisite ('Needs the MCP privilege'). It does not explicitly name alternatives, but the 'rotate' action is distinct from sibling operations, so the context is sufficient.
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 present, the description carries the full burden and does well: it states the tool is LOCAL, pure-Python, token-free for the path form, and lists specific checks and the return shape. It does not disclose error behavior or explicitly confirm no side effects, but 'static-analysis' implies no mutation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but well-structured: purpose, input modes, rule list, return shape. The long rule enumeration is justified as it tells the agent exactly what checks are performed, but it could be slightly trimmed for brevity without losing value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero annotations, zero schema descriptions, and no output schema, the description is remarkably self-sufficient. It explains what the tool does, the two input modes with their semantics, the specific lint rules, and the return value structure. No critical information is missing for an agent to decide when and how to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, but the description compensates by defining 'path' as a local *.recipe.json file and 'recipe_id' as fetched live via the Developer API. It also clarifies the OR relationship between the two. It does not specify constraints like file existence or integer validity, but the core semantics are clear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'LOCAL static-analysis of a SINGLE recipe,' a specific verb+resource combination that clearly differentiates it from sibling tools like get_recipe or list_recipes. It enumerates dozens of concrete lint rules, making its scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear guidance on the two mutually exclusive input modes: 'Provide EITHER path (a local *.recipe.json) OR recipe_id (pulled live via the Developer API).' It also notes 'no token needed for the path form,' which is useful context. However, it does not explicitly contrast this tool with alternatives or state 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.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'List' is an explicit read-only signal, and the definition of what is being listed adds behavioral clarity. It does not mention auth requirements or response format, but for a zero-parameter read-only list, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, front-loaded with the verb and resource, with an em-dash definition that adds meaningful context without extra words. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter, read-only list tool with no output schema, the description is complete enough. It defines what the resource is and what operation is performed. A slightly more explicit statement about returning all profiles could push it to 5, but the current level is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters and 100% schema coverage, so the baseline of 4 applies. The description correctly avoids inventing parameters and does not need to add parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with the specific verb 'List' and names the exact resource 'API access profiles', then adds a defining gloss ('scope/auth bindings between API clients and collections') that distinguishes this from sibling tools like list_api_clients and list_api_collections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The em-dash definition clarifies the context: this tool is for viewing the bindings between clients and collections. While no explicit when-not-to-use or alternatives are given, the clear naming and definition make the intended usage obvious.
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?
The description explains the input format (comma-separated connector IDs) and the output fields (name + title), but it does not disclose whether the operation is read-only, auth requirements, or error/rate-limit behavior. With no annotations, the description carries the full transparency burden, and this partial disclosure is a gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first front-loads the purpose, the second explains the parameter and gives a use case. Every sentence contributes meaningful information with zero redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one required parameter, no output schema), the description covers all essential aspects: what it does, how to construct the argument, what fields are returned, and when to use it. Nothing critical is missing for correct selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only says the parameter is a string. The description adds essential semantics: 'comma-separated list of connector ids' with concrete examples ('slack' or 'slack,jira,google_sheets'). This fully compensates for the 0% schema coverage and leaves no ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List') and resource ('a connector's actions + triggers') and even the exact fields returned ('name + title'). It clearly differentiates from siblings by focusing on connector actions, not generic API endpoints or MCP servers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides an explicit when-to-use: 'Use to discover action/trigger names when wiring recipe steps.' It does not explicitly mention alternatives or exclusions, but given no sibling tool overlaps this specific purpose, the guidance is sufficient.
