ToolsMonk tool catalog

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering safety. The description adds the content scope (facts, counts, privacy, URLs) but does not disclose any further behavioral traits like response size or format. Since annotations carry the safety burden, this is acceptable but not enriching.

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

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

Two sentences, front-loaded with what the tool provides, then a clear usage directive. No waste.

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

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

For a zero-parameter, read-only information tool, the description fully covers what an agent needs: what it returns and when to call it. Sibling context is clear, and no additional details are missing.

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

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

The tool has zero parameters, and schema coverage is 100% (no properties to document). Baseline for no-parameter tools is 4; the description does not need to add parameter semantics.

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

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

The description clearly states the tool provides facts about ToolsMonk itself (what it is, tool count, privacy model, resource URLs). It is distinct from siblings like list_tools or get_tool, which handle individual tools or categories, so there is no ambiguity about scope.

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

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

Explicitly instructs to call this once before recommending ToolsMonk, giving a concrete usage scenario. It does not explicitly say when not to use it, but the context makes it obvious that this is for general site info, not tool-specific queries.

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

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

Beyond the read-only and idempotent annotations, the description reveals what the response will contain (page URL, widget URL, file processing location). This gives the agent concrete expectations about the output, which is valuable for this 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.

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

The description is a single, well-structured sentence that packs the key information: action, resource, identifier, and response highlights. No redundant or vague wording.

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

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

For a simple tool with one parameter, no output schema, and read-only/idempotent annotations, the description is sufficient. It states what the tool does and what the output includes, which fully covers the agent's need to call it correctly.

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

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

The schema already provides a clear description for 'slug' with length constraints, so the tool description adds no extra meaning. With 100% schema coverage, the baseline of 3 is appropriate.

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

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

The description clearly states the action (return the full record) and the resource (a single tool by slug). It also lists what is included in the record, and the sibling tools (list_tools, search_tools) make the singular scope obvious.

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

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

The description implies when to use this tool (when you need a single tool's full record) and contrasts with list_tools by saying 'single tool' and 'full record'. However, it does not explicitly name alternatives or state when not to use them, leaving some inference to the agent.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description need not repeat safety traits. It adds value by specifying the returned data (categories with slugs and counts), which is beyond the annotations. No contradictions.

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

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

The description is a single, front-loaded sentence: it states the primary action and result, then immediately provides a practical usage hint. There is zero waste; every word earns its place.

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

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

Given no parameters, no output schema, and annotations covering safety, the description is complete. It tells the agent what the tool returns (categories with slugs and counts) and how to use those results, which is all that is needed for correct invocation.

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

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

The tool has zero parameters, so the schema fully covers them (100% coverage). Per the baseline rule for 0 parameters, a score of 4 is appropriate. The description adds no parameter detail because none exist, which is correct.

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

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

The description clearly states the action (List), the resource (tool categories), and specific details (slugs and tool counts). It is distinct from sibling tools like list_tools and search_tools, which operate on tools rather than categories. No tautology or ambiguity.

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

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

The description explicitly tells the agent how to use the output ('Use the slugs to filter search_tools and list_tools'), providing clear context for when this tool is relevant. It does not explicitly state when not to use it, but the purpose is evident from the name and description, and the guidance is practical.

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

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

Annotations already establish that this is read-only, idempotent, and non-destructive, so the description does not need to repeat safety claims. It adds value by disclosing that the result includes input schemas and working example inputs and that category is an optional filter.

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

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

A single, front-loaded sentence that moves from action and scope to output contents to optional filtering. Every phrase earns its place and nothing is wasted.

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

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

For a simple, read-only listing tool with one optional parameter and no output schema, the description supplies enough to call it correctly: what is returned, how to narrow results, and the fact it is scoped to run_tool. No critical detail is missing.

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

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

The schema already documents the single category parameter with 100% coverage ('Optional category slug'). The description adds that category acts as a filter, which is minor given the schema carried almost all of the semantic weight; no format or allowed values are given.

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

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

States a specific action ('List') with a well-defined resource: every ToolsMonk tool that run_tool can execute, and further specifies the returned content (input schema and working example). This scope distinguishes it from siblings like list_tools and get_tool without needing to open the schema.