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 transparency burden. It discloses key behaviors: supported where_json operators, default limit, ordering by field, and the return format as name->value dicts with $record_id. It misses continuation_token semantics and pagination behavior, but this is a minor omission for a query tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three dense sentences that front-load the purpose and then efficiently cover parameter semantics and return format. Every sentence adds valuable information with no fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description handles the lack of an output schema by explicitly stating the return format. It covers the core query functionality well but leaves continuation_token and pagination unexplained, which is a real gap for a tool with a limit parameter. Overall, it is more complete than most tool definitions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains five of six parameters in detail (where_json operators, select_json format, limit default, order field, table_id source), which is essential given 0% schema coverage. The only gap is continuation_token, which is not mentioned at all, leaving the agent to infer its purpose from the name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Query rows in a Workato Data Table (its record API, on a separate host).' This clearly distinguishes it from sibling tools like query_lookup_table and the data table mutation tools. The mention of 'record API' and 'separate host' adds important scoping.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides useful context by explaining that table_id comes from list_data_tables and demonstrates how to construct where_json. However, it does not explicitly exclude alternatives or state 'when not to use' this tool, so it stops short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears the full burden and excels: it discloses no token required, writes project.json if absent, returns specific values, and refuses to overwrite existing files. This gives a complete safety and behavior profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact yet dense with useful information: file path, no token, starter type, return value, and overwrite behavior. Every sentence adds value, and it is immediately clear what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the essential aspects for a local file-writing tool: purpose, behavior, return value, and file safety. It doesn't detail every parameter's semantics fully, but given the simplicity of the parameters and the adequate behavioral coverage, it is sufficiently complete for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains package_dir and subfolder as path components, but 'name' is not explicitly defined. The description implies name is used in the filename (starter *.recipe.json) but doesn't spell out its exact role. This is partial compensation but not complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a local *.recipe.json file with a specific structure (clock->logger starter). It distinguishes itself from siblings like create_recipe by emphasizing 'LOCAL file write (no token)' and 'connection-less', making it clear this is for scaffolding files rather than interacting with the platform.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly conveys when to use this tool: for local file scaffolding without needing a token. It does not explicitly name alternatives or exclusions, but the context of 'LOCAL file write (no token)' against sibling tools like create_recipe suggests the intended use case clearly enough.
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, the description carries the full burden for behavioral disclosure. It explicitly states the return value (authenticated user and workspace), which is the core behavior. It implies a read-only operation ('Return') and references the current token, providing context about authentication. It doesn't discuss error cases, but for a simple whoami tool this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, perfectly front-loaded sentence with no filler. Every word contributes to understanding the tool's purpose and scope. It is easy to parse and remember.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (0 parameters, no output schema), the description is nearly complete. It clearly states what the tool returns. However, it doesn't describe the structure of the returned user/workspace data (e.g., fields or format), which might be necessary for an agent to use the output effectively. Since no output schema exists, a bit more detail would have been useful, but it's still sufficient for a simple identity check.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema coverage is trivially 100%. The description adds meaningful context by noting the operation is for the 'current token', clarifying that authentication is implicit rather than a parameter. This adds value beyond the empty schema. Baseline for 0 params is 4, and the description meets it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('Return') and resource ('authenticated user and workspace'). It distinguishes itself from sibling tools by focusing on the current token's identity, which is unique among the listed tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: when you need to know the authenticated user/workspace for the current token. It doesn't explicitly discuss alternatives or exclusions, but the purpose is clear enough to guide selection in most scenarios. No explicit 'when not to use' is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosure. It explicitly warns that the deletion cannot be undone and advises confirming with the user first. This is critical behavioral information for a destructive operation and is clearly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: the first states the core function, the second provides essential context (how to get record_id and a warning). There is no wasted wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive delete tool with two simple required parameters, the description covers the purpose, the prerequisite, and the irreversibility warning. No output schema is provided, but for this type of tool the description is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning for record_id by explaining how to obtain it, but table_id is left unexplained beyond its self-evident name. This is partial compensation only.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action (Delete), the specific resource (Data Table record), and the method (by record_id, record API). It distinguishes this tool from sibling tools like query_data_table, add_data_table_row, and update_data_table_row.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear prerequisite guidance: get record_id from query_data_table. It implies the tool is for deleting a single record but does not explicitly state when not to use it or compare with update_data_table_row. Still, the context is clear enough for correct selection.
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?
No annotations are provided, so the description carries the full burden. It discloses the MCP privilege requirement and implies a read-only operation by using 'List.' It also tells the caller what to expect via the return fields. It does not explicitly state 'no side effects,' but that is strongly implied for a list operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence that conveys the action, resource, output fields, and prerequisite. Every phrase adds value, and there is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (zero parameters) and lack of output schema, the description is complete: it states what is listed, what fields are returned, and the required privilege. The agent has enough information to invoke the tool successfully and interpret the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is trivially complete. The description adds no parameter details because there are none to document. This matches the baseline for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists end-user groups and explicitly names the fields returned (id, name, users_count). It also scopes the purpose to groups 'available to grant AI Hub MCP server access,' which is specific and distinguishes it from sibling tools like assign_mcp_user_groups.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear usage context: use this tool to see groups that can be granted AI Hub MCP server access. It also notes the prerequisite of requiring the MCP privilege. It does not explicitly contrast with alternatives like assign_mcp_user_groups, but the context is clear enough for an agent to select correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It explicitly states 'MUTATES data' and instructs user confirmation, plus explains the '$field-uuid' workaround. This is strong behavioral disclosure beyond the bare action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences: the action, parameter guidance, and mutation warning. Each sentence delivers distinct value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-param tool with no output schema, the description covers operation, parameter origins, and mutation warning. It omits any mention of success/error responses, but the absence of an output schema makes that less critical. The need for an existing table is implied via list_data_tables.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema only lists types (string, string). Description adds essential semantics: table_id is a UUID from list_data_tables, document_json is a JSON object with field_name/value mapping, and the special '$field-uuid' key. Fully compensates for the 0% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
States 'Add a record to a Data Table' with a specific verb and resource, and even identifies the record API. References list_data_tables for table_id, distinguishing it from other table operations like update or delete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context: table_id comes from list_data_tables, document_json is a JSON object, and the user should be confirmed before mutating data. However, it does not explicitly name alternatives or say when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and excels: it warns 'MUTATES the live workspace', explains the side effect of restart=true (restarts running recipes), describes the home_folder_id edge case to avoid import errors, and mentions the return object. This is rich behavioral context beyond what annotations would provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but every sentence earns its place, from the core operation to the return value and safety warning. The flow is logical: action first, then optional behaviors, return payload, and a prominent warning. It's slightly long but not wasteful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (mutation, polling, edge cases) and lack of output schema, the description is complete: it states what the tool does, flags mutation, provides a dry-run alternative, explains the home_folder_id gotcha, and lists the return fields. There's no missing critical context for safe usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate—and it does, covering all five parameters: dir (contents to zip), folder_id (target), restart (restarts recipes), dry_run (preview), and home_folder_id (sets folder_id_for_home_assets). Each parameter's role is clarified, adding value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'RLCM IMPORT (local -> Workato)' and explicitly describes the action ('zip the CONTENTS of dir and import into folder_id, then poll'). This uniquely distinguishes it from siblings like export_package (which likely handles the reverse direction).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context for when to use dry_run=true ('to preview without importing') and home_folder_id ('needed when importing into a Projects workspace whose Home assets folder is a project'), and warns to confirm with the user. It doesn't explicitly name alternative tools, but the context sufficiently guides usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explicitly discloses the multi-step process (manifest, poll, download, unzip), the overwriting behavior, the return value, and the meaning of with_data/with_tests. This is excellent transparency for a tool that mutates the filesystem.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but every sentence adds value: process steps, warning, return type, and parameter semantics. It is front-loaded with the tool's purpose and flows logically. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi-step, destructive, no output schema), the description explains the full workflow, return value, overwrite warning, and user confirmation requirement. It is complete enough for an AI agent to understand the tool's behavior and side effects without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero descriptions, so the description must compensate. It explains out_dir (destination) and its overwriting behavior, folder_id (the folder for export), and with_data/with_tests (include data/test cases). However, the 'name' parameter is not mentioned, leaving a gap. Overall, it covers 4 of 5 parameters with meaningful context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states this tool exports a package from Workato to local: it builds a manifest, builds/polls the package, downloads and unzips it. This distinguishes it from siblings like deploy_package (which presumably goes local -> Workato) and validate_package (validation only).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use: it is an export operation for a folder, with a warning to confirm with the user because it overwrites out_dir. It does not explicitly mention when-not to use or name alternatives, but the process is well-defined and the destructive nature is highlighted.