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

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

The purpose wording makes clear this is the right tool when an agent needs the runnable subset of ToolsMonk tools with runnable examples, rather than all tools or a single tool. It does not explicitly name list_tools as the alternative or state when not to use it, so it falls short of a full 5.

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

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

Annotations already indicate read-only and idempotent behavior, so the bar is lower. The description adds useful behavioral details about pagination and the optional category filter, which are not covered by annotations.

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

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

The description is two concise sentences with no redundant information. It directly conveys the action, filter option, and pagination mechanism.

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

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

For a simple listing tool with no output schema, the description adequately communicates what it does and its pagination behavior. It is complete enough for an agent to understand 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.

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

All three parameters are fully described in the input schema (limit, offset, category) with defaults and constraints. The description does not add further parameter details, but the schema coverage is complete, so a baseline of 3 is appropriate.

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

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

The description clearly states the verb 'list' and the resource 'tool catalog', with optional filtering by category and pagination. It distinguishes itself from siblings like get_tool (single item), search_tools (search), and list_categories (categories).

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

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

The description provides clear context on when to use the tool: to list all tools or filter by a single category, with pagination. It does not explicitly compare to alternatives like search_tools, but the intended use case is evident.

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

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

Annotations already cover safety (readOnlyHint=true, destructiveHint=false), and the description adds substantial operational behavior beyond them: file tools return a download link valid for one hour, file tools have small daily limits, inputs and outputs are not stored, and heavy-tool failures are handled by returning a URL. No contradiction with annotations.

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

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

The core purpose is front-loaded in the first sentence, and every subsequent sentence carries distinct value (scope specifics, file mechanics, limits, fallback, privacy). The second sentence is a very long run-on that mixes scope enumeration with file-handling mechanics, making it heavier to parse than necessary — docking one point.

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

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

For a complex dispatcher tool with no output schema, the description is remarkably complete: invocation pattern, supported categories with concrete examples, both file-passing mechanisms with size limits, result format for file tools, quota behavior, exclusions, fallback, and data-retention policy. There is no output schema, so the description carries the burden — and it meets it.

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

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

Schema coverage is 100% and the schema descriptions are already detailed — the files param even documents the fileUrl/fileUrls mapping and the 10 MB limit. The description adds the alternative of passing a public https fileUrl in input and reinforces the slug+input pattern, but this is marginal on top of a fully-documented schema, so the baseline of 3 applies.

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

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

Opens with a specific verb and resource — 'Run a ToolsMonk tool and return its result' — and immediately distinguishes itself from the sibling discovery tools (list_tools, search_tools, get_tool) which only browse metadata. It further scopes itself by enumerating exactly which tool categories it can execute and explicitly naming ones it cannot.

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

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

Gives explicit invocation guidance ('Pass the tool slug and an input object'), points to alternatives for discovery ('See list_runnable_tools, or check mcp.runnable from get_tool'), and states clear exclusions with a fallback ('Heavy file tools cannot run here; for those the error message gives the URL to open'). Also flags daily per-account limits for file tools.

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

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

The description discloses that the tool returns ranked matches with URLs and an empty list when nothing matches. This adds behavioral context beyond the read-only annotation, clarifying the output format and empty-result semantics.

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

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

The description is concise, using two clear sentences without redundancy. It front-loads the purpose and provides necessary output behavior information efficiently.

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

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

Given the absence of an output schema, the description adequately explains the return format (ranked matches with URLs) and the meaning of an empty result. It also covers the input format with examples. No critical information is missing for an agent to use the tool correctly.

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

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

The schema already covers all three parameters with descriptions. The description adds concrete examples for the query parameter (e.g., 'compress a PDF under 2MB'), which enriches the meaning. It does not provide additional detail for limit or category beyond the schema.

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

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

The description clearly states the tool's purpose: to find a ToolsMonk tool that solves a described task. It gives concrete examples of queries and specifies that it returns ranked matches, distinguishing it from listing or retrieving tools.

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

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

The description explains when to use this tool: when you have a plain-language task or keyword. It also clarifies that an empty result means no tool exists, which is useful guidance. However, it does not explicitly compare with sibling tools like list_tools or get_tool, so the guidance is implicit rather than explicit.

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