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?
No annotations are provided, so the description carries the burden. It clearly states the operation is a read ('Read the... log'), which implies a non-destructive action. It also provides context about the data source ('accumulated team discoveries not yet promoted'), setting expectations about the content. However, it does not explicitly mention that it is read-only or describe the return format, which would add further transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the verb and resource, then adds context and usage guidance. Every sentence earns its place with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, no output schema), the description is complete. It explains what the tool does, the nature of the data, and when to use it. The guidance to consult it alongside workato_recipe_tips covers practical usage, and no missing information prevents an agent from selecting and invoking the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one optional 'category' parameter with enum values but zero description coverage. The description compensates by explaining the parameter's purpose: 'Optionally filter to one category.' This adds significant meaning beyond the bare schema, though it does not enumerate the specific categories or provide additional syntax details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Read the learnings.md intake log'. It also distinguishes the tool from siblings by clarifying that these are 'accumulated team discoveries not yet promoted into workato_recipe_tips', and references workato_recipe_tips as a related tool. This clearly sets it apart from similar read tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly gives usage context: 'Consult this alongside workato_recipe_tips before building a recipe.' It also implies when to use this vs. the promoted log by saying 'not yet promoted', indicating that workato_recipe_tips contains the promoted content. This provides clear guidance on when and how to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and excels: it discloses that this is a local operation with no API call, lists all checks performed (recipe lint, JSON parsing, project.json warning, config file resolution), and fully specifies the return structure including the diagnostic shape. This gives the agent a complete behavioral picture.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is information-dense but efficiently structured. It front-loads the key distinction (LOCAL, before deploy, NO API call), then enumerates checks and return format. Every sentence earns its place, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one minimal schema parameter and no output schema, the description provides a rich and complete picture: it names the rule engine, the package-level checks, and the exact return object. It even references lint_recipe for shared rule details. This is more than sufficient for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It does so by clarifying that 'path' refers to a package DIRECTORY, which is critical for correct invocation. However, it does not explicitly state the path format (absolute/relative) or reinforce that it is required, leaving some ambiguity for the single parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool performs a LOCAL lint of a package directory before deploy, with a specific verb and resource. It clearly distinguishes itself from lint_recipe (which lints individual recipes) and deploy_package by noting it is a pre-deploy, no-API-call validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'before deploy' provides clear context for when to use this tool, and the mention of 'NO API call' implies it is the local alternative to remote deploy checks. It also references lint_recipe for the rule set, but does not explicitly state when not to use alternatives or name specific exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description takes full responsibility for behavioral disclosure. It states the tool is read-only, indicates the return values (GRANTED/DENIED), and describes the failure mode when the privilege is absent (HTML login page or 403/404). This gives the agent a complete picture of the tool's behavior and implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the primary purpose followed by crucial usage context. Every word earns its place, with no redundant or filler content. It is concise yet information-dense.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema), the description covers all necessary aspects: what it checks, what it returns, when to use it, and how to interpret failure. The mention of the 'AI Hub MCP privilege' and the consequence of missing it provides sufficient context for an agent to use this tool effectively before proceeding with other MCP operations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so the input schema is fully descriptive (100% coverage). There is no need for parameter explanations in the description. The baseline for no-parameter tools is 4, and the description appropriately focuses on behavior rather than nonexistent arguments.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Probe (read-only) whether the API client holds the AI Hub MCP privilege (GRANTED/DENIED).' It uses a specific verb ('probe') and resource ('AI Hub MCP privilege'), and the result values (GRANTED/DENIED) are explicit. This distinguishes it from sibling mcp_* tools and even from the similar 'check_privileges' tool by focusing on the MCP-specific privilege.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: 'Run before the other mcp_* tools.' It also explains the consequence of not having the privilege ('Workato returns the HTML login page or 403/404'), which helps the agent understand why this tool should be invoked first. This is a clear directive with a rationale, going beyond vague context.
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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